From 4d4cd62bc3c53809519330fb136bac19fda25473 Mon Sep 17 00:00:00 2001 From: justin Date: Fri, 10 May 2024 07:30:07 -0500 Subject: [PATCH 01/40] reorg --- .github/workflows/verify.yml | 10 - .skiff/cloudbuild-deploy.yaml | 88 - .skiff/util.libsonnet | 103 - .skiff/webapp.jsonnet | 455 --- app/.dockerignore | 8 - app/.gitignore | 58 - app/.streamlit/config.toml | 13 - app/Dockerfile | 24 - app/app.py | 232 -- app/requirements.txt | 10 - .../{resources.jsonl => resources.json} | 0 app/src/__init__.py | 0 app/src/api/__init__.py | 0 app/src/api/api.py | 78 - app/src/components/__init__.py | 0 app/src/components/goat_counter.py | 35 - app/src/constants.py | 252 -- app/src/theme/__init__.py | 0 app/src/theme/style.css | 117 - app/src/theme/theme.py | 52 - app/src/utils.py | 86 - bin/verify | 167 - docker-compose.yaml | 46 - it/.dockerignore | 3 - it/.eslintignore | 4 - it/.eslintrc.js | 3 - it/.gitignore | 2 - it/Dockerfile | 16 - it/fixture/robots.txt | 9 - it/is_ready.js | 45 - it/package.json | 44 - it/src/conf.ts | 39 - it/src/site.test.ts | 59 - it/tsconfig.json | 19 - it/yarn.lock | 2747 ----------------- skiff.json | 7 - sonar/Dockerfile | 14 - sonar/ping.py | 67 - sonar/requirements.txt | 1 - 39 files changed, 4913 deletions(-) delete mode 100644 .github/workflows/verify.yml delete mode 100644 .skiff/cloudbuild-deploy.yaml delete mode 100644 .skiff/util.libsonnet delete mode 100644 .skiff/webapp.jsonnet delete mode 100644 app/.dockerignore delete mode 100644 app/.gitignore delete mode 100644 app/.streamlit/config.toml delete mode 100644 app/Dockerfile delete mode 100644 app/app.py delete mode 100644 app/requirements.txt rename app/resources/{resources.jsonl => resources.json} (100%) delete mode 100644 app/src/__init__.py delete mode 100644 app/src/api/__init__.py delete mode 100644 app/src/api/api.py delete mode 100644 app/src/components/__init__.py delete mode 100644 app/src/components/goat_counter.py delete mode 100644 app/src/constants.py delete mode 100644 app/src/theme/__init__.py delete mode 100644 app/src/theme/style.css delete mode 100644 app/src/theme/theme.py delete mode 100644 app/src/utils.py delete mode 100755 bin/verify delete mode 100644 docker-compose.yaml delete mode 100644 it/.dockerignore delete mode 100644 it/.eslintignore delete mode 100644 it/.eslintrc.js delete mode 100644 it/.gitignore delete mode 100644 it/Dockerfile delete mode 100644 it/fixture/robots.txt delete mode 100644 it/is_ready.js delete mode 100644 it/package.json delete mode 100644 it/src/conf.ts delete mode 100644 it/src/site.test.ts delete mode 100644 it/tsconfig.json delete mode 100644 it/yarn.lock delete mode 100644 skiff.json delete mode 100644 sonar/Dockerfile delete mode 100644 sonar/ping.py delete mode 100644 sonar/requirements.txt diff --git a/.github/workflows/verify.yml b/.github/workflows/verify.yml deleted file mode 100644 index 27290b3..0000000 --- a/.github/workflows/verify.yml +++ /dev/null @@ -1,10 +0,0 @@ -# This tells GitHub to run our integration tests everytime we push changes. -name: verify -on: push -jobs: - it: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - name: verify - run: ./bin/verify diff --git a/.skiff/cloudbuild-deploy.yaml b/.skiff/cloudbuild-deploy.yaml deleted file mode 100644 index 7902500..0000000 --- a/.skiff/cloudbuild-deploy.yaml +++ /dev/null @@ -1,88 +0,0 @@ -# This file tells Google Cloud Build how to deploy the application. -# It can be attached to a variety of triggers, the default being whenever -# someone merges changes to the `main` branch. -steps: -- id: 'app.build' - name: 'gcr.io/cloud-builders/docker' - args: [ - 'build', - '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-app:latest', - '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-app:$COMMIT_SHA', - '--cache-from', 'gcr.io/$PROJECT_ID/$REPO_NAME-app:latest', - '--build-arg', 'BUILDKIT_INLINE_CACHE=1', - '.' - ] - waitFor: [ '-' ] - dir: 'app' -- id: 'app.push' - name: 'gcr.io/cloud-builders/docker' - args: [ - 'push', - 'gcr.io/$PROJECT_ID/$REPO_NAME-app:$COMMIT_SHA', - ] - waitFor: [ 'app.build' ] -- id: 'proxy.build' - name: 'gcr.io/cloud-builders/docker' - args: [ - 'build', - '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:latest', - '-t', 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA', - '--cache-from', 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:latest', - '--build-arg', 'CONF_FILE=prod.conf', - '--build-arg', 'BUILDKIT_INLINE_CACHE=1', - '.' - ] - waitFor: [ '-' ] - dir: 'proxy' -- id: 'proxy.push' - name: 'gcr.io/cloud-builders/docker' - args: [ - 'push', - 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA' - ] - waitFor: [ 'proxy.build' ] -# Generate our Kubernetes configuration -- id: 'config' - name: 'gcr.io/ai2-reviz/jsonnet' - args: [ - 'eval', - '-y', - '--output-file', './webapp.yaml', - '--tla-str', 'env=$_ENV', - '--tla-str', 'appImage=gcr.io/$PROJECT_ID/$REPO_NAME-app:$COMMIT_SHA', - '--tla-str', 'proxyImage=gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA', - '--tla-str', 'sha=$COMMIT_SHA', - '--tla-str', 'cause="Automated Skiff Deploy SHA:$COMMIT_SHA BUILD:$BUILD_ID"', - '--tla-str', 'branch=$BRANCH_NAME', - '--tla-str', 'repo=$REPO_NAME', - '--tla-str', 'buildId=$BUILD_ID', - './webapp.jsonnet' - ] - dir: '.skiff' - waitFor: [ '-' ] -# Deploy the image to Kubernetes -- id: 'deploy' - name: 'gcr.io/ai2-reviz/rudder' - args: [ - 'deploy', - '-f', - 'webapp.yaml' - ] - dir: '.skiff' - waitFor: [ 'app.push', 'proxy.push', 'config' ] -substitutions: - _ENV: prod -images: [ - 'gcr.io/$PROJECT_ID/$REPO_NAME-app:$COMMIT_SHA', - 'gcr.io/$PROJECT_ID/$REPO_NAME-app:latest', - 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:$COMMIT_SHA', - 'gcr.io/$PROJECT_ID/$REPO_NAME-proxy:latest' -] -artifacts: - objects: - location: 'gs://skiff-archive/$REPO_NAME/$_ENV/$BUILD_ID/$COMMIT_SHA' - paths: ['.skiff/webapp.yaml'] -options: - env: - - "DOCKER_BUILDKIT=1" -timeout: 20m diff --git a/.skiff/util.libsonnet b/.skiff/util.libsonnet deleted file mode 100644 index de2000d..0000000 --- a/.skiff/util.libsonnet +++ /dev/null @@ -1,103 +0,0 @@ -/** - * This file contains a few helper methods that are used in webapp.jsonnet. - * They're put here as to not distract the reader from the stuff that really matters -- - * that is the code that produces their application's configuration. - */ -{ - local util = self, - - /** - * We're pinned to jsonnet 0.12.0, std.any was added after that version. - * So we implement our own. - */ - any(list): - std.length(std.filter(function(x) x, list)) > 0, - - isCustomHost(host): - if std.endsWith(host, '.allen.ai') then - if std.length(std.split(host, '.')) != 3 then - true - else - false - else if std.endsWith(host, '.apps.allenai.org') then - if std.length(std.split(host, '.')) != 4 then - true - else - false - else - true, - - /** - * Groups by the provided TLDs. Returns a tuple. The first value is a map of hosts - * by TLD. The second a list of hosts that didn't match a TLD. - */ - groupHosts(hosts, tlds): - local byTLD = { [tld]: std.filter(function(host) std.endsWith(host, tld), hosts) for tld in tlds }; - local rest = std.filter(function(host) !self.any([ std.endsWith(host, tld) for tld in tlds ]), hosts); - [ byTLD, rest ], - - hasCustomHost(hosts): - std.length(std.filter(util.isCustomHost, hosts)) > 0, - - /** - * Returns a list of hostnames, given the provided environment identifier, Skiff config - * and top level domain. - */ - getHosts(env, config, tld): - if env == 'prod' then - [ config.appName + tld ] - else - [ config.appName + '-' + env + tld ], - - /** - * Returns a few TLS related constructs given the provided hosts. If the application is - * only using direct subdomains of `.apps.allenai.org` then an empty configuration is provided, - * as the wildcard certificate that's managed by Skiff Bermuda can be used instead. - */ - getTLSConfig(fqn, hosts): { - local needsTLSCert = util.hasCustomHost(hosts), - - ingressAnnotations: - if needsTLSCert then - { 'cert-manager.io/cluster-issuer': 'letsencrypt-prod' } - else {}, - spec: - if needsTLSCert then - { secretName: fqn + '-tls' } - else - {}, - }, - - /** - * Returns the path to authenticate requets with our Skiff Login system (OAuth2 Proxy). - * If config has an array of strings in the field "login_allowed_emails", then they are - * used to limit access to account with those email addresses. - */ - authPath(config): - if !('login_allowed_emails' in config) then - '/oauth2/auth' - else if std.length(config.login_allowed_emails) > 0 then - '/oauth2/auth?allowed_emails=' + std.join(',', config.login_allowed_emails), - - /** - * Returns Ingress annotations that enable authentication, given the provided Skiff config. - */ - getAuthAnnotations(config, tld): - if !('login' in config) then - {} - else if config.login == "ai2" then - { - 'nginx.ingress.kubernetes.io/auth-url': 'https://ai2.login' + tld + $.authPath(config), - 'nginx.ingress.kubernetes.io/auth-signin': 'https://ai2.login' + tld + '/oauth2/start?rd=https://$host$request_uri', - 'nginx.ingress.kubernetes.io/auth-response-headers': 'X-Auth-Request-User, X-Auth-Request-Email' - } - else if config.login == "google" then - { - 'nginx.ingress.kubernetes.io/auth-url': 'https://google.login' + tld + $.authPath(config), - 'nginx.ingress.kubernetes.io/auth-signin': 'https://google.login' + tld + '/oauth2/start?rd=https://$host$request_uri', - 'nginx.ingress.kubernetes.io/auth-response-headers': 'X-Auth-Request-User, X-Auth-Request-Email' - } - else - error 'Unknown login type: ' + config.login, -} - diff --git a/.skiff/webapp.jsonnet b/.skiff/webapp.jsonnet deleted file mode 100644 index 4309bb1..0000000 --- a/.skiff/webapp.jsonnet +++ /dev/null @@ -1,455 +0,0 @@ -/** - * This is a template that's compiled down to a definition of the - * infrastructural resources required for running your application. - * - * For more information on the JSONNET language, see: - * https://jsonnet.org/learning/getting_started.html - */ - -// This file is generated once at template creation time and unlikely to change -// from that point forward. -local config = import '../skiff.json'; -local util = import './util.libsonnet'; - -function( - appImage, proxyImage, cause, sha, env='prod', branch='', repo='', - buildId='' -) - // Produce a list of hostnames served by your application. - // See: https://skiff.allenai.org/domains.html. - local domains = - util.getHosts(env, config, '.apps.allenai.org') + - util.getHosts(env, config, '.allen.ai') + - if env == 'prod' && 'customDomains' in config then - config.customDomains else - []; - - local grouped = util.groupHosts( - domains, - ['.allenai.org', '.allen.ai', '.semanticscholar.org' ] - ); - local canonical = grouped[0]; - local extra = grouped[1]; - - // We provision 3 ingresses so that Skiff Login works with each TLD. We - // only support 3 TLS for authentication purposes. - local allenAIHosts = canonical['.allen.ai']; - local scholarHosts = canonical['.semanticscholar.org']; - - // The allenai.org TLD inherits everything else. - local hosts = canonical['.allenai.org'] + extra; - - // In production you run should run two or more replicas of your - // application, so that if one instance goes down or is busy (e.g., during - // a deployment), users can still use the remaining replicas of your - // application. - // - // However, if you use GPUs, which are expensive, consider setting the prod - // replica count to 1 as a trade-off between availability and costs. - // - // In all other environments (e.g., adhocs) we run a single instance to - // save money. - local numReplicas = if env == 'prod' then config.replicas.prod else 1; - - // Each app gets it's own namespace. - local namespaceName = config.appName; - - // Since we deploy resources for different environments in the same namespace, - // we need to give things a fully qualified name that includes the environment - // as to avoid unintentional collission / redefinition. - local fullyQualifiedName = config.appName + '-' + env; - - // Every resource is tagged with the same set of labels. These labels serve the - // following purposes: - // - They make it easier to query the resources, i.e. - // kubectl get pod -l app=my-app,env=staging - // - The service definition uses them to find the pods it directs traffic to. - local namespaceLabels = { - app: config.appName, - contact: config.contact, - team: config.team - }; - - local labels = namespaceLabels + { - env: env - }; - - local selectorLabels = { - app: config.appName, - env: env - }; - - // By default multiple instances of your application could get scheduled - // to the same node. This means if that node goes down your application - // does too. We use the label below to avoid that. - local antiAffinityLabels = { - onlyOneOfPerNode: config.appName + '-' + env - }; - local podLabels = labels + antiAffinityLabels; - - // Annotations carry additional information about your deployment that - // we use for auditing, debugging and administrative purposes - local annotations = { - "apps.allenai.org/sha": sha, - "apps.allenai.org/branch": branch, - "apps.allenai.org/repo": repo, - "apps.allenai.org/build": buildId - }; - - // Running on a GPU requires a special limit on the container, and a - // specific nodeSelector. - local gpuInConfig = std.count(std.objectFields(config), "gpu") > 0; - - // determine number of gpus - local gpuLimits = if gpuInConfig then - if config.gpu == "k80x2" || config.gpu == "a100-40gbx2" then - { 'nvidia.com/gpu': 2 } - else if config.gpu == "t4x4" then - { 'nvidia.com/gpu': 4 } - else - { 'nvidia.com/gpu': 1 } - else {}; - - local nodeSelector = if gpuInConfig then - if config.gpu == "k80" || config.gpu == "k80x2" then - { 'cloud.google.com/gke-accelerator': 'nvidia-tesla-k80' } - else if config.gpu == "p100" then - { 'cloud.google.com/gke-accelerator': 'nvidia-tesla-p100' } - else if config.gpu == "t4x4" then - { 'cloud.google.com/gke-accelerator': 'nvidia-tesla-t4' } - else if config.gpu == "a100-40gb" || config.gpu == "a100-40gbx2" then - { 'cloud.google.com/gke-accelerator': 'nvidia-tesla-a100' } - else - error "invalid GPU specification; expected 'k80', 'k80x2', 'p100', 't4x4', 'a100-40gb', or 'a100-40gbx2' but got: " + config.gpu - else - { }; - - // The port the NGINX proxy is bound to. - local proxyPort = 8080; - - // The port the streamlit app is bound to. - local appPort = 8000; - - // This is used to verify that the proxy (and thereby the UI portion of the - // application) is healthy. If this fails the application won't receive traffic, - // and may be restarted. - local proxyHealthCheck = { - port: proxyPort, - scheme: 'HTTP' - }; - - // This is used to verify that the streamlit app is funtional. - local appHealthCheck = { - port: appPort, - scheme: 'HTTP' - }; - - local namespace = { - apiVersion: 'v1', - kind: 'Namespace', - metadata: { - name: namespaceName, - labels: namespaceLabels - } - }; - - local tls = util.getTLSConfig(fullyQualifiedName, hosts); - local ingress = { - apiVersion: 'networking.k8s.io/v1', - kind: 'Ingress', - metadata: { - name: fullyQualifiedName, - namespace: namespaceName, - labels: labels, - annotations: annotations + tls.ingressAnnotations + util.getAuthAnnotations(config, '.apps.allenai.org') + { - 'nginx.ingress.kubernetes.io/ssl-redirect': 'true' - } - }, - spec: { - tls: [ tls.spec + { hosts: hosts } ], - rules: [ - { - host: host, - http: { - paths: [ - { - path: '/', - pathType: 'Prefix', - backend: { - service: { - name: fullyQualifiedName, - port: { - number: proxyPort - } - } - } - } - ] - } - } for host in hosts - ] - } - }; - - local allenAITLS = util.getTLSConfig(fullyQualifiedName + '-allen-dot-ai', allenAIHosts); - local allenAIIngress = { - apiVersion: 'networking.k8s.io/v1', - kind: 'Ingress', - metadata: { - name: fullyQualifiedName + '-allen-dot-ai', - namespace: namespaceName, - labels: labels, - annotations: annotations + allenAITLS.ingressAnnotations + util.getAuthAnnotations(config, '.allen.ai') + { - 'nginx.ingress.kubernetes.io/ssl-redirect': 'true' - } - }, - spec: { - tls: [ allenAITLS.spec + { hosts: allenAIHosts } ], - rules: [ - { - host: host, - http: { - paths: [ - { - path: '/', - pathType: 'Prefix', - backend: { - service: { - name: fullyQualifiedName, - port: { - number: proxyPort - } - } - } - } - ] - } - } for host in allenAIHosts - ] - } - }; - - local scholarTLS = util.getTLSConfig(fullyQualifiedName + '-scholar', scholarHosts); - local scholarIngress = { - apiVersion: 'networking.k8s.io/v1', - kind: 'Ingress', - metadata: { - name: fullyQualifiedName + '-scholar', - namespace: namespaceName, - labels: labels, - annotations: annotations + scholarTLS.ingressAnnotations + util.getAuthAnnotations(config, 'apps.semanticscholar.org') + { - 'nginx.ingress.kubernetes.io/ssl-redirect': 'true' - } - }, - spec: { - tls: [ scholarTLS.spec + { hosts: scholarHosts } ], - rules: [ - { - host: host, - http: { - paths: [ - { - path: '/', - pathType: 'Prefix', - backend: { - service: { - name: fullyQualifiedName, - port: { - number: proxyPort - } - } - } - } - ] - } - } for host in scholarHosts - ] - } - }; - - local deployment = { - apiVersion: 'apps/v1', - kind: 'Deployment', - metadata: { - labels: labels, - name: fullyQualifiedName, - namespace: namespaceName, - annotations: annotations + { - 'kubernetes.io/change-cause': cause - } - }, - spec: { - strategy: { - type: 'RollingUpdate', - rollingUpdate: { - maxSurge: numReplicas // This makes deployments faster. - } - }, - revisionHistoryLimit: 3, - replicas: numReplicas, - selector: { - matchLabels: selectorLabels - }, - template: { - metadata: { - name: fullyQualifiedName, - namespace: namespaceName, - labels: podLabels, - annotations: annotations - }, - spec: { - # This block tells the cluster that we'd like to make sure - # each instance of your application is on a different node. This - # way if a node goes down, your application doesn't: - # See: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#node-isolation-restriction - affinity: { - podAntiAffinity: { - requiredDuringSchedulingIgnoredDuringExecution: [ - { - labelSelector: { - matchExpressions: [ - { - key: labelName, - operator: "In", - values: [ antiAffinityLabels[labelName], ], - } for labelName in std.objectFields(antiAffinityLabels) - ], - }, - topologyKey: "kubernetes.io/hostname" - }, - ] - }, - }, - nodeSelector: nodeSelector, - containers: [ - { - name: fullyQualifiedName + '-app', - image: appImage, - # The "probes" below allow Kubernetes to determine - # if your application is working properly. - # - # The readinessProbe is used to determine if - # an instance of your application can accept live - # requests. The configuration below tells Kubernetes - # to stop sending live requests to your application - # if it returns 3 non 2XX responses over 30 seconds. - # When this happens the application instance will - # be taken out of rotation and given time to "catch-up". - # Once it returns a single 2XX, Kubernetes will put - # it back in rotation. - # - # Kubernetes also has a livenessProbe that can be used to restart - # deadlocked processes. You can find out more about it here: - # https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/#define-a-liveness-command - # - # We don't use a livenessProbe as it's easy to cause unnecessary - # restarts, which can be really disruptive to a site's availability. - # If you think your application is likely to be unstable after running - # for long periods send a note to reviz@allenai.org so we can work - # with you to craft the right livenessProbe. - readinessProbe: { - httpGet: appHealthCheck + { - path: '/?check=rdy' - }, - periodSeconds: 10, - failureThreshold: 3 - }, - # This tells Kubernetes what CPU and memory resources your app needs. - # We set these values low by default, as most applications receive - # bursts of activity and accordingly don't need dedicated resources - # at all times. - # - # Your application will be allowed to use more resources than what's - # specified below. But your application might be killed if it uses - # more than what's requested. If you know you need more memory - # or that your workload is CPU intensive, consider increasing the - # values below. - # - # For more information about these values, and the current maximums - # that your application can request, see: - # https://skiff.allenai.org/resources.html - resources: { - requests: { - cpu: 0.1, - memory: '500M' - }, - limits: { } - + gpuLimits # only the first container should have gpuLimits applied - }, - env: [ - { - name: 'STREAMLIT_ENV', - value: env, - } - ], - }, - { - name: fullyQualifiedName + '-proxy', - image: proxyImage, - readinessProbe: { - httpGet: proxyHealthCheck + { - path: '/?check=rdy' - } - }, - resources: { - requests: { - cpu: 0.1, - memory: '100M' - } - } - } - ] - } - } - } - }; - - local service = { - apiVersion: 'v1', - kind: 'Service', - metadata: { - name: fullyQualifiedName, - namespace: namespaceName, - labels: labels, - annotations: annotations - }, - spec: { - selector: selectorLabels, - ports: [ - { - port: proxyPort, - name: 'http' - } - ] - } - }; - - local pdb = { - apiVersion: 'policy/v1', - kind: 'PodDisruptionBudget', - metadata: { - name: fullyQualifiedName, - namespace: namespaceName, - labels: labels, - }, - spec: { - minAvailable: if numReplicas > 1 then 1 else 0, - selector: { - matchLabels: selectorLabels, - }, - }, - }; - - local defaultObjects = [ - namespace, - ingress, - allenAIIngress, - deployment, - service, - pdb - ]; - - if std.length(scholarHosts) > 0 then - defaultObjects + [ scholarIngress ] - else - defaultObjects diff --git a/app/.dockerignore b/app/.dockerignore deleted file mode 100644 index 232db3d..0000000 --- a/app/.dockerignore +++ /dev/null @@ -1,8 +0,0 @@ -# Python cache files -**.pyc -**/__pycache__ -.dockerignore - -# Docker artifacts -Dockerfile - diff --git a/app/.gitignore b/app/.gitignore deleted file mode 100644 index 9b1e997..0000000 --- a/app/.gitignore +++ /dev/null @@ -1,58 +0,0 @@ -# build artifacts - -.eggs/ -.mypy_cache -*.egg-info/ -build/ -dist/ -pip-wheel-metadata/ - - -# dev tools - -.envrc -.python-version -.idea -.venv/ -.vscode/ -/*.iml -pyrightconfig.json -.ruff.toml - - -# jupyter notebooks - -.ipynb_checkpoints - - -# miscellaneous - -.cache/ -doc/_build/ -*.swp -.DS_Store - - -# python - -*.pyc -*.pyo -__pycache__ - - -# testing and continuous integration - -.coverage -.pytest_cache/ -.benchmarks - -# documentation build artifacts - -docs/build -site/ - -# runs -/runs/ -/wandb/ -/scratch/ -core diff --git a/app/.streamlit/config.toml b/app/.streamlit/config.toml deleted file mode 100644 index aeacf66..0000000 --- a/app/.streamlit/config.toml +++ /dev/null @@ -1,13 +0,0 @@ -[server] -port = 8000 -enableCORS = false -enableXsrfProtection = false -headless = true - -[browser] -gatherUsageStats = false - -[theme] -primaryColor="#1A4CAE" -secondaryBackgroundColor="#F8F9FA" -textColor="#47515C" diff --git a/app/Dockerfile b/app/Dockerfile deleted file mode 100644 index 6bef32a..0000000 --- a/app/Dockerfile +++ /dev/null @@ -1,24 +0,0 @@ -FROM python:3.8.18 - -# Uncomment the following lines to make PyTorch available to your application. -# See https://skiff.allenai.org/gpu.html for more details. -# -# ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64 -# ENV NVIDIA_VISIBLE_DEVICES all -# ENV NVIDIA_DRIVER_CAPABILITIES compute,utility -# RUN pip install torch==1.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html - -WORKDIR /app - -# Install Python dependencies -COPY requirements.txt . -RUN pip install -r requirements.txt - -# Copy over the source code -COPY . ./ - -ENV PYTHONPATH=$PYTHONPATH':app/' - -# Kick things off -ENTRYPOINT [ "streamlit" ] -CMD [ "run", "./app.py" ] diff --git a/app/app.py b/app/app.py deleted file mode 100644 index ae7bd32..0000000 --- a/app/app.py +++ /dev/null @@ -1,232 +0,0 @@ -import datetime as dt -import itertools - -import pandas as pd -import streamlit as st -from PIL import Image - -from src.api.api import filter_resources -from src.components.goat_counter import add_goat_counter_tracker -from src.constants import BASE_DIR, ORDERED_SECTION_HEADERS -from src.theme import theme -from src.utils import create_markdown_img, load_data, load_logos - -pd.options.display.html.border = 0 - - -def write_resource(row, logos: dict) -> None: - col1, col2, col3, col4 = st.columns([0.4, 1, 5, 1], gap="small") - - modality_icons = [] - for mod_img, modality in [ - (logos["text"], "Text"), - (logos["vision"], "Vision"), - (logos["speech"], "Speech"), - ]: - mod_icon = ( - create_markdown_img(mod_img, None, 20) - if modality in row["Modalities"] - else " " - ) - modality_icons.append(mod_icon) - col1.markdown(" ".join(modality_icons), unsafe_allow_html=True) - - col2.write(row["Name"]) - col3.write(row["Description"]) - - logo_links = [] - for logo_img, col in [ - (logos["arxiv"], "Paper Link"), - (logos["hf"], "HuggingFace Link"), - (logos["github"], "GitHub Link"), - (logos["web"], "Website Link"), - ]: - logo_link = ( - create_markdown_img(logo_img, row[col], dim=20) if row[col] else " " - ) # "
" - logo_links.append(logo_link) - # col4.markdown(logo_link, unsafe_allow_html=True) - col4.markdown(" ".join(logo_links), unsafe_allow_html=True) - - -def streamlit_app(): - st.set_page_config( - page_title="Foundation Model Development Cheatsheet", layout="wide" - ) # , initial_sidebar_state='collapsed') - - RESOURCES = load_data() - LOGOS = load_logos() - - # add analytics tracking - add_goat_counter_tracker() - # add custom AI2 branded CSS theme and header banner - theme.add_theme() - - st.markdown( - "", - unsafe_allow_html=True, - ) - - # st.title("Foundation Model Development Cheatsheet") - st.markdown( - "

Foundation Model Development Cheatsheet

", - unsafe_allow_html=True, - ) - - st.caption( - "Resources and recommendations for best practices in developing and releasing models." - ) - st.markdown( - """ - This cheatsheet serves as a succinct guide, prepared *by* foundation model developers *for* foundation model developers. - As the field of AI foundation model development rapidly expands, welcoming new contributors, scientists, and - applications, we hope to lower the barrier for new community members to become familiar with the variety of - resources, tools, and findings. The focus of this cheatsheet is not only, or even primarily, to support building, - but to inculcate good practices, awareness of limitations, and general responsible habits as community norms. - - To add to the cheatsheet please see [Contribute Resources](https://github.com/allenai/fm-cheatsheet?tab=readme-ov-file#add-to-cheatsheet).""" - ) - scope_limitations_text = """ - We've compiled resources, tools, and papers that have helped guide our own intuitions around model development, - and which we believe will be especially helpful to nascent (and sometimes even experienced) developers in the field. - However, this guide is far from exhaustive---and here's what to consider when using it: - - * We scope these resources to newer foundation model developers, usually releasing models to the community. - Larger organizations, with commercial services, have even broader considerations for responsible development and release. - - * Foundation model development is a rapidly evolving science. **To keep this cheatsheet up-to-date, we are open to - public [contributions](https://github.com/allenai/fm-cheatsheet?tab=readme-ov-file#add-to-cheatsheet)**. - - * We've scoped our content modalities only to **text, vision, and speech**. - - * A cheatsheet **cannot be comprehensive**. - We prioritize resources we have found helpful, and rely heavily on survey papers and repositories to point out the - many other awesome works which deserve consideration, especially for developers who plan to dive deeper into a topic. - - * **We cannot take responsibility for these resources---onus is on the reader to assess their viability, particularly for - their circumstance.** At times we have provided resources with conflicting advice, as it is helpful to be aware of - divided community perspectives. Our notes throughout are designed to contextualize these resources, to help guide - the readers judgement. - """ - with st.expander("Scope & Limitations"): - st.markdown(scope_limitations_text) - col1a, col1b, col1c = st.columns([0.3, 0.3, 0.4], gap="small") - # TODO: Replace button links. - with col1a: - st.link_button( - "FM Dev Cheatsheet Paper", - "https://github.com/allenai/fm-cheatsheet/blob/main/app/resources/paper.pdf", - type="primary", - ) - with col1b: - st.link_button( - "Contribute Resources!", - "https://github.com/allenai/fm-cheatsheet?tab=readme-ov-file#add-to-cheatsheet", - type="primary", - ) - - st.markdown("
", unsafe_allow_html=True) - st.markdown( - "

Assembled by open model developers from AI2, EleutherAI, Google, Hugging Face, Masakhane, MIT, MLCommons, Princeton, Stanford CRFM, University of California Santa Barbara (UCSB), Univesity College London (UCL) and University of Washington (UW).

", - unsafe_allow_html=True, - ) - st.image("resources/orgs.png", use_column_width=True) - st.markdown("
", unsafe_allow_html=True) - - #### FILTER MENU STARTS HERE - - with st.form("data_selection"): - - category_select = st.multiselect( - label="Resource Categories:", - options=list(ORDERED_SECTION_HEADERS.keys()), - # default=["All"], - ) - - st.markdown( - '

Modalities:

', - unsafe_allow_html=True, - ) - - # col1, col2, col3 = st.columns([1, 2, 1], gap="medium") - # st.markdown("Modality Types:") - checkbox_text = st.checkbox("Text", value=True) - checkbox_vision = st.checkbox("Vision") - checkbox_speech = st.checkbox("Speech") - - date_format = "MMM, YYYY" # format output - start_date = dt.date(year=2000, month=1, day=1) - end_date = dt.datetime.now().date() - # max_days = end_date - start_date - - time_selection = st.slider( - label="Start Date:", - min_value=start_date, - value=start_date, - max_value=end_date, - format=date_format, - ) - - st.divider() - - # Every form must have a submit button. - submitted = st.form_submit_button("Submit Selection") - - #### FILTER MENU ENDS HERE - - # legend: - st.divider() - st.markdown("**Legend**") - col0, col1, col2, col3 = st.columns([1, 1, 1, 1], gap="small") - text_img = create_markdown_img(LOGOS["text"], None, 20) - vision_img = create_markdown_img(LOGOS["vision"], None, 20) - speech_img = create_markdown_img(LOGOS["speech"], None, 20) - arxiv_img = create_markdown_img(LOGOS["arxiv"], None, 20) - hf_img = create_markdown_img(LOGOS["hf"], None, 20) - github_img = create_markdown_img(LOGOS["github"], None, 20) - web_img = create_markdown_img(LOGOS["web"], None, 20) - - col1.markdown(text_img + " = Text Modality", unsafe_allow_html=True) - col1.markdown(vision_img + " = Vision Modality", unsafe_allow_html=True) - col1.markdown(speech_img + " = Speech Modality", unsafe_allow_html=True) - - col2.markdown(arxiv_img + " = Paper Link", unsafe_allow_html=True) - col2.markdown(hf_img + " = HuggingFace Link", unsafe_allow_html=True) - col2.markdown(github_img + " = GitHub Link", unsafe_allow_html=True) - col2.markdown(web_img + " = Web Link", unsafe_allow_html=True) - st.divider() - - if submitted: - for category in [s for s in ORDERED_SECTION_HEADERS if s in category_select]: - - filtered_resources = filter_resources( - RESOURCES, - # sections=category_select, - sections=[category], - text_mod=checkbox_text, - vision_mod=checkbox_vision, - speech_mod=checkbox_speech, - time_range=time_selection, - ) - - html_table = filtered_resources.to_html( - columns=["Modality", "Name", "Description", "Links"], - index=False, - header=False, - escape=False, - border=0, - ) - st.header(category) - st.write(ORDERED_SECTION_HEADERS[category]) - st.write(html_table, unsafe_allow_html=True) - st.divider() - - # Please don't edit or remove the content of this footer as we'd like to include these important - # links on all AI2 applications - theme.add_footer() - - -if __name__ == "__main__": - streamlit_app() diff --git a/app/requirements.txt b/app/requirements.txt deleted file mode 100644 index 73a380b..0000000 --- a/app/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -numpy==1.24.2 -pandas==1.3.5 -matplotlib -streamlit==1.27.0 -streamlit-aggrid==0.3.4 -jsonlines -awesome-streamlit==20200728.1 -beautifulsoup4==4.12.2 -lxml==5.0.0 -watchdog==4.0.0 diff --git a/app/resources/resources.jsonl b/app/resources/resources.json similarity index 100% rename from app/resources/resources.jsonl rename to app/resources/resources.json diff --git a/app/src/__init__.py b/app/src/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/app/src/api/__init__.py b/app/src/api/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/app/src/api/api.py b/app/src/api/api.py deleted file mode 100644 index 0f56714..0000000 --- a/app/src/api/api.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python3 - -import warnings - -import pandas as pd - - -def is_date_match(release_date, filter_start, filter_end="2030"): - def convert_to_dt(x): - if isinstance(x, str): - return ( - pd.to_datetime(x, format="%m-%Y") - if "-" in x - else pd.to_datetime(x, format="%Y") - ) - return x - - if not release_date or release_date.lower() == "frequently updated": - return True - - try: - release_datetime = convert_to_dt(release_date) - filter_start_dt = convert_to_dt(filter_start) - filter_end_dt = convert_to_dt(filter_end) - - return filter_start_dt <= release_datetime <= filter_end_dt - except Exception as e: - warnings.warn(f"Error in date comparison: {e}") - return False - - -# Function to apply the date filter -def apply_date_filter(df, release_date): - return df[df["Date"].apply(lambda x: is_date_match(x, release_date))] - - -# Function to search for resources -def search_resources(df, search_string): - return df[ - df.apply( - lambda row: search_string.lower() in row["Name"].lower() - or search_string.lower() in row["Description"].lower(), - axis=1, - ) - ] - - -def filter_resources( - resources_df, sections, text_mod, vision_mod, speech_mod, time_range -) -> pd.DataFrame: - # breakpoint() - - # Apply sections filter - if "All" not in sections: - resources_df = resources_df[ - resources_df["Categories"].apply( - lambda x: any(item in sections for item in x) - ) - ] - - # Apply combined modality filter - allowed_modalities = { - m - for m, flag in zip( - ["Text", "Vision", "Speech"], [text_mod, vision_mod, speech_mod] - ) - if flag - } - resources_df = resources_df[ - resources_df["Modalities"].apply( - lambda mods: any(mod in allowed_modalities for mod in mods) - ) - ] - - # Apply date filter - resources_df = apply_date_filter(resources_df, time_range) - - return resources_df diff --git a/app/src/components/__init__.py b/app/src/components/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/app/src/components/goat_counter.py b/app/src/components/goat_counter.py deleted file mode 100644 index 9ff8d15..0000000 --- a/app/src/components/goat_counter.py +++ /dev/null @@ -1,35 +0,0 @@ -import pathlib - -import streamlit as st -from bs4 import BeautifulSoup - -from src.utils import get_environment - - -def add_goat_counter_tracker(): - script_id = "skiff-stats" - index_path = pathlib.Path(st.__file__).parent / "static" / "index.html" - - skiff_stats_script = f""" - - """ - - if get_environment() == "prod": - # This is an extreme workaround to be able to inject a script tag into the streamlit template. - # Streamlit goes through well-meaning lengths to prevent this for security reasons. - # So we are essentially altering the base streamlit index.html file. - # inspired by: https://github.com/streamlit/streamlit/issues/969#issuecomment-1030832993 - soup = BeautifulSoup(index_path.read_text(), features="lxml") - if not soup.find(id=script_id): # if cannot find tag - html = str(soup) - # Insert the script directly in the head tag of the static template - new_html = html.replace("", skiff_stats_script + "") - index_path.write_text(new_html) - else: - # Search for and remove the Skiff Stats script if it is found. - # This is mainly a tool for aiding development in case the prod ENV is being tested locally. - soup = BeautifulSoup(index_path.read_text(), features="lxml") - if soup.find(id=script_id): # if find tag, remove it - html = str(soup) - new_html = html.replace(skiff_stats_script, "") - index_path.write_text(new_html) diff --git a/app/src/constants.py b/app/src/constants.py deleted file mode 100644 index 5f859bc..0000000 --- a/app/src/constants.py +++ /dev/null @@ -1,252 +0,0 @@ -from pathlib import Path - -ORDERED_SECTION_HEADERS = { - "Pretraining Data Sources": """ - Pretraining data consists of thousands, or even millions, of individual documents, often web scraped. - As a result, their contents are often superficially documented or understood. - Model knowledge and behavior will likely reflect a compression of this information and its communication qualities. - Consequently, its important to carefully select the data composition. - This decision should reflect choices in the language coverage, the mix of sources, and preprocessing decisions. - We highlight a few of the most popular pretraining corpora which have accumulated deeper documentation or analyses. - """, - "Finetuning Data Catalogs": """ - Finetuning data is used for a variety of reasons: to hone specific capabilities, orient the model to a certain task - format, improve its responses to general instructions, mitigate harmful or unhelpful response patterns, or generally - align its responses to human preferences. - Developers increasingly use a variety of data annotations and loss objectives for traditional supervised finetuning, - [DPO](https://arxiv.org/abs/2305.18290) or reinforcement learning with [human feedback](https://arxiv.org/abs/2203.02155). - As a result of this variety, we recommend data catalogs, with attached documentation, to help an informed selection. - The largest catalog is [HuggingFace Datasets](https://huggingface.co/docs/datasets/index), though cross-reference its metadata with - academic papers and repositories, as its crowdsourced documentation can be sparse or incorrect. - - Aside from HuggingFace Datasets, we point to some lesser known resources that catalog more specialized finetuning - data sources. - The breadth of available finetuning data is expansive, so we focus on catalogs rather than individual datasets, - and particularly those that provide strong documentation or more specialized sources. - """, - "Data Search, Analysis, & Exploration": """ - Exploring training datasets with search and analysis tools helps practitioners develop a nuanced intuition for - what's in the data, and therefore their model. - Many aspects of data are difficult to summarize or document without hands-on exploration. - For instance, text data can have a distribution of lengths, topics, tones, formats, licenses, and even diction. - We recommend developers use the many available tools to search and analyze their training datasets. - """, - "Data Cleaning, Filtering, & Mixing": """ - Data cleaning and filtering is an important step in curating a dataset. Filtering and cleaning remove unwanted - data from the dataset. They can improve training efficiency as well as ensuring that data has desirable properties, - including: high information content, desired languages, low toxicity, and minimal personally identifiable information. - We recommend that practitioners consider the possible trade-offs when using some filters. - For example, [Dodge et al. (2021)](https://arxiv.org/abs/2104.08758) find that some filters disproportionately remove - text written by, and about, minority individuals. Additionally, [Welbl et al. (2021)](https://arxiv.org/abs/2109.07445) - and [Longpre et al. (2023)](https://arxiv.org/abs/2305.13169) find that - removing content that classifiers believe are "toxic" can have adverse affects, including lowering performance on - evaluations, and disproportionately removing text representing marginalized groups. Data mixing is another - important component of data preparation, where the mixture proportions of pretraining data domains - (e.g. scientific articles, GitHub, and books) have been shown to dramatically affect downstream performance, - as shown in [The Pile](https://arxiv.org/abs/2101.00027), [Doremi](https://crfm.stanford.edu/2023/09/14/doremi), and - [Efficient Online Data Mixing](https://arxiv.org/abs/2312.02406).""", - "Data Deduplication": """ - Data deduplication is an important preprocessing step where duplicated documents, or chunks within a document, - are removed from the dataset. Removing duplicates can reduce the likelihood of memorizing undesirable pieces - of information such as boilerplate text, copyrighted data, and personally identifiable information. Additionally, - removing duplicated data improves training efficiency by reducing the total dataset size. - Practitioners should always determine whether duplicated data will harm or help the model for their use case. - For example, memorization is a crucial component for a model intended to be used in a closed-book question - answering system, but will tend to be harmful for application-agnostic models - (see [Lee et al. (2022)](https://arxiv.org/abs/2107.06499)).""", - "Data Decontamination": """ - Data decontamination is the process of removing evaluation data from the training dataset. This important step in data - preprocessing ensures the integrity of model evaluation, ensuring that metrics are reliable and not misleading. - The following resources aid in proactively protecting test data with canaries, decontaminating data before training, and - identifying or proving what data a model was trained on. - [A Note on Canary Exposure](https://arxiv.org/abs/2306.00133) explains how to interpret canary exposure, including by relating it to membership inference - attacks, and differential privacy. - [Proving Test Set Contamination in Black Box Language Models](https://arxiv.org/abs/2310.17623) provides methods for provable guarantees of test set contamination in language models without - access to pretraining data or model weights. - """, - "Data Auditing": """ - Auditing datasets is an essential component of dataset design. You should always spend a substantial amount of - time reading through your dataset, ideally at many stages of the dataset design process. Many datasets have - problems specifically because the authors did not do sufficient auditing before releasing them. - - At early stages of a project the data search, analysis, & exploration tools, outlined in the Data Search section, - are typically sufficient to track the evolution of a dataset. However it can also be helpful to do systematic - studies of the process.""", - "Data Documentation": """ - When releasing new data resources with a model, it is important to thoroughly document the - data, (see e.g. [Data Statements](https://aclanthology.org/Q18-1041/) or the [Data Nutrition Project](https://datanutrition.org/). - Documentation allows users to understand its intended uses, legal restrictions, attribution, relevant contents, - privacy concerns, and other limitations. - It is common for datasets to be widely used by practitioners, who may be unaware of undesirable properties - (such as [CSAM](https://www.theverge.com/2023/12/20/24009418/generative-ai-image-laion-csam-google-stability-stanford)). - While many data documentation standards have been proposed, their adoption has been uneven, or when - crowdsourced, as with [Hugging Face Datasets](https://huggingface.co/docs/datasets/index), they may contain - [some errors and omissions](https://arxiv.org/abs/2310.16787). - """, - "Data Governance": """ - Releasing all datasets involved in the development of a Foundation Model, including training, fine-tuning, and - evaluation data, can facilitate external scrutiny and support further research. However, releasing and hosting - the data as it was used may not always be an option, especially when it includes data with external rights-holders; - e.g., when data subjects' privacy, intellectual property, or other rights need to be taken into account. Proper - data governance practices can be required at the curation and release stages to account for these rights. - - In some jurisdictions, projects may be required to start with a Data Management Plan that requires developers to - ensure that the data collection has a sufficient legal basis, follows principles of data minimization, and allows - data subject to have sufficient visibility into and control over their representation in a dataset - ([CNIL resource sheet](https://www.cnil.fr/en/ai-how-sheets)). Data curation steps to that end can include - respecting opt-out preference signals (Spawning, HaveIBeenTrained), or applying pseudonymization or PII redaction - (BigCode Governance card). - - Once a dataset is released, it can be made available either broadly or with access control based on research needs - (ROOTS, BigCode PII training dataset). Developers can also enable data subjects to ask for removal from the hosted - version of the dataset by providing a contact address (OSCAR, PAraCrawl), possibly complemented by a membership - test to check whether their data is included (Stack data portraits) or an automated process (BigCode, AmIinTheStack). - """, - "Model Training: Pretraining Repositories": """ - Practitioners should consider using already-optimized codebases, especially in the pre-training phase, to - ensure effective use of computational resources, capital, power, and effort. Existing open-source codebases - targeted at foundation model pretraining can make pretraining significantly more accessible to new practitioners - and help accumulate techniques for efficiency in model training. - - Here, we provide a sample of existing widely-used pre-training codebases or component tools that developers can - use as a jumping-off point for pre-training foundation models.""", - "Model Training: Finetuning Repositories": """ - Fine-tuning, or other types of adaptation performed on foundation models after pretraining, are an equally - important and complex step in model development. Fine-tuned models are more frequently deployed than base models. - - Here, we also link to some useful and widely-used resources for adapting foundation models or otherwise fine-tuning - them. Use of these tools can ensure greater ecosystem compatibility of resulting models, or reduce the barrier to - experimentation by abstracting away common pitfalls or providing guidance on effective hyperparameters.""", - "Model Training: Efficiency & Resource Allocation": """ - Knowledge of training best practices and efficiency techniques can reduce costs to train a desired model significantly. - Here, we include a select few readings and resources on effectively using a given resource budget for model training, - such as several canonical papers on fitting *scaling laws*, a common tool for extrapolating findings across - scales of cost. These are used frequently to determine the most efficient allocation of resources, such as allocating - compute between model size and dataset size for a given budget. - - Additionally, practitioners seeking to embrace an open approach to model development should consider how their - decisions when training a foundation model may have impacts long after that model's creation and release. - For instance, a model that is released openly but is too computationally demanding to be run on consumer-grade - hardware will be limited in its impact on the field, or a model trained to minimize training compute but not - minimize inference cost may result in a greater environmental impact than spending more training compute in the - first place for a cheaper-to-infer model. Practitioners should thus be aware of potential second-order effects of - their model releases and training choices.""", - "Model Training: Educational Resources": """ - Training models at any scale can be quite daunting to newer practitioners. Here, we include several educational - resources that may be useful in learning about the considerations required for successfully and effectively - training or fine-tuning foundation models, and recommend that practitioners review these resources and use them - to guide further reading about model training and usage. """, - "Environmental Impact": """ - Current tools, including the ones mentioned in the table, focus on the latter point by measuring the energy - consumed during training or inference and multiplying it by the carbon intensity of the energy source used. - While other steps of the model life cycle (e.g. manufacturing hardware, heating/cooling datacenters, storing and - transferring data) also come with environmental impacts, we currently lack the information necessary to meaningfully - measure these impacts (see [Estimating the Carbon Footprint of BLOOM](https://arxiv.org/abs/2211.02001)). - The table below outlines resources for back-of-the-envelope estimations of environmental impact, in-code estimation, - as well as dashboard for cloud computing platforms to estimate environmental impact - (see [Carbontracker](https://arxiv.org/abs/2007.03051) and - [Quantifying the Carbon Emissions of Machine Learning](https://arxiv.org/abs/1910.09700)). - - For efficient use of resources, several decisions made during or prior to model training can have significant impacts on the upstream and - downstream environmental impact of a given model. - Use [Scaling Laws](https://arxiv.org/abs/2001.08361) and other methodologies to find the best allocation of your compute budget. - For models frequently used downstream, consider the inference footprint and inference cost during model creation, - to minimize the environmental impact of inference (see - [Scaling Data-Constrained Language Models](https://arxiv.org/abs/2305.16264)). - For further resources and discussion, see the resources and best practices for training models efficiently.""", - "Model Evaluation: Capabilities": """ - Many modern foundation models are released with general conversational abilities, such that their use cases are - poorly specified and open-ended. - This poses significant challenges to evaluation benchmarks which are unable to critically evaluate so many tasks, - applications, and risks systematically or fairly. - As a result, it is important to carefully scope the original intentions for the model, and the evaluations to those - intentions. - Even then, the most relevant evaluation benchmarks may not align with real use, and so should be qualified with their - limitations, and carefully supplemented with real user/human evaluation settings, where feasible. - - Below we note common benchmarks, as of December 2023, but caution that all of these come with substantial limitations. - For instance, many multiple choice college knowledge benchmarks are not indicative of real user questions, and can - be gamed with pseudo-data contamination. - Additionally, while leaderboards are exceedingly popular, model responses are often scored by other models, which - have implicit biases to model responses that are longer, and look similar to their own - (see [AlpacaFarm](https://arxiv.org/abs/2305.14387)).""", - "Model Evaluation: Risks & Harms Taxonomies": """ - Taxonomies provide a way of categorising, defining and understanding risks and hazards created through the use and - deployment of AI systems. Some taxonomies focus primarily on the types of interactions and uses that - *create* a risk of harm (often called "hazards") whereas others focus on the negative effects that - they lead to (often called "harms"). - Some taxonomies focus on existing issues, such as models that create hate speech or child abuse material, whereas - others are focused on longer term threats related to dangerous weapons development, cybersecurity, and military use. - These tend to focus on future model capabilities and their misuse. - Many taxonomies assess the available evidence for the risks and hazards, discuss their impact, and offer mitigation - strategies. - There is a substantial focus on text-only models and future work should consider paying more attention to multimodal - models.""", - "Model Evaluation: Risks & Harms": """ - Evaluations of risk serve multiple purposes: to identify if there are issues which need mitigation, to track the - success of any such mitigations, to document for other users of the model what risks are still present, and to - help make decisions related to model access and release. - Harm is highly contextual, so developers should consider the context in which their foundation model might be used - and evaluate the highest severity and most likely risks. - - To think through the possible risks, many taxonomies of harm have been created and provide good starting points. - Determining how to evaluate risk is also challenging, as there are risks and modalities with limited evaluation - coverage. The sample included below are a starting point for certain key areas, but we encourage developers to - browse the evaluation repository (linked below) to see if there is something more suited to their needs. - In addition to fixed benchmarks, an emergent approach to evaluation is using one model to evaluate another - (see [Red Teaming Language Models with Language Models](https://arxiv.org/abs/2202.03286)) and Anthropic's - [Constitutional AI](https://www.anthropic.com/news/constitutional-ai-harmlessness-from-ai-feedback) work.""", - "Model Documentation": """ - It is important to document models that are used and released. Even models and code released openly are important - to document thoroughly, in order to specify how to use the model, recommended and non-recommended use cases, - potential harms, state or justify decisions made during training, and more. - - Documenting models is important not just for responsible development, but also to enable other developers to - effectively build on a model. Models are not nearly as useful as artifacts if not properly documented. - - We include frequently-used standards for model documentation as well as tools for easy following of standards - and creation of documentation. """, - "Reproducibility": """ - Model releases often go accompanied with claims on evaluation performance, but those results are not always - reproducible, or can be misleading. - If code is not released, is not comprehensive, is difficult to run, or misses key details, this will cost the - scientific community time and effort to replicate and verify the claims. - Replication time will also slow progress, and discourage developers from adopting that resource over others. - - For these reasons, we strongly recommend carefully curating code, for model training, inference and evaluation. - Reproducible code begins with clear dependencies, versioning, and setup scripts, that are easy to adopt even if - the tools and frameworks are unfamiliar. - Clear documentation, code legibility and scripts for each entry point also improve ease of adoption. - Notably, Colab Notebooks provide shareable environment setup and execution tools. - These measures will significantly improves scientific reproducibility, and transparency.""", - "License Selection": """ - Foundation models, like software, are accompanied by licenses that determine how they may be distributed, used, - and repurposed. There are a variety of licenses to choose between for open foundation model developers, presenting - potential challenges for new developers. The table below includes resources that can help guide developers through - the process of selecting a specific license for their model as well as several examples of licenses that include - use restrictions. While licenses with use restrictions may be appropriate for certain types of models, in other - cases use restrictions can limit the ability of certain categories of stakeholders to re-use or adapt the models. - - Responsible AI Licenses in particular, including BigScience's Open RAIL and AI2's ImpACT Licenses, have seen - growing adoption, but also criticism of the difficulties they may pose even for well-intentioned actors seeking - to comply with their requirements---especially in commercial applications---and because their enforceability still - remains an open question - (see [AI Licensing Can't Balance "Open" with "Responsible"](https://katedowninglaw.com/2023/07/13/ai-licensing-cant-balance-open-with-responsible/)). - While they can provide a convenient way to help a developer - express their understanding of their model's limitations, in conjunction with a model card that outlines - in-scope and out-of-scope uses, adopters should also consider unintended consequences in limiting the scope of - the follow-up research that may be conducted with the licensed artifacts. Responsible AI licenses can act as a - useful norm-setting and self-reflection tool, but users should be aware of their limitations and potential downsides, - especially compared to established open-source software licenses.""", - "Usage Monitoring": """ - Some open foundation model developers attempt to monitor the usage of their models, whether by watermarking model - outputs or gating access to the model. - The table below includes resources related to usage monitoring, including examples of how to watermark content, - provide guidance on appropriate use, report adverse events associated with model use, and limit some forms of - access to models. - Several of these approaches have significant drawbacks: for example, there are no known robust watermarking - techniques for language models. As with many of the sections above, usage monitoring remains an area of active - research.""", -} - -BASE_DIR = Path(__file__).resolve().parent.parent diff --git a/app/src/theme/__init__.py b/app/src/theme/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/app/src/theme/style.css b/app/src/theme/style.css deleted file mode 100644 index 2030f4c..0000000 --- a/app/src/theme/style.css +++ /dev/null @@ -1,117 +0,0 @@ -:root { - /* Color variables */ - --color-b6: #265ED4; - --color-b7: #1A4CAE; - --color-b8: #1B4596; - --color-b10: #223367; - --color-n2: #F8F9FA; - /* semantic palette */ - --color-palette-primary-default: var(--color-b6); - --color-palette-primary-dark: var(--color-b7); - --color-palette-primary-veryDark: var(--color-b8); - - --color-palette-background-light: var(--color-n2); - --color-palette-background-dark: var(--color-b10); - - /* Breakpoints */ - --breakpoint-md: 768px; - - /* Z indices */ - /* Note this value was observed in their calendar component popover and is not officially documented anywhere. - * We are assuming that everything above this will appear over their popovers. - */ - --z-index-streamlit-popover: 1000040; - /* semantic z indices */ - /* Place the header just under popovers but above all other content */ - --z-index-header: calc(var(--z-index-streamlit-popover) - 1); -} - -/* Hide default streamlit header */ -header { - visibility: hidden; -} - -/* Custom AI2 header */ -.header { - background: var(--color-palette-background-dark); - color: var(--color-palette-background-light); - position: fixed; - top: 0; - left: 0; - width: 100%; - padding: .25em 0; - z-index: var(--z-index-header); -} - -.header>* { - margin-left: calc(18rem); -} - -@media only screen and (max-width: var(--breakpoint-md)) { - .header>* { - margin-left: 1em; - } -} - -/* Pushing streamlit controls below the custom AI2 banner */ -#MainMenu { - visibility: visible; - /* moving menu button below our custom header */ - margin-top: 4rem; -} - -section[data-testid="stSidebar"] { - margin-top: calc(2rem + 2px); -} - -section[data-testid="stSidebar"]>div:nth-child(2) { - top: calc(2.5rem); -} - -/* Button styling */ -button[kind="primary"] { - background-color: var(--color-palette-primary-dark); - color: var(--color-palette-background-light); -} - -button[kind="primary"]:hover, -button[kind="primary"]:active, -button[kind="primary"]:focus, -button[kind="primary"]:focus:not(:active) { - color: #fff; - text-decoration: none; - border-color: #3a69ba; - background: #3a69ba; -} - -.block-container { - padding-top: 0rem; - padding-bottom: 1rem; - padding-left: 4rem; - padding-right: 4rem; -} - -/* Hide default streamlit footer */ -footer { - visibility: hidden; -} - - -/* Hide borders for the table */ -table { - border: none!important; - /* Remove borders around the outside of the table */ -} - -/* Hide borders for table cells */ -th, td { - border: none!important; - border-style: none; - border-width: 0px; - /* Remove borders around table cells */ -} - - -#logo { - margin-bottom: 0rem; -} diff --git a/app/src/theme/theme.py b/app/src/theme/theme.py deleted file mode 100644 index 79dedc1..0000000 --- a/app/src/theme/theme.py +++ /dev/null @@ -1,52 +0,0 @@ -import os -from os import path - -import streamlit as st - -from src.utils import get_environment - -__location__ = path.realpath(path.join(os.getcwd(), path.dirname(__file__))) - - -def add_theme(): - with open(os.path.join(__location__, "style.css")) as f: - st.markdown(f"", unsafe_allow_html=True) - # hide the streamlit menu if prod - if get_environment() != "dev": - st.markdown( - """ - - """, - unsafe_allow_html=True, - ) - # add the AI2 banner - add_header() - - -def add_header(): - ... - # st.write( - # '
' - # 'Allen Institute for AI
', - # unsafe_allow_html=True, - # ) - - -def add_footer(): - """ - Please don't edit or remove this footer as we'd like to include these important links on all AI2 applications - """ - st.markdown( - "

" - "Website hosted by the Allen Institute for AI | " - " Privacy Policy | " - " Terms of Use | " - " Business Code of Conduct" - "

", - unsafe_allow_html=True, - ) diff --git a/app/src/utils.py b/app/src/utils.py deleted file mode 100644 index ae36e6b..0000000 --- a/app/src/utils.py +++ /dev/null @@ -1,86 +0,0 @@ -import base64 -import gzip -import os -from pathlib import Path - -import jsonlines -import pandas as pd -import streamlit as st - -from .constants import BASE_DIR - - -def read_jsonl(inpath: Path): - if inpath.suffix in (".gz", ".gzip"): - with gzip.open(inpath, "rb") as fp: - j_reader = jsonlines.Reader(fp) - return [l for l in j_reader] - else: - with open(inpath, "rb") as fp: - j_reader = jsonlines.Reader(fp) - return [l for l in j_reader] - - -@st.cache_data -def load_data(): - df = pd.DataFrame(read_jsonl(BASE_DIR / "resources" / "resources.jsonl")).fillna("") - logos = load_logos() - - def add_links(row): - links = [] - if row["Paper Link"]: - links.append(create_markdown_img(logos["arxiv"], row["Paper Link"], 20)) - if row["HuggingFace Link"]: - links.append(create_markdown_img(logos["hf"], row["HuggingFace Link"], 20)) - if row["GitHub Link"]: - links.append(create_markdown_img(logos["github"], row["GitHub Link"], 20)) - if row["Website Link"]: - links.append(create_markdown_img(logos["web"], row["Website Link"], 20)) - - return " ".join(links) - - df["Links"] = df.apply(add_links, axis=1) - - def add_modality(row): - return " ".join( - [ - create_markdown_img(logos[modality.lower()], None, 20) - for modality in row["Modalities"] - ] - ) - - df["Modality"] = df.apply(add_modality, axis=1) - - return df - - -def load_logos(): - def get_image_base64(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode() - - return { - "hf": get_image_base64(BASE_DIR / "resources" / "logos/hf.png"), - "web": get_image_base64(BASE_DIR / "resources" / "logos/web.png"), - "arxiv": get_image_base64(BASE_DIR / "resources" / "logos/arxiv.png"), - "github": get_image_base64(BASE_DIR / "resources" / "logos/github.png"), - "text": get_image_base64(BASE_DIR / "resources" / "logos/text.png"), - "vision": get_image_base64(BASE_DIR / "resources" / "logos/vision.png"), - "speech": get_image_base64(BASE_DIR / "resources" / "logos/speech.png"), - "cheatsheet": get_image_base64( - BASE_DIR / "resources" / "logos/cheatsheet-0.png" - ), - } - - -@st.cache_data -def create_markdown_img(base64_string, link_url=None, dim=15): - img_tag = f'Image' - if link_url: - return f'{img_tag}' - else: - return img_tag - - -def get_environment(): - return os.getenv("STREAMLIT_ENV", "dev") diff --git a/bin/verify b/bin/verify deleted file mode 100755 index fe80d12..0000000 --- a/bin/verify +++ /dev/null @@ -1,167 +0,0 @@ -#!/usr/bin/env python3 -# -# Verify the code by: -# - Running the integration tests defined in it/ against the running -# application. -# - -import os -import json -import shlex - -from argparse import ArgumentParser -from pathlib import Path -from subprocess import check_call, check_output, call -from dataclasses import dataclass, field -from typing import Dict, Optional -from time import sleep - -SCRIPT_DIR = Path(os.path.dirname(os.path.realpath(__file__))) -ROOT = SCRIPT_DIR / ".." - -def get_app_name() -> str: - with open(ROOT / "skiff.json", mode="r", encoding="utf-8") as fh: - return json.load(fh).get("appName") - -def check(exit_code: int) -> None: - if exit_code != 0: - raise RuntimeError(f"command failed with exit code: {exit_code}") - -@dataclass -class Image: - id: str - path: str - dockerfile: str = "Dockerfile" - build_args: Dict[str, str] = field(default_factory=dict) - - def tag(self) -> str: - return f"gcr.io/ai2/reviz/{self.id}:latest" - - def build(self) -> str: - env = os.environ.copy() - env["DOCKER_BUILDKIT"] = "1" - args = " ".join([ f"--build-arg {name}={value}" for name, value in self.build_args.items() ]) - cmd = shlex.split(f""" - docker build - --tag {self.tag()} - --file {self.path}/{self.dockerfile} - {args} - {self.path} - """) - check_call(cmd, cwd=ROOT, env=env) - return self.digest() - - def digest(self) -> str: - cmd = shlex.split(f"docker image inspect {self.tag()}") - info = json.loads(check_output(cmd, cwd=ROOT, encoding="utf-8"))[0] - return info["Id"] - -@dataclass -class Container: - id: str - image: str - host_port: Optional[int] = None - container_port: Optional[int] = None - detach: bool = True - env: Dict[str, str] = field(default_factory=dict) - network: Optional[str] = None - tty: bool = False - init: bool = False - - def start(self, cmd: str = "") -> int: - env = " ".join([ f"--env {name}={value}" for name, value in self.env.items() ]) - detach = "--detach" if self.detach is True else "" - publish = "" - if self.host_port is not None and self.container_port is not None: - publish = f"--publish {self.host_port}:{self.container_port}" - network = f"--network {self.network}" if self.network is not None else "" - tty = "--tty" if self.tty else "" - init = "--init" if self.init else "" - start_cmd = shlex.split(f""" - docker run - --name {self.id} - --rm - {detach} - {env} - {publish} - {network} - {tty} - {init} - {self.image} - {cmd} - """) - return call(start_cmd) - - def run(self, cmd: str = "") -> int: - return self.start(cmd) - - def stop(self) -> None: - cmd = shlex.split(f"docker stop {self.id}") - check_call(cmd) - -if __name__ == "__main__": - parser = ArgumentParser( - prog="verify", - description="A helper for running integration tests for your application." - ) - - # Even though there's no arguments, we have to try to parse them - # in order to provide support for `--help`. We do this for good measure. - args = parser.parse_args() - - app_name = get_app_name() - - app_id = f"{app_name}-app" - app_image = Image(app_id, path="app") - app = Container( - app_id, - image=app_image.build(), - host_port=8000, - container_port=8000, - env={ "SKIFF_ENV": "production" }, - init=True - ) - proxy_id = f"{app_name}-proxy" - proxy_image = Image( - proxy_id, - path="proxy", - dockerfile="Dockerfile", - build_args={ "CONF_FILE": 'prod.conf' } - ) - proxy = Container( - proxy_id, - image=proxy_image.build(), - network="host" - ) - - it_id = f"{app_name}-it" - it_image = Image(it_id, path="it") - it = Container( - it_id, - image=it_image.build(), - network="host", - detach=False, - tty=True, - env={ "IT_ORIGIN": "http://localhost:8080" } - ) - - try: - check(app.start()) - check(proxy.start()) - print("waiting for application to start...") - is_ready = False - attempts = 0 - while not is_ready: - exit_code = it.run(f"check:ready http://localhost:8080") - is_ready = exit_code == 0 - attempts += 1 - if not is_ready: - if attempts >= 5: - raise RuntimeError("application not ready after 5 attempts") - print("application is not ready, sleeping for 5 seconds...") - sleep(5) - check(it.run()) - finally: - app.stop() - proxy.stop() - diff --git a/docker-compose.yaml b/docker-compose.yaml deleted file mode 100644 index 6049cdd..0000000 --- a/docker-compose.yaml +++ /dev/null @@ -1,46 +0,0 @@ -version: '3' -services: - sonar: - build: ./sonar - depends_on: - - app - - proxy - app: - build: ./app - volumes: - - ./app:/app - environment: - # This ensures that errors are printed as they occur, which - # makes debugging easier. - - PYTHONUNBUFFERED=1 - - LOG_LEVEL=DEBUG - - proxy: - build: ./proxy - ports: - - 8080:8080 - depends_on: - - app - it: - build: ./it - # This tells Docker to run our entrypoint in a way that ensures - # signals are handled properly and that zombie processes are killed. - # The main outcome being that it makes it faster to shut down the - # stack. - init: true - # We override the default command so that the integration tests don't - # run on startup, and so that the container stays alive. This makes - # it faster when a developer wants to run the integration tests. - entrypoint: - - tail - command: - - -f - - /dev/null - volumes: - - ./it/src:/it/src - - ./it/package.json:/it/package.json - - ./it/yarn.lock:/it/yarn.lock - - ./it/tsconfig.json:/it/tsconfig.json - depends_on: - - proxy - - app diff --git a/it/.dockerignore b/it/.dockerignore deleted file mode 100644 index 0d76b02..0000000 --- a/it/.dockerignore +++ /dev/null @@ -1,3 +0,0 @@ -node_modules/ -Dockerfile -.dockerignore diff --git a/it/.eslintignore b/it/.eslintignore deleted file mode 100644 index e3a22c3..0000000 --- a/it/.eslintignore +++ /dev/null @@ -1,4 +0,0 @@ -node_modules/ -dist/ -package.json -tsconfig.json diff --git a/it/.eslintrc.js b/it/.eslintrc.js deleted file mode 100644 index 71a79ea..0000000 --- a/it/.eslintrc.js +++ /dev/null @@ -1,3 +0,0 @@ -module.exports = { - extends: ['@allenai/eslint-config-varnish'], -}; diff --git a/it/.gitignore b/it/.gitignore deleted file mode 100644 index b947077..0000000 --- a/it/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -node_modules/ -dist/ diff --git a/it/Dockerfile b/it/Dockerfile deleted file mode 100644 index add5a5e..0000000 --- a/it/Dockerfile +++ /dev/null @@ -1,16 +0,0 @@ -FROM node:16-alpine - -WORKDIR /it - -ARG NODE_ENV -ENV NODE_ENV ${NODE_ENV:-development} - -COPY package.json yarn.lock ./ -RUN yarn install --frozen-lockfile - -COPY . . - -RUN yarn tsc - -ENTRYPOINT [ "yarn" ] -CMD [ "test" ] diff --git a/it/fixture/robots.txt b/it/fixture/robots.txt deleted file mode 100644 index 9d64e32..0000000 --- a/it/fixture/robots.txt +++ /dev/null @@ -1,9 +0,0 @@ -# If you would like to obtain the data behind this application, please contact -# The Allen Institute for AI: http://www.allenai.org -# -# Our models can require a significant amount of computation, and crawling the -# app comes at our expense and can even cause applications to fail. -# We will block unauthorized crawling of the app. -# -# Privacy Policy: https://allenai.org/privacy-policy.html -# Terms and Conditions of use: https://allenai.org/terms.html diff --git a/it/is_ready.js b/it/is_ready.js deleted file mode 100644 index 93b35b5..0000000 --- a/it/is_ready.js +++ /dev/null @@ -1,45 +0,0 @@ -/** - * This script is used by /bin/verify, to determine if the application is - * ready. The /bin/verify script can't simply hit http://localhost:8080, - * because on MacOS docker runs in a VM and when the "host" network is used - * it's the host network of that VM. - * - * The script just makes a request to the provided origin. If a 200 response is - * received it exists with the 0 exit code. If the request fails, or a non-200 - * response is received it exists with a non-zero exit code. - * - * The origin should be provided as the first argument to this script, i.e.: - * - * is_ready http://localhost:8080 - * - */ - -const http = require('http'); - -async function checkIfReady(origin) { - return new Promise((resolve, reject) => { - const req = http.request(origin, (res) => { - const isReady = res.statusCode === 200; - console.log(`${origin} ~> ${res.statusCode}: ${isReady ? 'OK' : 'Not Ready'}`); - if (isReady) { - resolve(); - } else { - reject(new Error(`Response status: ${res.statusCode}`)); - } - }); - req.on('error', reject); - req.end(); - }); -} - -const origin = process.argv[2]; -if (!origin) { - throw Error('You must set an origin. Usage: node is_ready ORIGIN'); -} - -checkIfReady(origin) - .then(() => process.exit(0)) - .catch((err) => { - console.error(`Not ready: ${err}`); - process.exit(1); - }); diff --git a/it/package.json b/it/package.json deleted file mode 100644 index 9e97547..0000000 --- a/it/package.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "name": "it", - "version": "0.0.1", - "private": true, - "description": "Integration Tests", - "license": "UNLICENSED", - "scripts": { - "check:ready": "node is_ready.js", - "lint": "eslint '**/*.{js,ts,tsx,json}' && echo 'πŸ’« Lint complete.'", - "lint:fix": "eslint '**/*.{js,ts,tsx,json}' --fix && sort-package-json && echo 'πŸ›  Lint --fix complete.'", - "test": "yarn test:build && yarn test:run", - "test:build": "tsc", - "test:run": "mocha 'dist/**/*.test.js'" - }, - "resolutions": { - "ansi-regex": "^5.0.1", - "trim": "^1.0.1", - "nanoid": "^3.1.31" - }, - "dependencies": { - "axios": "^0.21.2", - "mocha": "^9.0.2" - }, - "devDependencies": { - "@allenai/eslint-config-varnish": "^1.0.7-alpha.0", - "@types/mocha": "^8.2.3", - "@types/node": "^12.12.6", - "@typescript-eslint/eslint-plugin": "^4.29.1", - "@typescript-eslint/parser": "^4.29.1", - "eslint": "^7.32.0", - "eslint-config-prettier": "^8.3.0", - "eslint-config-standard": "^16.0.3", - "eslint-plugin-import": "^2.24.0", - "eslint-plugin-mdx": "^1.14.1", - "eslint-plugin-node": "^11.1.0", - "eslint-plugin-prettier": "^3.4.0", - "eslint-plugin-promise": "^5.1.0", - "eslint-plugin-react": "^7.24.0", - "eslint-plugin-standard": "^5.0.0", - "prettier": "^2.3.2", - "sort-package-json": "^1.50.0", - "typescript": "^4.1.6" - } -} diff --git a/it/src/conf.ts b/it/src/conf.ts deleted file mode 100644 index 834fa8e..0000000 --- a/it/src/conf.ts +++ /dev/null @@ -1,39 +0,0 @@ -import { URL } from 'url'; -import path from 'path'; -import fs from 'fs'; -import util from 'util'; - -export namespace conf { - export class Config { - constructor(readonly origin: string, readonly fixtureRoot: string) {} - - url() { - return new URL('', this.origin); - } - - fixture(file: string) { - if (path.isAbsolute(file)) { - throw new Error(`${file} is an absolute path, must be relative`); - } - return path.resolve(this.fixtureRoot, file); - } - - async readFixture(file: string, encoding: string = 'utf-8') { - // This is gross, but this is the accepted mechanism for producing - // a version of fs.readFile that can be used with async/await. - const rd = util.promisify(fs.readFile); - return rd(this.fixture(file), encoding); - } - - static fromEnv(): Config { - // The default, `http://proxy:8080`, is the origin of the NGINX - // reverse proxy when running things via `docker compose`. - return new Config( - process.env.IT_ORIGIN || 'http://proxy:8080', - process.env.IT_FIXTURE_ROOT || '/it/fixture' - ); - } - } -} - -export default conf; diff --git a/it/src/site.test.ts b/it/src/site.test.ts deleted file mode 100644 index 2a3f108..0000000 --- a/it/src/site.test.ts +++ /dev/null @@ -1,59 +0,0 @@ -import axios from 'axios'; - -import assert from 'assert'; - -import conf from './conf'; - -/** - * Returns the mime-type and optional charset expressed in the provided string, - * which is presumedly the value of the HTTP Content-Type header. The values - * are converted to lowercase before being returned. - * - * We do this so that the tests pass in both the development environment and - * when running with production settings. The charset is returned as UTF-8 by - * the server used to serve the UI in development, and utf-8 by NGINX when - * using production settings. - */ -function parseContentType(header: string): { mimeType: string; charset?: string } { - const [mimeType, rawCharset] = header.toLowerCase().split(';'); - const charset = (rawCharset ?? '').split('=')[1]; - return { mimeType, charset }; -} - -describe('/', () => { - it('returns a 200', async () => { - const config = conf.Config.fromEnv(); - - const url = config.url(); - url.pathname = '/'; - - const resp = await axios.get(url.toString()); - - assert.strictEqual(resp.status, 200); - assert.deepStrictEqual(parseContentType(resp.headers['content-type']), { - mimeType: 'text/html', - charset: 'utf-8', - }); - }); -}); - - -describe('/robots.txt', () => { - it('returns the expected response', async () => { - const config = conf.Config.fromEnv(); - - const url = config.url(); - url.pathname = '/robots.txt'; - - const resp = await axios.get(url.toString()); - - assert.strictEqual(resp.status, 200); - assert.deepStrictEqual(parseContentType(resp.headers['content-type']), { - mimeType: 'text/plain', - charset: 'utf-8', - }); - - const robotsTxt = await config.readFixture('robots.txt'); - assert.strictEqual(resp.data, robotsTxt); - }); -}); diff --git a/it/tsconfig.json b/it/tsconfig.json deleted file mode 100644 index 64b408c..0000000 --- a/it/tsconfig.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "compilerOptions": { - "target": "es5", - "allowJs": false, - "strict": true, - "forceConsistentCasingInFileNames": true, - "module": "commonjs", - "moduleResolution": "node", - "allowSyntheticDefaultImports": true, - "esModuleInterop": true, - "outDir": "dist" - }, - "exclude": [ - "node_modules" - ], - "include": [ - "src/**/*.ts" - ] -} diff --git a/it/yarn.lock b/it/yarn.lock deleted file mode 100644 index 55a8cba..0000000 --- a/it/yarn.lock +++ /dev/null @@ -1,2747 +0,0 @@ -# THIS IS AN AUTOGENERATED FILE. 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"appName": "openfm", - "contact": "lucas", - "team": "s2research", - "replicas": { "prod": 1 }, - "customDomains": ["fmcheatsheet.org"] -} diff --git a/sonar/Dockerfile b/sonar/Dockerfile deleted file mode 100644 index 7a745d2..0000000 --- a/sonar/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM python:3.7.2-alpine3.9 - -WORKDIR /sonar - -COPY requirements.txt . - -RUN pip install -r requirements.txt - -COPY ping.py . - -ENV PYTHONUNBUFFERED=1 - -ENTRYPOINT [ "python" ] -CMD [ "ping.py" ] diff --git a/sonar/ping.py b/sonar/ping.py deleted file mode 100644 index 84019a5..0000000 --- a/sonar/ping.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- - -import requests -import time -import math -import signal - -def is_ok(url: str) -> bool: - """ - Returns True if the provided URL responds with a 2XX when fetched via - a HTTP GET request. - """ - try: - resp = requests.get(url) - except: - return False - return True if math.floor(resp.status_code / 100) == 2 else False - -def scan(): - """ - Broadcasts the availability of the proxy's HTTP server once the app is ready for traffic. - - This script exists solely to ease confusion locally, as both Streamlit logs tells the - user about the ports they're bound to (even though they're inaccessible). - """ - - print("") - print("βš“οΈ Ahoy!") - print("") - print( - "Your application is starting and will be available at " + - "http://localhost:8080 when it's ready." - ) - print("") - - # If someone tries to cancel the `docker-compose up` invocation, docker - # will send a SIGTERM to the program. We need to handle this and set a - # value that allows the loop to be broken. - term = False - def handle_interrupt(signal_number, stack_frame): - global term - term = True - signal.signal(signal.SIGTERM, handle_interrupt) - - last_check = time.perf_counter() - is_app_live = False - while (is_app_live != True): - if term is True: - break - # We don't use `time.sleep()`, as that'd prevent us from being able - # to break the loop quickly in the event of a SIGTERM. - now = time.perf_counter() - if (now - last_check >= 5): - last_check = now - if not is_app_live: - is_app_live = is_ok("http://app:8000") - if is_app_live: - print("") - print("✨ Your local environment is ready:") - print("") - print(" http://localhost:8080") - print("") - print("⛡️ Smooth sailing!") - print("") - -if __name__ == "__main__": - scan() diff --git a/sonar/requirements.txt b/sonar/requirements.txt deleted file mode 100644 index a8ed785..0000000 --- a/sonar/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -requests==2.26.0 From 5f5d8d8a793c8e29673c9ea210c6b05c8cb647f1 Mon Sep 17 00:00:00 2001 From: justin Date: Fri, 10 May 2024 07:35:34 -0500 Subject: [PATCH 02/40] reorg --- app/resources/resources.json | 265 -- static/app/resources/resources.json | 4823 +++++++++++++++++++++++++++ 2 files changed, 4823 insertions(+), 265 deletions(-) delete mode 100644 app/resources/resources.json create mode 100644 static/app/resources/resources.json diff --git a/app/resources/resources.json b/app/resources/resources.json deleted file mode 100644 index 1fc250c..0000000 --- a/app/resources/resources.json +++ /dev/null @@ -1,265 +0,0 @@ -{"Name": "BigBench", "Description": "A collaborative benchmark of 100s of tasks, probing LLMs on a wide array of unique capabilities.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "6-2022", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2206.04615", "Website Link": "", "GitHub Link": "https://github.com/google/BIG-bench", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BigBench Hard", "Description": "A challenging subset of 23 BigBench tasks where at time of release models did not outperform annotator performance.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "10-2022", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2210.09261", "Website Link": "", "GitHub Link": "https://github.com/suzgunmirac/BIG-Bench-Hard", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BigCode Evaluation Harness", "Description": "A framework for the evaluation of code generation models, compiling many evaluation sets.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "CLIP benchmark", "Description": "Image classification, retrieval and captioning", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "4-2022", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/LAION-AI/CLIP_benchmark", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "DataComp eval suite", "Description": "38 image classification and retrieval downstream tasks", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "4-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2304.14108", "Website Link": "https://www.datacomp.ai/", "GitHub Link": "https://github.com/mlfoundations/datacomp#evaluation", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "HEIM", "Description": "A large suite of text-to-image evaluations. Useful for thorough capability analysis of these model types.", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "11-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://crfm.stanford.edu/heim/v1.1.0/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "HELM classic", "Description": "A large suite of benchmarks and metric types, to holistically evaluate many model qualities aside from performance on general tasks. Useful for a thorough comparison against other well known models.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "11-2022", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2211.09110", "Website Link": "https://crfm.stanford.edu/helm/latest/", "GitHub Link": "https://github.com/stanford-crfm/helm", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Hugging Face Leaderboards Collection", "Description": "A collection of unique leaderboards on Hugging Face for ranking models across modalities and tasks.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "Frequently Updated", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "https://huggingface.co/blog?tag=leaderboard", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/collections/clefourrier/leaderboards-and-benchmarks-64f99d2e11e92ca5568a7cce", "Added By": "Original Authors"} -{"Name": "HumanEvalPack", "Description": "HumanEvalPack is a code evaluation benchmark across 6 languages and 3 tasks, extending OpenAI's HumanEval.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "8-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2308.07124", "Website Link": "", "GitHub Link": "https://github.com/bigcode-project/octopack", "HuggingFace Link": "https://huggingface.co/datasets/bigcode/humanevalpack", "Added By": "Original Authors"} -{"Name": "Lighteval", "Description": "Small, highly configurable LLM evaluation library, for fast experimentation and iteration.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/huggingface/lighteval", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "LM Evaluation Harness", "Description": "Orchestration framework for standardizing LM prompted evaluation, supporting hundreds of subtasks.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities", "Reproducibility"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/EleutherAI/lm-evaluation-harness", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "LMSys Chatbot Arena", "Description": "A leaderboard of models based on Elo ratings where humans or models select their preferred response between two anonymous models. Chatbot Arena, MT-Bench, and 5-shot MMLU are used as benchmarks. This resource provides a general purpose, and GPT-4 biased perspective into model capabilities.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "Frequently Updated", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2306.05685", "Website Link": "", "GitHub Link": "https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md", "HuggingFace Link": "https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard", "Added By": "Original Authors"} -{"Name": "MMBench", "Description": "A joint vision and text benchmark evaluating dozens of capabilities, using curated datasets and ChatGPT in the loop.", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "7-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2307.06281", "Website Link": "https://opencompass.org.cn/mmbench", "GitHub Link": "https://github.com/open-compass/MMBench", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "MME", "Description": "An evaluation benchmark for multimodal large language models with 14 manually curated subtasks, to avoid data leakage.", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "6-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2306.13394", "Website Link": "", "GitHub Link": "https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "MTEB", "Description": "The Massive Text Embedding Benchmark measures the quality of embeddings across 58 datasets and 112 languages for tasks related to retrieval, classification, clustering or semantic similarity.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "10-2022", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2210.07316", "Website Link": "", "GitHub Link": "https://github.com/embeddings-benchmark/mteb", "HuggingFace Link": "https://huggingface.co/spaces/mteb/leaderboard", "Added By": "Original Authors"} -{"Name": "OpenASR Leaderboard", "Description": "An automatic leaderboard ranking and evaluating speech recognition models on common benchmarks.", "Modalities": ["Speech"], "Categories": ["Model Evaluation: Capabilities"], "Date": "Frequently Updated", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/huggingface/open_asr_leaderboard", "HuggingFace Link": "https://huggingface.co/spaces/hf-audio/open_asr_leaderboard", "Added By": "Original Authors"} -{"Name": "OpenFlamingo eval suite", "Description": "VQA, captioning, classification", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "8-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2308.01390", "Website Link": "", "GitHub Link": "https://github.com/mlfoundations/open_flamingo/tree/main/open_flamingo/eval", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Open LLM Leaderboard", "Description": "A popular leaderboard on Hugging Face for ranking open LLMs on their knowledge, reasoning and math capabilities.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "Frequently Updated", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/open-llm-leaderboard", "Added By": "Original Authors"} -{"Name": "SWE Bench", "Description": "SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub. Given a codebase and an issue, a language model is tasked with generating a patch that resolves the described problem.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "10-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2310.06770", "Website Link": "https://www.swebench.com/", "GitHub Link": "https://github.com/princeton-nlp/SWE-bench", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The Edinburgh International Accents of English Corpus", "Description": "Benchmark dataset of diverse English varieties for evaluating automatic speech recognition models (typically trained and tested only on US English)", "Modalities": ["Speech"], "Categories": ["Model Evaluation: Capabilities"], "Date": "3-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2303.18110", "Website Link": "https://groups.inf.ed.ac.uk/edacc/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "HELM lite", "Description": "A lightweight subset of capability-centric benchmarks within HELM with comparisons to many prominent open and closed models.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Capabilities"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "https://crfm.stanford.edu/2023/12/19/helm-lite.html", "Website Link": "https://crfm.stanford.edu/helm/lite/latest/#/", "GitHub Link": "https://github.com/stanford-crfm/helm", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "MMMU", "Description": "A benchmark to evaluate joint text and vision models on 11k examples spanning 30 college-level subject domains.", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Capabilities"], "Date": "11-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2311.16502", "Website Link": "https://mmmu-benchmark.github.io/", "GitHub Link": "https://github.com/MMMU-Benchmark/MMMU", "HuggingFace Link": "https://huggingface.co/datasets/MMMU/MMMU", "Added By": "Original Authors"} -{"Name": "Anaconda", "Description": "An environment and dependency management tool.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Reproducibility"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.anaconda.com/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Colab Notebooks", "Description": "A tool to execute and share reproducible code snippets.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Reproducibility"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://colab.research.google.com/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Docker", "Description": "An environment and dependency management tool.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Reproducibility"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://docker-curriculum.com/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Jupyter Notebooks", "Description": "A tool to execute and share reproducible code snippets.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Reproducibility"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://jupyter.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Semver", "Description": "A widely used protcol for versioning to software, to ensure easy reproducibility.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Reproducibility"], "Date": "", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://semver.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Reforms", "Description": "Reporting Standards for ML-based Science.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Reproducibility"], "Date": "8-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2308.07832", "Website Link": "https://reforms.cs.princeton.edu/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "A Retrospective Datasheet for BookCorpus", "Description": "A third party datasheet for BookCorpus", "Modalities": ["Text"], "Categories": ["Data Auditing"], "Date": "5-2021", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2105.05241", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Provenance Initiative", "Description": "A large scale audit of 2000+ popular datasets in AI.", "Modalities": ["Text"], "Categories": ["Data Auditing"], "Date": "Frequently Updated", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2310.16787", "Website Link": "https://www.dataprovenance.org/", "GitHub Link": "https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection", "HuggingFace Link": "https://huggingface.co/DataProvenanceInitiative", "Added By": "Original Authors"} -{"Name": "Datasheet for the Pile", "Description": "A datasheet for the Pile", "Modalities": ["Text"], "Categories": ["Data Auditing"], "Date": "1-2022", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2201.07311", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "HaveIBeenTrained", "Description": "A combination search tool / opt out tool for LAION", "Modalities": ["Text", "Vision"], "Categories": ["Data Auditing", "Data Governance"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://haveibeentrained.com/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Into the LAIONs Den", "Description": "Auditing hateful content in text-to-vision datasets.", "Modalities": ["Text", "Vision"], "Categories": ["Data Auditing"], "Date": "9-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2311.03449", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Multimodal datasets: misogyny, pornography, and malignant stereotypes", "Description": "Auditing vision datasets for sensitive content.", "Modalities": ["Text", "Vision"], "Categories": ["Data Auditing"], "Date": "10-2021", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2110.01963", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "On Hate Scaling Laws For Data-Swamps", "Description": "Auditing text and vision datasets for systemic biases and hate.", "Modalities": ["Text", "Vision"], "Categories": ["Data Auditing"], "Date": "6-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2306.13141", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Quality at a Glance", "Description": "An audit of allegedly multilingual parallel text corpora.", "Modalities": ["Text"], "Categories": ["Data Auditing"], "Date": "3-2021", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2103.12028", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Training Data Transparency Blog", "Description": "A blog on transparency for training data in AI.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Auditing"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://huggingface.co/blog/yjernite/data-transparency", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Selection via Importance Resampling (DSIR)", "Description": "A tool for selecting data with a similar distribution to a target dataset", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "12-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2302.03169", "Website Link": "", "GitHub Link": "https://github.com/p-lambda/dsir", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "DataComp filtering", "Description": "Various quality filters", "Modalities": ["Text", "Vision"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "4-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2304.14108", "Website Link": "https://www.datacomp.ai/", "GitHub Link": "https://github.com/mlfoundations/datacomp/tree/main#baselines", "HuggingFace Link": "https://huggingface.co/datasets/mlfoundations/datacomp_1b", "Added By": "Original Authors"} -{"Name": "DataComp pre-filtering", "Description": "NSFW detection, dedup with eval datasets", "Modalities": ["Text", "Vision"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "4-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2304.14108", "Website Link": "https://www.datacomp.ai/", "GitHub Link": "https://github.com/mlfoundations/dataset2metadata", "HuggingFace Link": "https://huggingface.co/datasets/mlfoundations/datacomp_1b", "Added By": "Original Authors"} -{"Name": "Detoxify", "Description": "A python library designed to identify toxic language in comments. Functions in seven languages: English, Italian, French, Russian, Portuguese, Spanish, Turking.", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/unitaryai/detoxify", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Dolma's Toolkit", "Description": "A Python framework for defining Taggers that identify non-language text, language ID, PII, toxic text, and \"quality\" text. Includes reimplementation of heuristics used by Gopher and C4 for non-natural language.", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "8-2023", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/allenai/dolma", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "DoReMi", "Description": "A github repository for Domain Reweighting with Minimax Optimization", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "5-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2305.10429", "Website Link": "", "GitHub Link": "https://github.com/sangmichaelxie/doremi", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "fastText language classifier", "Description": "A tool for classifying the language of text", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "5-2023", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/facebook/fasttext-language-identification", "Added By": "Original Authors"} -{"Name": "FUN-LangID", "Description": "Frequently Used N-grams Language ID model, a character 4-gram model trained to recognize up to 1633 languages.", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "9-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/google-research/url-nlp/tree/main/fun-langid", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "GlotLID", "Description": "A model for identifying languages, with support for more than 1600 languages.", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2310.16248", "Website Link": "", "GitHub Link": "https://github.com/cisnlp/GlotLID", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Langdetect", "Description": "A tool to predict the language of text, used to filter out/in data from the desired languages", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "5-2021", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://pypi.org/project/langdetect/", "GitHub Link": "https://github.com/Mimino666/langdetect", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Lilac", "Description": "A python package for better understanding your data. Includes keyword and semantic search, as well as detection for PII, duplicates, and language.", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "9-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://www.lilacml.com/", "GitHub Link": "https://github.com/lilacai/lilac", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Online Data Mixing", "Description": "A github repository for efficient online data mixing", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "12-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2312.02406", "Website Link": "", "GitHub Link": "https://github.com/alon-albalak/online-data-mixing", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "OpenLID", "Description": "A model (and data used to train the model) for identifying 200+ languages.", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2305.13820", "Website Link": "", "GitHub Link": "https://github.com/laurieburchell/open-lid-dataset", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Roots data cleaning pipeline", "Description": "A pipeline for processing and improving quality of crowdsourced datasets", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "10-2022", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/01a_catalogue_cleaning_and_filtering", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "SpeechBrain’s Spoken language ID model", "Description": "Pre-trained spoken language identification model trained on VoxLingua107, dataset of audio sourced from YouTube for 107 languages", "Modalities": ["Speech"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "6-2021", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2106.04624", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/speechbrain/lang-id-voxlingua107-ecapa", "Added By": "Original Authors"} -{"Name": "The Pile processing scripts", "Description": "A series of scripts to replicate the Pile dataset. Includes filtering and cleaning for: language, profanity, deduplication, and test set decontamination.", "Modalities": ["Text"], "Categories": ["Data Cleaning, Filtering, & Mixing"], "Date": "12-2020", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/EleutherAI/the-pile/tree/master/processing_scripts", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BigBench Canaries", "Description": "BigBench's \"Training on the Test Set\" Task provies guidance on using canaries to check if an evaluation set was trained on.", "Modalities": ["Text"], "Categories": ["Data Decontamination"], "Date": "10-2021", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/training_on_test_set/README.md#training-on-the-test-set", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Carper AI Decontamination Tool", "Description": "A repository, heavily based by the BigCode repository, to decontaminate evaluation sets from a text training set.", "Modalities": ["Text"], "Categories": ["Data Decontamination"], "Date": "1-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/CarperAI/decontamination/tree/main", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Portraits", "Description": "A tool to test for membership inference of popular datasets, like The Pile or The Stack, i.e. whether a model has seen certain data.", "Modalities": ["Text"], "Categories": ["Data Decontamination"], "Date": "3-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2303.03919", "Website Link": "https://dataportraits.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Detect Pretrain Data (Min-K Prob)", "Description": "Detect Pretrain Data (Min-K Prob)", "Modalities": ["Text"], "Categories": ["Data Decontamination"], "Date": "11-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2310.16789", "Website Link": "https://swj0419.github.io/detect-pretrain.github.io/", "GitHub Link": "https://github.com/swj0419/detect-pretrain-code", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Interpreting Canary Exposure", "Description": "An explanation on how to interpret canary exposure, including by relating it to membership inference attacks, and differential privacy.", "Modalities": ["Text"], "Categories": ["Data Decontamination"], "Date": "5-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2306.00133", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Proving Test Set Contamination in Black Box Language Models", "Description": "A paper that provides methods for provable guarantees of test set contamination in language models without access to pretraining data or model weights.", "Modalities": ["Text"], "Categories": ["Data Decontamination"], "Date": "10-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2310.17623", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Apricot", "Description": "apricot implements submodular optimization for the purpose of summarizing massive data sets into minimally redundant subsets that are still representative of the original data. These subsets are useful for both visualizing the modalities in the data (such as in the two data sets below) and for training accurate machine learning models with just a fraction of the examples and compute.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Deduplication"], "Date": "7-1905", "Primary Link": "GitHub", "Paper Link": "https://dl.acm.org/doi/abs/10.5555/3455716.3455877", "Website Link": "", "GitHub Link": "https://github.com/jmschrei/apricot", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Datacomp image dedup", "Description": "Data to deduplicate vision datasets for the Datacomp challenge.", "Modalities": ["Vision"], "Categories": ["Data Deduplication"], "Date": "8-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://www.datacomp.ai/", "GitHub Link": "https://github.com/mlfoundations/dataset2metadata", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Dolma Dedupe Tool", "Description": "Dolma's text deduplication tool for pretraining data", "Modalities": ["Text"], "Categories": ["Data Deduplication"], "Date": "10-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/allenai/dolma", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Google Text Deduplication", "Description": "A repository to deduplicate language model datasets. They release the ExactSubstr deduplication implementation (written in Rust) along with scripts to perform ExactSubstr deduplication and inspect the results (written in Python). They also release the document clusters resulting from running NearDup deduplication on C4, RealNews, LM1B, and Wiki-4B-en.", "Modalities": ["Text"], "Categories": ["Data Deduplication"], "Date": "7-2021", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2107.06499", "Website Link": "", "GitHub Link": "https://github.com/google-research/deduplicate-text-datasets", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "RedPajama-Data", "Description": "Tools for: exact deduplication with bloom filter, fuzzy deduplication with LSH, calculating quality scores", "Modalities": ["Text"], "Categories": ["Data Deduplication"], "Date": "10-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/togethercomputer/RedPajama-Data", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Pile", "Description": "A set of tools for deduplication with MinHashLSH", "Modalities": ["Text"], "Categories": ["Data Deduplication"], "Date": "5-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2101.00027", "Website Link": "", "GitHub Link": "https://huggingface.co/datasets/EleutherAI/pile-standard-pythia-preshuffled", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Cards Playbook", "Description": "A tool to create a Data Card that thoroughly documents a new dataset.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Documentation"], "Date": "6-2022", "Primary Link": "Webpage", "Paper Link": "https://dl.acm.org/doi/fullHtml/10.1145/3531146.3533231", "Website Link": "https://sites.research.google/datacardsplaybook/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Nutrition Labels", "Description": "A generic but thorough form of dataset documentation.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Documentation"], "Date": "2020", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/1805.03677", "Website Link": "https://datanutrition.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Provenance Attribution Card", "Description": "A repository to select datasets and generate a summary. It can also generate a bibtex to attribute all developers of the datasets.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Documentation"], "Date": "10-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2310.16787", "Website Link": "https://www.dataprovenance.org/", "GitHub Link": "https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Statements", "Description": "A data statement to thoroughly document a new dataset.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Documentation"], "Date": "2018", "Primary Link": "Paper", "Paper Link": "https://aclanthology.org/Q18-1041/", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Datasheets for Datasets", "Description": "A datasheet to thoroughly document a new dataset.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Documentation"], "Date": "3-2018", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/1803.09010", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Datasheets for Digital Cultural Heritage Datasets", "Description": "A datasheet specifically designed for digital cultural heritage datasets and their considerations.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Documentation"], "Date": "2023", "Primary Link": "Paper", "Paper Link": "https://cris.unibo.it/handle/11585/947893", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Governance in the Age of Large-Scale Data-Driven Language Technology", "Description": "A paper detailing the data governance decisions undertaken during BigScience's BLOOM project. ", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Governance"], "Date": "5-2022", "Primary Link": "Paper", "Paper Link": "https://dl.acm.org/doi/abs/10.1145/3531146.3534637", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/spaces/bigscience-data/roots-search", "Added By": "Original Authors"} -{"Name": "Reclaiming the Digital Commons: A Public Data Trust for Training Data", "Description": "A paper that argues for the creation of a public data trust for collective input into the creation of AI systems and analyzes the feasibility of such a data trust.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Governance"], "Date": "3-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2303.09001", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BigCode Governance Card", "Description": "A report outlining governance questions, approaches, and tooling in the BigCode project, with a focus on Data governance", "Modalities": ["Text"], "Categories": ["Data Governance"], "Date": "11-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2312.03872", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "AmIinTheStack", "Description": "A tool to let software developers check whether their code was included in TheStack dataset and opt out of inclusion in future versions", "Modalities": ["Text"], "Categories": ["Data Governance"], "Date": "9-2022", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/spaces/bigcode/in-the-stack", "Added By": "Original Authors"} -{"Name": "StarPII: BigCode Pseudonymization Model", "Description": "A model trained on a new dataset of PII in code used for pseudonymization of a dataset prior to training", "Modalities": ["Text"], "Categories": ["Data Governance"], "Date": "4-2023", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/bigcode/starpii", "Added By": "Original Authors"} -{"Name": "French DPA Resource sheets on AI and GDPR", "Description": "A set of resource sheets focused on GDPR compliance covering legal basis for data collection, sharing, and best practices for handling personal data", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Data Governance"], "Date": "10-2023", "Primary Link": "Webpage", "Paper Link": "https://www.cnil.fr/en/ai-how-sheets", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "AI2 C4 Search Tool", "Description": "A search tool that lets users to execute full-text queries to search Google's C4 Dataset.", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "7-1905", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://c4-search.apps.allenai.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Finder", "Description": "A tool to help build search over academic datasets given a natural language description of the idea.", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "5-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2305.16636", "Website Link": "", "GitHub Link": "https://github.com/viswavi/datafinder", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Data Provenance Explorer", "Description": "An explorer tool for selecting, filtering, and visualizing popular finetuning, instruction, and alignment training datasets from Hugging Face, based on their metadata such as source, license, languages, tasks, topics, among other properties.", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2310.16787", "Website Link": "https://www.dataprovenance.org/", "GitHub Link": "https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection", "HuggingFace Link": "https://huggingface.co/DataProvenanceInitiative", "Added By": "Original Authors"} -{"Name": "GAIA Search Tool", "Description": "A search tool over C4, the Pile, ROOTS, and the text captions of LAION, developed with Pyserini (https://github.com/castorini/pyserini).", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "6-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2306.01481", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/spaces/spacerini/gaia", "Added By": "Original Authors"} -{"Name": "Hugging Face Data Measurements Tool", "Description": "A tool to analyze, measure, and compare properties of text finetuning data, including their distributional statistics, lengths, and vocabularies.", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "7-1905", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/spaces/huggingface/data-measurements-tool", "Added By": "Original Authors"} -{"Name": "Know your data", "Description": "A tool for exploring over 70 vision datasets", "Modalities": ["Vision"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "5-2021", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://knowyourdata-tfds.withgoogle.com/", "GitHub Link": "https://github.com/PAIR-code/knowyourdata", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "LAION search", "Description": "Nearest neighbor search based on CLIP embeddings", "Modalities": ["Text", "Vision"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "3-2022", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://rom1504.github.io/clip-retrieval/", "GitHub Link": "https://github.com/rom1504/clip-retrieval", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "NVIDIA Speech Data Explorer", "Description": "Tool for exploring speech data", "Modalities": ["Speech"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/tools/speech_data_explorer.html", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "ROOTS Search Tool", "Description": "A tool, based on a BM25 index, to search over text for each language or group of languages included in the ROOTS pretraining dataset.", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "7-1905", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/spaces/bigscience-data/roots-search", "Added By": "Original Authors"} -{"Name": "What's In My Big Data?", "Description": "A platform for analyzing large text datasets at scale", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "10-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2310.20707", "Website Link": "https://wimbd.apps.allenai.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "WIMBD", "Description": "A dataset analysis tool to count, search, and compare attributes across several massive pretraining corpora at scale, including C4, The Pile, and RedPajama.", "Modalities": ["Text"], "Categories": ["Data Search, Analysis, & Exploration"], "Date": "11-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2310.20707", "Website Link": "https://wimbd.apps.allenai.org/", "GitHub Link": "https://github.com/allenai/wimbd", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Everything about Distributed Training and Efficient Finetuning", "Description": "A rundown and crash course in distributed training for deep learning, with an eye toward LLM finetuning and current useful tools and resources. Provides a good overview of the various (distributed) training strategies for efficient and scalable training.", "Modalities": ["Text"], "Categories": ["Model Training: Educational Resources"], "Date": "10-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://sumanthrh.com/post/distributed-and-efficient-finetuning/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Machine Learning Engineering Online Book", "Description": "An \"online textbook\" and resource collection on ML engineering at scale, ranging from debugging distributed systems, parallelism strategies, effective use of large HPC clusters, and chronicles of past large-scale training runs with lessons learned.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Training: Educational Resources"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/stas00/ml-engineering", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "nanoGPT", "Description": "A minimal, stripped-down training codebase for teaching purposes and easily-hackable yet performant small-scale training.", "Modalities": ["Text"], "Categories": ["Model Training: Educational Resources"], "Date": "12-2022", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/karpathy/nanoGPT", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The EleutherAI Model Training Cookbook", "Description": "A set of resources on how to train large scale AI systems", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Training: Educational Resources"], "Date": "12-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/EleutherAI/cookbook", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Transformer Inference Arithmetic", "Description": "A blog post on the inference costs of transformer-based LMs. Useful for providing more insight into deep learning accelerators and inference-relevant decisions to make when training a model.", "Modalities": ["Text"], "Categories": ["Model Training: Educational Resources"], "Date": "3-2022", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://kipp.ly/transformer-inference-arithmetic/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Transformer Math 101", "Description": "An introductory blog post on training costs of LLMs, going over useful formulas and considerations from a high to low level", "Modalities": ["Text"], "Categories": ["Model Training: Educational Resources"], "Date": "4-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://blog.eleuther.ai/transformer-math/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Azure Emissions Impact Dashboard", "Description": "Monitoring the environmental impact of training machine learning models on Azure", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Environmental Impact"], "Date": "10-2021", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.microsoft.com/en-us/sustainability/emissions-impact-dashboard", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Carbontracker", "Description": "carbontracker is a tool for tracking and predicting the energy consumption and carbon footprint of training deep learning models as described in Anthony et al. (2020).", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Environmental Impact"], "Date": "7-2020", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2007.03051", "Website Link": "", "GitHub Link": "https://github.com/lfwa/carbontracker", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "CodeCarbon", "Description": "Estimate and track carbon emissions from your computer, quantify and analyze their impact.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Environmental Impact"], "Date": "11-2020", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://mlco2.github.io/codecarbon/", "GitHub Link": "https://github.com/mlco2/codecarbon", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model", "Description": "A comprehensive account of the broader environmental impact of the BLOOM language model.", "Modalities": ["Text"], "Categories": ["Environmental Impact"], "Date": "6-2023", "Primary Link": "Paper", "Paper Link": "https://jmlr.org/papers/v24/23-0069.html", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Experiment Impact Tracker", "Description": "The experiment-impact-tracker is meant to be a simple drop-in method to track energy usage, carbon emissions, and compute utilization of your system. Currently, on Linux systems with Intel chips (that support the RAPL or powergadget interfaces) and NVIDIA GPUs, we record: power draw from CPU and GPU, hardware information, python package versions, estimated carbon emissions information, etc. In California we even support realtime carbon emission information by querying caiso.com!", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Environmental Impact"], "Date": "1-2020", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2002.05651", "Website Link": "", "GitHub Link": "https://github.com/Breakend/experiment-impact-tracker", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Google Cloud Carbon Footprint Measurement", "Description": "Tracking the emissions of using Google's cloud compute resources", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Environmental Impact"], "Date": "10-2021", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://cloud.google.com/carbon-footprint?hl=en", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Making AI Less \"Thirsty\"", "Description": "Uncovering and Addressing the Secret Water Footprint of AI Models, and estimating water usage for training and deploying LLMs.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Environmental Impact"], "Date": "4-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2304.03271", "Website Link": "", "GitHub Link": "https://github.com/Ren-Research/Making-AI-Less-Thirsty", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "ML CO2 Impact", "Description": "A tool for estimating carbon impacts of ML training", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Environmental Impact"], "Date": "10-2019", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/1910.09700", "Website Link": "https://mlco2.github.io/impact/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Scaling Laws for Neural Language Models", "Description": "Provide scaling laws to determine the optimal allocation of a fixed compute budget.", "Modalities": ["Text"], "Categories": ["Environmental Impact", "Model Training: Efficiency & Resource Allocation"], "Date": "1-2020", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2001.08361", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Training Compute-Optimal Large Language Models", "Description": "Provides details on the optimal model size and number of tokens for training a transformer-based language model in a given computational budget.", "Modalities": ["Text"], "Categories": ["Environmental Impact"], "Date": "3-2022", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2203.15556", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "AI4Bhārat 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Link": "", "Added By": "Original Authors"} -{"Name": "Lanfrica", "Description": "An online catalogue that provides links to African language resources (papers and datasets) in both texts and speech", "Modalities": ["Text", "Speech"], "Categories": ["Finetuning Data Catalogs"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://lanfrica.com/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Masakhane NLP", "Description": "A repository of African language text and speech resources, including datasets.", "Modalities": ["Text", "Speech"], "Categories": ["Finetuning Data Catalogs"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://www.masakhane.io/", "GitHub Link": "https://github.com/masakhane-io", "HuggingFace Link": "https://huggingface.co/masakhane", "Added By": "Original Authors"} -{"Name": "MS COCO", "Description": "Object detection, segmentation, 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"https://github.com/OpenAccess-AI-Collective/axolotl", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BLIP-2", "Description": "Fine-tuned LLMs on multimodal data using a projection layer", "Modalities": ["Text", "Vision"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "1-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2301.12597", "Website Link": "", "GitHub Link": "https://github.com/salesforce/LAVIS/tree/main/projects/blip2", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "LLaMA-Adapter", "Description": "Fine-tuned LLMs on multimodal data using adapters", "Modalities": ["Text", "Vision"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "3-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2304.15010", "Website Link": "", "GitHub Link": "https://github.com/OpenGVLab/LLaMA-Adapter", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "LLaMA-Factory", "Description": "A framework for efficiently fine-tuning LLMs using cutting-edge algorithms with a user-friendly web UI", "Modalities": ["Text"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2403.13372", "Website Link": "", "GitHub Link": "https://github.com/hiyouga/LLaMA-Factory", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "LLaVA", "Description": "Fine-tuned LLMs on multimodal data using a projection layer", "Modalities": ["Text", "Vision"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "4-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2310.03744", "Website Link": "https://llava-vl.github.io/", "GitHub Link": "https://github.com/haotian-liu/LLaVA", "HuggingFace Link": "https://huggingface.co/spaces/badayvedat/LLaVA", "Added By": "Original Authors"} -{"Name": "MiniGPT4", "Description": "Fine-tuned LLMs on 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architecture", "Modalities": ["Text", "Vision"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "4-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2311.04219", "Website Link": "", "GitHub Link": "https://github.com/Luodian/Otter", "HuggingFace Link": "https://huggingface.co/spaces/Otter-AI/OtterHD-Demo", "Added By": "Original Authors"} -{"Name": "peft", "Description": "A library for doing parameter efficient finetuning", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/huggingface/peft", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "trl", "Description": "A library for doing RLHF on LLMs.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/huggingface/trl", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "trlX", "Description": "A library for doing RLHF on LLMs.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Training: Finetuning Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "https://aclanthology.org/2023.emnlp-main.530/", "Website Link": "https://trlx.readthedocs.io/en/latest/", "GitHub Link": "https://github.com/CarperAI/trlx", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Levanter", "Description": "Levanter is a framework for training large language models (LLMs) and other foundation models that strives for legibility, scalability, and reproducibility:", "Modalities": ["Text"], "Categories": ["Model Training: Finetuning Repositories", "Model Training: Pretraining Repositories"], "Date": "6-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://crfm.stanford.edu/2023/06/16/levanter-1_0-release.html", "GitHub Link": "https://github.com/stanford-crfm/levanter", "HuggingFace Link": "https://huggingface.co/stanford-crfm", "Added By": "Original Authors"} -{"Name": "AI Licensing Can’t Balance β€œOpen” with β€œResponsible”", "Description": "A blog post by an IP lawyer arguing against responsible use licensing", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "7-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://katedowninglaw.com/2023/07/13/ai-licensing-cant-balance-open-with-responsible/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "AI Pubs Open RAIL-M License", "Description": "Template for a responsible AI model license where the model is intended for research use. Use restrictions relate to discrimination, transparency, and violating the law", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "3-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.licenses.ai/ai-pubs-open-railm-vz1", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "AI2 ImpACT-LR License", "Description": "License for low risk AI artifacts (data and models) that allows for distribution of the artifact and its derivatives. Use restrictions include weapons development and military surveillance", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "7-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://allenai.org/licenses/impact-lr", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "AI2 ImpACT-MR License", "Description": "License for medium risk AI artifacts (data and models) that does not allows for distribution of the artifact but does allow for distribution of its derivatives. Use restrictions include weapons development and military surveillance", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "7-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://allenai.org/licenses/impact-mr", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Apache 2.0 License", "Description": "The most common open-source license for model weights", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "1-2004", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.apache.org/licenses/LICENSE-2.0", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Behavioral Use Licensing for Responsible AI", "Description": "A paper that provides a theoretical framework for licenses inteded for open models with use restrictions", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "6-2022", "Primary Link": "Paper", "Paper Link": "https://dl.acm.org/doi/10.1145/3531146.3533143", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BigCode Open RAIL-M License", "Description": "Template for a responsible AI model license. Use restrictions include generation and dissemination of malware", "Modalities": ["Text"], "Categories": ["License Selection"], "Date": "5-2023", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement", "Added By": "Original Authors"} -{"Name": "BigScience Open RAIL-M License", "Description": "Template for a responsible AI model license. Use restrictions include defamation, disinformation, and discrimination", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "8-2022", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://static1.squarespace.com/static/5c2a6d5c45776e85d1482a7e/t/6308bb4bba3a2a045b72a4b0/1661516619868/BigScience+Open+RAIL-M+License.pdf", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Choose an open source license", "Description": "A guide for choosing among open source licenses that includes general selection criteria and explanations for software licenses", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://choosealicense.com/", "GitHub Link": "https://github.com/github/choosealicense.com/tree/gh-pages", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Create Commons License Chooser", "Description": "A guide for choosing among Creative Commons licenses with an explanation of how they function", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://chooser-beta.creativecommons.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Legal Playbook For Natural Language Processing Researchers", "Description": "This playbook is a legal research resource for various activities related to data gathering, data governance, and disposition of an AI model available as a public resource.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "7-1905", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://bigscience.huggingface.co/blog/legal-playbook-for-natural-language-processing-researchers", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Licensing is neither feasible nor effective for addressing AI risks", "Description": "Argues that licensing is not the correct way to address risks with AI systems", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "6-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.aisnakeoil.com/p/licensing-is-neither-feasible-nor", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Open RAIL-S License", "Description": "Template for a responsible AI source code license. Use restrictions relate to surveillance, synthetic media, healthcare and the criminal legal system", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "11-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.licenses.ai/source-code-license", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Primer on AI2 ImpACT Licenses", "Description": "A post by AI2 describing when and why an organization should use a specific ImpACT license", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "7-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://allenai.org/impact-license", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The Open Source Definition", "Description": "The definition of an \"open source\" license", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "2-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://opensource.org/osd/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The Turning Way, Licensing", "Description": "A guide to reproducible research and licensing", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://the-turing-way.netlify.app/reproducible-research/licensing", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "What is Free Software?", "Description": "A philosophical argument for why free software licenses are important", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["License Selection"], "Date": "2-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.gnu.org/philosophy/free-sw.en.html", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Ecosystem Cards", "Description": "Ecosystem Graphs centralize information about models and their impact in the broader ecosystem. ", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Documentation"], "Date": "3-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2303.15772", "Website Link": "https://hai.stanford.edu/news/ecosystem-graphs-social-footprint-foundation-models", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Foundation Model Transparency Index", "Description": "An index to measure the transparency of a foundation model with respect to its inputs, development, and downstream uses or policies.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Documentation"], "Date": "10-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2310.12941", "Website Link": "https://crfm.stanford.edu/fmti/", "GitHub Link": "https://github.com/stanford-crfm/fmti", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Model Card Resources", "Description": "A release of several resources surrounding model cards, including templates and tools for easy documentation creation, and how these are frequently used in practice.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Documentation"], "Date": "12-2022", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://huggingface.co/blog/model-cards", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Model Cards", "Description": "A standard for reporting and documenting machine learning models, for promoting and easing transparent and open model development or reporting.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Documentation"], "Date": "10-2018", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/1810.03993", "Website Link": "https://huggingface.co/spaces/huggingface/Model_Cards_Writing_Tool", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Hugging Face ML Research Release Toolkit ", "Description": "A new researcher guide to releasing model or data resources, documenting the research and Hugging Face objects.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Documentation"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://docs.google.com/document/d/1EOxyZ11piIIRLDlhofX8nfnU0mHCU-TZ3EU4tx5g9aE/edit#heading=h.8zrjwmlee7ge", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "C4", "Description": "An English, cleaned version of Common Crawl's web crawl corpus (https://commoncrawl.org).", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "4-2019", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/1910.10683", "Website Link": "https://commoncrawl.org", "GitHub Link": "https://github.com/google-research/text-to-text-transfer-transformer#c4", "HuggingFace Link": "https://huggingface.co/datasets/allenai/c4", "Added By": "Original Authors"} -{"Name": "Common Voice", "Description": "28k hours [as of 11/2023] of crowd-sourced read speech from 100+ languages", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "11-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://commonvoice.mozilla.org/en/datasets", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "CulturaX", "Description": "A pertaining dataset of 16T tokens, covering 167 languages, cleaned, deduplicated, and refined. Combines mC4 into 2020, with OSCAR project data up to 2023.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "9-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2309.09400", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/uonlp/CulturaX", "Added By": "Original Authors"} -{"Name": "DataComp-1B and CommonPool-13B", "Description": "A large pool of 13B image-text pairs from CommonCrawl and a curated 1B subset", "Modalities": ["Text", "Vision"], "Categories": ["Pretraining Data Sources"], "Date": "4-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2304.14108", "Website Link": "https://www.datacomp.ai/", "GitHub Link": "https://github.com/mlfoundations/datacomp", "HuggingFace Link": "https://huggingface.co/datasets/mlfoundations/datacomp_1b", "Added By": "Original Authors"} -{"Name": "Dolma", "Description": "A pretraining dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "8-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2402.00159", "Website Link": "", "GitHub Link": "https://github.com/allenai/dolma", "HuggingFace Link": "https://huggingface.co/datasets/allenai/dolma", "Added By": "Original Authors"} -{"Name": "GigaSpeech", "Description": "40k hours (10k transcribed) multi-domain English speech corpus", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "7-1905", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2106.06909", "Website Link": "", "GitHub Link": "https://github.com/SpeechColab/GigaSpeech", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Golos", "Description": "1,240 hours of crowd-sourced Russian speech", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "6-2021", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2106.10161", "Website Link": "https://www.openslr.org/114/", "GitHub Link": "https://github.com/sberdevices/golos", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "IndicCorp v2", "Description": "A multilingual pre-training corpus for 24 Indian languages", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "5-2023", "Primary Link": "GitHub", "Paper Link": "https://aclanthology.org/2023.acl-long.693/", "Website Link": "", "GitHub Link": "https://github.com/AI4Bharat/IndicBERT/tree/main#indiccorp-v2", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "IndicSUPERB", "Description": "1,684 hour crowd-sourced corpus of 12 Indian languages", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "8-2022", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2208.11761", "Website Link": "https://ai4bharat.iitm.ac.in/indicsuperb/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Libri-Light", "Description": "60k hour read English speech from LibriVox audiobooks", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "12-2019", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/1912.07875", "Website Link": "", "GitHub Link": "https://github.com/facebookresearch/libri-light", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "LibriSpeech", "Description": "960 hour read English speech from LibriVox audiobooks", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "7-1905", "Primary Link": "Webpage", "Paper Link": "http://www.danielpovey.com/files/2015_icassp_librispeech.pdf", "Website Link": "https://www.openslr.org/12/", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/librispeech_asr", "Added By": "Original Authors"} -{"Name": "MADLAD-400", "Description": "A manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "9-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2309.04662", "Website Link": "", "GitHub Link": "https://github.com/google-research/google-research/tree/master/madlad_400", "HuggingFace Link": "https://huggingface.co/datasets/allenai/MADLAD-400", "Added By": "Original Authors"} -{"Name": "mC4", "Description": "The fully multilingual, cleaned version of Common Crawl's web crawl corpus (https://commoncrawl.org).", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "4-2019", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/1910.10683", "Website Link": "https://commoncrawl.org", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/mc4", "Added By": "Original Authors"} -{"Name": "MMC4", "Description": "Interleaved image-text data from Common Crawl (570M images, 43B tokens)", "Modalities": ["Text", "Vision"], "Categories": ["Pretraining Data Sources"], "Date": "4-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2304.06939", "Website Link": "", "GitHub Link": "https://github.com/allenai/mmc4", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "OBELICS", "Description": "Interleaved image-text data from Common Crawl (353 M images, 115B tokens)", "Modalities": ["Text", "Vision"], "Categories": ["Pretraining Data Sources"], "Date": "6-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2306.16527", "Website Link": "https://huggingface.co/blog/idefics", "GitHub Link": "https://github.com/huggingface/OBELICS", "HuggingFace Link": "https://huggingface.co/datasets/HuggingFaceM4/OBELICS", "Added By": "Original Authors"} -{"Name": "OLC", "Description": "The Open License Corpus is a 228B token corpus of permissively-licensed, primarily English text data for pretraining.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "8-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2308.04430", "Website Link": "", "GitHub Link": "https://github.com/kernelmachine/silo-lm#download-data", "HuggingFace Link": "https://huggingface.co/datasets/kernelmachine/open-license-corpus", "Added By": "Original Authors"} -{"Name": "OpenWebMath", "Description": "A dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of 6.3 million documents containing a total of 14.7B tokens.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "10-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2310.06786", "Website Link": "", "GitHub Link": "https://github.com/keirp/OpenWebMath", "HuggingFace Link": "https://huggingface.co/datasets/open-web-math/open-web-math", "Added By": "Original Authors"} -{"Name": "OPUS", "Description": "The Open Parallel Corpus is a massive collection of translated text pairs from the web.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://opus.nlpl.eu/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "OSCAR", "Description": "The Open Super-large Crawled Aggregated coRpus provides web-based multilingual datasets across 166 languages.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "https://aclanthology.org/2022.wnut-1.23/", "Website Link": "https://oscar-project.org/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "peS2o", "Description": "A collection of ~40M creative open-access academic papers, cleaned, filtered, and formatted for pre-training of language models, originally derived from the Semantic Scholar Open Research Corpus (S2ORC).", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "1-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/1911.02782", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/allenai/peS2o", "Added By": "Original Authors"} -{"Name": "Pile of Law", "Description": "An open-source, English dataset with ∼256GB of legal and administrative data, covering court opinions, contracts, administrative rules, and legislative records.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "11-2022", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2207.00220", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/pile-of-law/pile-of-law", "Added By": "Original Authors"} -{"Name": "RedPajama v2", "Description": "A pretraining dataset of 30 trillion filtered and deduplicated tokens (100+ trillions raw) from 84 CommonCrawl dumps covering 5 languages, along with 40+ pre-computed data quality annotations that can be used for further filtering and weighting.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "10-2023", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "https://www.together.ai/blog/redpajama-data-v2", "GitHub Link": "https://github.com/togethercomputer/RedPajama-Data", "HuggingFace Link": "https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2", "Added By": "Original Authors"} -{"Name": "ROOTS", "Description": "A massive multilingual pretraining corpus from BigScience, comprised of 1.6TB of text spanning 59 languages. It is a mix of OSCAR (https://oscar-project.org/) and the datasets found in the BigScience Catalogue (https://huggingface.co/spaces/bigscience/SourcingCatalog).", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "5-2022", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2303.03915", "Website Link": "https://bigscience.huggingface.co/", "GitHub Link": "https://github.com/bigscience-workshop/bigscience/tree/master/data", "HuggingFace Link": "https://huggingface.co/bigscience-data", "Added By": "Original Authors"} -{"Name": "SamrΓ³mur", "Description": "2,200 hour crowd-sourced corpus of Icelandic speech", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "7-1905", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.openslr.org/128/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Shrutilipi", "Description": "6,400 hour corpus of TV/Radio broadcasts from 12 Indian languages", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "8-2022", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2208.12666", "Website Link": "https://ai4bharat.iitm.ac.in/shrutilipi/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The People’s Speech", "Description": "30k hour conversational English dataset", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "11-2021", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2111.09344", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/MLCommons/peoples_speech", "Added By": "Original Authors"} -{"Name": "The Pile", "Description": "An 825GB English pretraining corpus that mixes portions of common crawl with 22 smaller, high-quality datasets combined together.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "12-2020", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2101.00027", "Website Link": "https://pile.eleuther.ai/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The Proof Pile 2", "Description": "The Proof-Pile-2 is a 55 billion token dataset of mathematical and scientific documents.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "9-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2310.10631", "Website Link": "https://blog.eleuther.ai/llemma/", "GitHub Link": "https://github.com/EleutherAI/math-lm", "HuggingFace Link": "https://huggingface.co/datasets/EleutherAI/proof-pile-2", "Added By": "Original Authors"} -{"Name": "The RefinedWeb", "Description": "An English-only, web-only, deduplicated pretraining dataset of five trillion tokens.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "6-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2306.01116", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/tiiuae/falcon-refinedweb", "Added By": "Original Authors"} -{"Name": "The Stack", "Description": "The Stack is a 6TB, permissively-licensed pretraining dataset from active GitHub repositories covering 358 programming languages.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "11-2022", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2211.15533", "Website Link": "https://www.bigcode-project.org/docs/about/the-stack/#datasets-and-data-governance-tools-released-by-bigcode", "GitHub Link": "https://github.com/bigcode-project/bigcode-dataset", "HuggingFace Link": "https://huggingface.co/datasets/bigcode/the-stack", "Added By": "Original Authors"} -{"Name": "VoxPopuli", "Description": "400k hours of unlabelled speech from 23 languages of the European parliament", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "1-2021", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2101.00390", "Website Link": "", "GitHub Link": "https://github.com/facebookresearch/voxpopuli", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "WebVid-10M", "Description": "10M videos with captions", "Modalities": ["Text", "Vision"], "Categories": ["Pretraining Data Sources"], "Date": "4-2021", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2104.00650", "Website Link": "https://maxbain.com/webvid-dataset/", "GitHub Link": "https://github.com/m-bain/webvid", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "WenetSpeech", "Description": "22.4k hour multi-domain corpus of Mandarin", "Modalities": ["Speech"], "Categories": ["Pretraining Data Sources"], "Date": "10-2021", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2110.03370", "Website Link": "https://www.openslr.org/121/", "GitHub Link": "https://github.com/wenet-e2e/WenetSpeech", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "WURA", "Description": "A manually audited multilingual pre-training corpus (document-level dataset) for 16 African languages and four high-resource languages widely spoken in Africa (English, French, Arabic and Portuguese)", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "11-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://aclanthology.org/2023.emnlp-main.11/", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/castorini/wura", "Added By": "Original Authors"} -{"Name": "WebDatasets", "Description": "A dataset format for high-performance streaming of data. Especially useful for modalities other than language that are more I/O intensive for training', such as images, video, or audio.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Pretraining Data Sources"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/webdataset/webdataset", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Multi Legal Pile", "Description": "A large-scale multilingual legal dataset and superset of the Pile of Law, suited for pretraining language models. It spans over 24 languages and five legal text types.", "Modalities": ["Text"], "Categories": ["Pretraining Data Sources"], "Date": "6-2023", "Primary Link": "Hugging Face object", "Paper Link": "https://arxiv.org/abs/2306.02069", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/datasets/joelniklaus/Multi_Legal_Pile", "Added By": "Original Authors"} -{"Name": "GPT-NeoX", "Description": "A library for training large language models, built off Megatron-DeepSpeed and Megatron-LM with an easier user interface. Used at massive scale on a variety of clusters and hardware setups.", "Modalities": ["Text"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/EleutherAI/gpt-neox", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Kosmos-2", "Description": "For training multimodal models with CLIP backbones.", "Modalities": ["Text", "Vision"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "6-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2306.14824", "Website Link": "", "GitHub Link": "https://github.com/microsoft/unilm/tree/master/kosmos-2", "HuggingFace Link": "https://huggingface.co/spaces/ydshieh/Kosmos-2", "Added By": "Original Authors"} -{"Name": "Lhotse", "Description": "Python library for handling speech data in machine learning projects", "Modalities": ["Speech"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "10-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://github.com/lhotse-speech/lhotse", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Megatron-DeepSpeed", "Description": "A library for training large language models, built off of Megatron-LM but extended by Microsoft to support features of their DeepSpeed library.", "Modalities": ["Text"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/microsoft/Megatron-DeepSpeed", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Megatron-LM", "Description": "One of the earliest open-source pretraining codebases for large language models. Still updated and has been used for a number of landmark distributed training and parallelism research papers by NVIDIA.", "Modalities": ["Text"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/NVIDIA/Megatron-LM", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "OpenCLIP", "Description": "Supports training and inference for over 100 CLIP models", "Modalities": ["Text", "Vision"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "9-2021", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/mlfoundations/open_clip", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "OpenLM", "Description": "OpenLM is a minimal language modeling repository, aimed to facilitate research on medium sized LMs. They have verified the performance of OpenLM up to 7B parameters and 256 GPUs. They only depend only on PyTorch, XFormers, or Triton.", "Modalities": ["Text"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/mlfoundations/open_lm", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Pytorch Image Models (timm)", "Description": "Hub for models, scripts and pre-trained weights for image classification models.", "Modalities": ["Vision"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "5-2019", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/huggingface/pytorch-image-models", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Stable Audio Tools", "Description": "A codebase for distributed training of generative audio models.", "Modalities": ["Speech"], "Categories": ["Model Training: Pretraining Repositories"], "Date": "Frequently Updated", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "", "GitHub Link": "https://github.com/Stability-AI/stable-audio-tools", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Bias Benchmark for QA (BBQ)", "Description": "A dataset of question-sets constructed by the authors that highlight attested social biases against people belonging to protected classes along nine different social dimensions relevant for U.S. English-speaking contexts.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "10-2021", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2110.08193", "Website Link": "", "GitHub Link": "https://github.com/nyu-mll/BBQ", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Crossmodal-3600", "Description": "Image captioning evaluation with geographically diverse images in 36 languages", "Modalities": ["Text", "Vision"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "5-2022", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2205.12522", "Website Link": "", "GitHub Link": "https://google.github.io/crossmodal-3600/", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "FactualityPrompt", "Description": "A benchmark to measure factuality in language models.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "6-2022", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2206.04624", "Website Link": "", "GitHub Link": "https://github.com/nayeon7lee/FactualityPrompt", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "From text to talk", "Description": "Harnessing conversational corpora for humane and diversity-aware language technology. They show how interactional data from 63 languages (26 families) harbours insights about turn-taking, timing, sequential structure and social action.", "Modalities": ["Speech"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "5-2022", "Primary Link": "Paper", "Paper Link": "https://aclanthology.org/2022.acl-long.385/ ", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Hallucinations", "Description": "Public LLM leaderboard computed using Vectara's Hallucination Evaluation Model. This evaluates how often an LLM introduces hallucinations when summarizing a document. ", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "10-2023", "Primary Link": "GitHub", "Paper Link": "", "Website Link": "https://github.com/vectara/hallucination-leaderboard", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/vectara/hallucination_evaluation_model", "Added By": "Original Authors"} -{"Name": "HolisticBias", "Description": "A bias and toxicity benchmark using templated sentences, covering nearly 600 descriptor terms across 13 different demographic axes, for a total of 450k examples", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "10-2022", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2205.09209", "Website Link": "https://ai.meta.com/research/publications/im-sorry-to-hear-that-finding-new-biases-in-language-models-with-a-holistic-descriptor-dataset/", "GitHub Link": "https://github.com/facebookresearch/ResponsibleNLP/tree/main/holistic_bias", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Purple Llama CyberSecEval", "Description": "A benchmark for coding assistants, measuring their propensity to generate insecure code and level of compliance when asked to assist in cyberattacks.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://ai.meta.com/research/publications/purple-llama-cyberseceval-a-benchmark-for-evaluating-the-cybersecurity-risks-of-large-language-models/", "GitHub Link": "https://github.com/facebookresearch/PurpleLlama/tree/main/CybersecurityBenchmarks", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Purple Llama Guard", "Description": "A tool to identify and protect against malicious inputs to LLMs.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "https://arxiv.org/abs/2312.06674", "Website Link": "https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/", "GitHub Link": "https://github.com/facebookresearch/PurpleLlama/tree/main/Llama-Guard", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Racial disparities in automated speech recognition", "Description": "A discussion of racial disparities and inclusiveness in automated speech recognition.", "Modalities": ["Speech"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "3-2020", "Primary Link": "Paper", "Paper Link": "", "Website Link": "https://www.pnas.org/doi/10.1073/pnas.1915768117", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "RealToxicityPrompts", "Description": "A dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "9-2020", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2009.11462", "Website Link": "https://toxicdegeneration.allenai.org/", "GitHub Link": "https://github.com/allenai/real-toxicity-prompts", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Red Teaming LMs with LMs", "Description": "A method for using one language model to automatically find cases where a target LM behaves in a harmful way, by generating test cases (\"red teaming\")", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "2-2022", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2202.03286", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Safety evaluation repository", "Description": "A repository of safety evaluations, across all modalities and harms, as of late 2023. Useful for delving deeper if the following evaluations don't meet your needs.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "10-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://dpmd.ai/46CPd58", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "SimpleSafetyTests", "Description": "Small probe set (100 English text prompts) covering severe harms: child abuse, suicide, self-harm and eating disorders, scams and fraud, illegal items, and physical harm", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "11-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2311.08370", "Website Link": "", "GitHub Link": "https://github.com/bertiev/SimpleSafetyTests", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "SneakyPrompt", "Description": "Automated jailbreaking method to generate NSFW content even with models that have filters applied", "Modalities": ["Vision"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "5-2023", "Primary Link": "GitHub", "Paper Link": "https://arxiv.org/abs/2305.12082", "Website Link": "", "GitHub Link": "https://github.com/Yuchen413/text2image_safety", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "StableBias", "Description": "Bias testing benchmark for Image to Text models, based on gender-occupation associations.", "Modalities": ["Vision"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "3-2023", "Primary Link": "Hugging Face object", "Paper Link": "", "Website Link": "https://arxiv.org/abs/2303.11408", "GitHub Link": "", "HuggingFace Link": "https://huggingface.co/spaces/society-ethics/StableBias", "Added By": "Original Authors"} -{"Name": "Cerebras Model Lab", "Description": "A calculator to apply compute-optimal scaling laws for a given budget, including factoring expected total inference usage.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Training: Efficiency & Resource Allocation"], "Date": "5-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://www.cerebras.net/model-lab/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "QLoRa", "Description": "An efficient finetuning approach that reduces memory usage while training.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Training: Efficiency & Resource Allocation"], "Date": "5-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2305.14314", "Website Link": "", "GitHub Link": "https://github.com/artidoro/qlora", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Scaling Data-Constrained Language Models", "Description": "Demonstrates an optimal allocation of compute when dataset size is bounded", "Modalities": ["Text"], "Categories": ["Model Training: Efficiency & Resource Allocation"], "Date": "5-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2305.16264", "Website Link": "", "GitHub Link": "https://github.com/huggingface/datablations", "HuggingFace Link": "https://huggingface.co/datablations", "Added By": "Original Authors"} -{"Name": "Training Compute-Optimal Language Models", "Description": "Proposes an optimal allocation of computational budget between model and dataset size, and shows experimental design for fitting scaling laws for compute allocation in a new setting.", "Modalities": ["Text"], "Categories": ["Model Training: Efficiency & Resource Allocation"], "Date": "3-2022", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2203.15556", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "AI Incident Database", "Description": "A database of harmful incidents tied to AI systems where developers or users can submit incident reports", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Usage Monitoring"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://incidentdatabase.ai/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BigScience Ethical Charter", "Description": "Outlines BigScience's core values and how they promote them, which in turn guides use restrictions and communicates acceptable usage to users", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Usage Monitoring"], "Date": "6-2022", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://bigscience.huggingface.co/blog/bigscience-ethical-charter", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Llama 2 Responsible Use Guide", "Description": "Guidance for downstream developers on how to responsibly build with Llama 2. Includes details on how to report issues and instructions related to red-teaming and RLHF", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Usage Monitoring"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://ai.meta.com/llama/responsible-use-guide/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Model Gating from Hugging Face", "Description": "A resource describing how to require user credentials for model access, which may be appropriate for models trained for topics such as hate speech", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Usage Monitoring"], "Date": "Frequently Updated", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://huggingface.co/docs/hub/models-gated", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Model Monitoring in Practice Tutorial", "Description": "A tutorial given at FAccT and other venues describing how and why to monitor ML models. Includes a presentation on using transformer models to monitor for error detection", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Usage Monitoring"], "Date": "6-2022", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://sites.google.com/view/model-monitoring-tutorial", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Robust Invisible Video Watermarking with Attention", "Description": "A widely used watermark for video models ", "Modalities": ["Vision"], "Categories": ["Usage Monitoring"], "Date": "9-2029", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/1909.01285", "Website Link": "", "GitHub Link": "https://github.com/DAI-Lab/RivaGAN", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Robust Distortion-free Watermarks for Language Models", "Description": "A watermark for autoregressive language models", "Modalities": ["Text"], "Categories": ["Usage Monitoring"], "Date": "7-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/abs/2307.15593", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "A Holistic Approach to Undesired Content Detection in the Real World", "Description": "Description of five primary categories (Sexual, Hateful, Violent, Self-harm, Harassment) with sub-categories (e.g. Sexual / sexual content involving minors). Also describes a moderation filter (the OpenAI moderation endpoint), and releases a dataset labelled for the categories.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "2-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2208.03274.pdf", "Website Link": "", "GitHub Link": "https://github.com/openai/moderation-api-release", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Perspective API", "Description": "Perspective API for content moderation. It has three classes of categories, each with 6+ attributes. (1) Production (Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, and Threats), (2) Experimental (Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, Threat, Sexually Explicit, and Flirtation), (3) NY Times (Attack on author, Attack on commenter, Incoherent, Inflammatory, Likely to Reject, Obscene, Spam, Unsubstantial).", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "8-2022", "Primary Link": "Paper", "Paper Link": "https://dl.acm.org/doi/pdf/10.1145/3534678.3539147", "Website Link": "https://developers.perspectiveapi.com/s/about-the-api-attributes-and-languages?language=en_US", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Mistral in-context self-reflection safety prompt", "Description": "Self-reflection prompt for use as a content moderation filter. It returns a binary value (safe/not) with 13 subcategories: Illegal, Child abuse, Hate Violence Harassment, Malware, Physical Harm, Economic Harm, Fraud, Adult, Political campaigning or lobbying, Privacy invasion, Unqualified law advice, Unqualified financial advice, Unqualified health advice", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "10-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2310.06825.pdf", "Website Link": "https://www.promptingguide.ai/models/mistral-7b", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Google, Gemini API Safety Filters (via Vertex)", "Description": "Safety filter for Gemini models, available through Vertex. 4 safety attributes are described: Hate speech, Harassment, Sexually Explicit, and Dangerous Content. Probabilities are returned for each attribute (Negligible, Low, Medium, High). ", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/configure-safety-attributes", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Google, PaLM API Safety Filters (via Vertex)", "Description": "Safety filter for PaLM models, available through Vertex. 16 safety attributes are described (some of which are 'topical' rather than purely safety risks): Derogatory, Toxic, Violent, Sexual, Insult, Profanity, Death Harm & Tragedy, Firearms & Weapons, Public safety, Health, Religion & belief, Illicit drugs, War & conflict, Politics, Finance, Legal.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "3-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://cloud.google.com/vertex-ai/docs/generative-ai/configure-safety-attributes-palm", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "ActiveFence's LLM Safety Review: Benchmarks and Analysis", "Description": "Description of 4 risk categories, as part of a benchmark review of LLM safety: (1) Hate, (2) Misinformation, (3) Self-harm & Suicide, (4) Child abuse & exploitation.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "07-2023", "Primary Link": "Paper", "Paper Link": "https://www.activefence.com/LLMSafety", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Anthropic content moderation prompt", "Description": "In-context prompt for assessing whether messages and responses contain inappropriate content: \"violent, illegal or pornographic activities\"", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://docs.anthropic.com/claude/docs/content-moderation", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", "Description": "Description of 20 risk areas, as part of red teaming Anthropics' models. Two of the tags are not interpretable (\"Other\" and \"N/A - Invalid attempt\"): Discrimination & justice, Hate speech & offensive language, Violence & incitement, Non-violent unethical behaviour (e.g. lying, cheating), Bullying & harassment, Other, Theft, N/A - Invalid attempt, Soliciting personally identifiable information, Conspiracy theories & misinformation, Substance abuse & banned substances, Fraud & deception, Weapons, Adult content, Property crime & vandalism, Animal abuse, Terrorism & organized crime, Sexual exploitation & human trafficking, Self-harm, Child abuse.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "10-2022", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2209.07858.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "BEAVERTAILS: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset", "Description": "Description of 14 risk areas, as part of a QA dataset for aligning models and evaluating their safety: Hate Speech, Offensive Language, Discrimination, Stereotype, Injustice, Violence, Aiding and Abetting, Incitement, Financial Crime, Property Crime, Theft, Privacy Violation, Drug Abuse, Weapons, Banned Substance, Non-Violent Unethical Behavior, Sexually Explicit, Adult Content, Controversial Topics, Politics, Misinformation Re. ethics, laws and safety, Terrorism, Organized Crime, Self-Harm, Animal Abuse, Child Abuse", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "10-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2307.04657.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Safety Assessment of Chinese Large Language Models", "Description": "Description of 8 risk areas (called \"safety scenarios)\": Insult, Unfairness and Discrimination, Crimes and Illegal Activities, Sensitive Topics, Physical Harm, Mental health, Privacy and Property, Ethics and Morality. Six \"instruction attacks\" are also described: Goal hijacking, Prompt leaking, RolePlay Instruction, Unsafe Instruction Topic, Inquiry with Unsafe Opinion, Reverse Exposure.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "4-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2304.10436.pdf", "Website Link": "", "GitHub Link": "https://github.com/thu-coai/Safety-Prompts", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "DECODINGTRUST: A Comprehensive Assessment of Trustworthiness in GPT Models", "Description": "Description of 8 evaluation areas: toxicity, stereotypes bias, adversarial robustness, out-of-distribution robustness, robustness against adversarial demonstrations, privacy, machine ethics, fairness.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "1-2024", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2306.11698.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "A Unified Typology of Harmful Content", "Description": "Taxonomy of harmful online content. There are 4 primary categories, which each have subcategories: (1) Hate and harassment (Doxxing, Identity attack, Identity misrepresentation, Insult, Sexual aggression, Threat of violence; (2) Self-inflicted harm (Eating disorder promotion, self-harm), (3) Ideological harm (Extremism Terrorism & Organized crime, Misinformation), (4) Exploitation (Adult sexual services, Child sexual abuse material, Scams).", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "Taxonomy of harmful online content. There are 4 primary categories, which each have subcategories: (1) Hate and harassment (Doxxing, Identity attack, Identity misrepresentation, Insult, Sexual aggression, Threat of violence; (2) Self-inflicted harm (Eating disorder promotion, self-harm), (3) Ideological harm (Extremism Terrorism & Organized crime, Misinformation), (4) Exploitation (Adult sexual services, Child sexual abuse material, Scams).", "Primary Link": "Paper", "Paper Link": "https://aclanthology.org/2020.alw-1.16.pdf", "Website Link": "https://docs.cohere.com/docs/content-moderation-with-classify", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements", "Description": "Description of 7 risk areas, as part of a survey on LLM risks: Toxicity and Abusive Content, Unfairness and Discrimination, Ethics and Morality Issues, Controversial Opinions, Misleading Information, Privacy and Data Leakage, Malicious Use and Unleashing AI Agents.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "11-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2302.09270.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Llama 2: Open Foundation and Fine-Tuned Chat Models", "Description": "Description of 3 risk areas, as part of the safety checks for releasing Llama2: (1) illicit and criminal activities (terrorism, theft, huam trafficking), (2) hateful and harmful activities (defamation, self-harm, eating disorders, discrimination), and (3) unqualified advice (medical, financial and legal advice). Other risk categories are described as part of red teaming and soliciting feedback.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "7-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2307.09288.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Ethical and social risks of harm from Language Models", "Description": "Two-tier taxonomy of risks, comprising both classification groups (of which there are 6) and associated harms (3 or 4 for each classification group). The classification groups are: (1) Discrimination, Exclusion and Toxicity, (2) Information Hazards, (3) Misinformation Harms, (4) Malicious Uses, (5) Human-Computer Interaction Harms, and (6) Automation, access, and environmental harms.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "12-2021", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2112.04359.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Sociotechnical Safety Evaluation of Generative AI Systems", "Description": "Two-tier taxonomy of risks, comprising both classification groups (of which there are 6) and associated harms (3 or 4 for each classification group). The classification groups are: (1) Representation and Toxicity Harms, (2) Misinformation Harms, (3) Information & Society Harms, (4) Malicious Use, (5) Human Autonomy & Integrity Harms, and (6) Socioeconomic & Environmental Harms.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "10-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2310.11986.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment", "Description": "Two-tier taxonomy of risks, with seven major categories of LLM trustworthiness, each of which has several associated sub-categories: (1) Reliability, (2) Safety, (3) Fairness, (4) Resistance to Misuse, (5) Explainability and Reasoning, (6) Social Norms, and (7) Robustness.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "8-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2308.05374.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets", "Description": "Description of 8 risk areas, as part of describing methods for aligning models: (1) Abuse, Violence and Threat (inclusive of self-harm), (2) Health (phyiscal and mental), (3) Human characteristics and behaviour, (4) Injustice and inequality (incl, discrimination, harmful stereotypes), (5) Political opinion and destabilization, (6) Relationships (romantic, familial friendships), (7) Sexual activity (inclusive of pornography), (8) Terrorism (inclusive of white supremacy).", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "6-2021", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2106.10328.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction", "Description": "Description of 5 categories of harm, with detailed subcategories: (1) Representational harms, (2) Allocative harms, (3) Quality of Service harms, (4) Interpersonal harms, and (5) Social system harms. ", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "7-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2210.05791.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks", "Description": "Taxonomy of 12 privacy risks, based on reviewing 321 privacy-related incidents, filtered from the AI, Algorithmic and Automation Incident and Controversy Repository (AIAAIC) Database. Risks are split into those that are created by AI (Identification, Distortion, Exposure, Aggregation, Phrenology/Physiognomy) and those that are exacerbated by AI (Intrusion, Surveillance, Exclusion, Secondary Use, Insecurity, Increased Accessibility).", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "10-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2310.07879.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The Ethical Implications of Generative Audio Models: A Systematic Literature Review", "Description": "Taxonomy of 12 \"negative broader impacts\" from generative models involving speech and music.", "Modalities": ["Speech"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "7-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2307.05527.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "An Overview of Catastrophic AI Risks", "Description": "Taxonomy of 4 catastrophic AI risks, with subcategories: (1) Malicious use (Bioterrrorism, Uncontrolled AI agents, AI capabilities for propaganda, Censorship and surveillance), (2) AI race (Autonomous weapons, Cyberwarfare, Automated human labour [mass unemployment and dependence on AI systems], (3) Organizational risks (AI accidentally leaked/stolen), (4) Rogue AIs (Proxy gaming, Goal drift, Power-seeking, Deception).", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "9-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2306.12001.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation", "Description": "Taxonomy of 3 AI security risks, with subcategories: (1) Digital Security, Physical Security, Political Security.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "2-2018", "Primary Link": "Paper", "Paper Link": "https://img1.wsimg.com/blobby/go/3d82daa4-97fe-4096-9c6b-376b92c619de/downloads/MaliciousUseofAI.pdf?ver=1553030594217", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Open-sourcing highly capable foundation models", "Description": "Description of risks from malicious use of AI: Influence operations, Surveillance and population control, Scamming and spear phishing, Cyber attacks, Biological and chemical weapons development. Some \"extreme risks\" are also described in the paper (e.g. disruption to key societal functions).", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "9-2023", "Primary Link": "Paper", "Paper Link": "https://cdn.governance.ai/Open-Sourcing_Highly_Capable_Foundation_Models_2023_GovAI.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "How Does Access Impact Risk? Assessing AI Foundation Model Risk Along a Gradient of Access ", "Description": "Description of risks from open-sourcing models, including five instances of malicious use: (1) Fraud and other crime schemes, (2) Undermining of social cohesion and democratic processes, (3) Human rights abuses, (4) Disruption of critical infrastructure, and (5) State conflict. ", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "12-2023", "Primary Link": "Paper", "Paper Link": "https://securityandtechnology.org/wp-content/uploads/2023/12/How-Does-Access-Impact-Risk-Assessing-AI-Foundation-Model-Risk-Along-A-Gradient-of-Access-Dec-2023.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "OpenAI Preparedness Framework (Beta)", "Description": "Description of 4 catastrophic AI risks: (1) Cybersecurity, (2) Chemical, Biological, Nuclear and Radiological (CBRN) threats, (3) Persuasion, and (4) Model autonomy. The paper also highlights the risk of \"unknown unknowns\".", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "12-2023", "Primary Link": "Paper", "Paper Link": "https://cdn.openai.com/openai-preparedness-framework-beta.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Anthropic's Responsible Scaling Policy", "Description": "Framework with four tiers of model capability, ffrom ASL-1 (smaller models) to ASL-4 (speculative), with increasing risk as models' capability increases. It also describes 4 catastrophic AI risks: (1) Misuse risks, (2) CBRN risks, (3) Cyber risks, and (4) Autonomy and replication risks.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "9-2023", "Primary Link": "Paper", "Paper Link": "https://www-files.anthropic.com/production/files/responsible-scaling-policy-1.0.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Model evaluation for extreme risks", "Description": "Framework of 9 dangerous capabilities of AI models: (1) Cyber-offense, (2) Deception, (3) Persuasion & manipulation, (4) Politial strategy, (5) Weapons acquisition, (6) Long-horizon planning, (7) AI development, (8) Situational awareness, (9) Self-proliferation.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "9-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2305.15324.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Frontier AI Regulation: Managing Emerging Risks to Public Safety", "Description": "Description of \"sufficiently dangerous capabilities\" of AI models to cause serious harm and disruption on a global scale, such as synthesing new biological or chemical weapons and evading human control through means of deception and obfuscation.", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "11-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2307.03718.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "The Fallacy of AI Functionality", "Description": "Taxonomy of four AI failure points: (1) Impossible tasks (either Conceptually impossible or Practically impossible), (2) Engineering failures (Design failures, Implementation failures, Missing Safety Features), (3) Post-Deployment Failures (Robustness Issues, Failure under Adversarial Attacks, Unanticipated Intractions, (4) Communication Failures (Falsified or Overstated Capabilities, Misrepresented Capabilities).", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "7-2022", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2206.09511.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI", "Description": "Framework of 3 potential harms from AI: (1) Harm to people (individual harm, Group/community harm, Societal harm), (2) Harm to an Organisation or Enterprise, (3) Harm to a system. ", "Modalities": ["Text", "Speech", "Vision"], "Categories": ["Model Evaluation: Risks & Harms Taxonomies"], "Date": "6-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2306.06924.pdf", "Website Link": "", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Cohere in-context content moderation prompt", "Description": "Few-shot prompt for classifying whether text is toxic or not.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "12-2023", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://docs.cohere.com/reference/toxicity-detection", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "NVidia NeMo Guardrails", "Description": "Open-source tooling to create guardrails for LLM applications.", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "4-2023", "Primary Link": "Paper", "Paper Link": "https://arxiv.org/pdf/2310.10501.pdf", "Website Link": "https://blogs.nvidia.com/blog/ai-chatbot-guardrails-nemo/", "GitHub Link": "https://github.com/NVIDIA/NeMo-Guardrails", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "SafetyPrompts", "Description": "Open repository of datasets for LLM safety", "Modalities": ["Text"], "Categories": ["Model Evaluation: Risks & Harms"], "Date": "1-2024", "Primary Link": "Webpage", "Paper Link": "", "Website Link": "https://safetyprompts.com/", "GitHub Link": "", "HuggingFace Link": "", "Added By": "Original Authors"} -{"Name": "Model Risk Cards", "Description": "A framework for structured assessment and documentation of risks associated with an application of language models. 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These subsets are useful for both visualizing the modalities in the data (such as in the two data sets below) and for training accurate machine learning models with just a fraction of the examples and compute.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Data Deduplication" + ], + "date": "7-1905", + "primary_link": "GitHub", + "paper_link": "https://dl.acm.org/doi/abs/10.5555/3455716.3455877", + "website_link": "", + "github_link": "https://github.com/jmschrei/apricot", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "jmschrei_logo.png" + }, + { + "name": "Datacomp image dedup", + "description": "Data to deduplicate vision datasets for the Datacomp challenge.", + "modalities": [ + "Vision" + ], + "categories": [ + "Data Deduplication" + ], + "date": "8-2023", + "primary_link": "GitHub", + "paper_link": "", + "website_link": "https://www.datacomp.ai/", + "github_link": "https://github.com/mlfoundations/dataset2metadata", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "mlfoundations_logo.png" + }, + { + "name": "Dolma Dedupe Tool", + "description": "Dolma's text deduplication tool for pretraining data", + "modalities": [ + "Text" + ], + "categories": [ + "Data Deduplication" + ], + "date": "10-2023", + "primary_link": "GitHub", + "paper_link": "", + "website_link": "", + "github_link": "https://github.com/allenai/dolma", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "allenai_logo.png" + }, + { + "name": "Google Text Deduplication", + "description": "A repository to deduplicate language model datasets. They release the ExactSubstr deduplication implementation (written in Rust) along with scripts to perform ExactSubstr deduplication and inspect the results (written in Python). They also release the document clusters resulting from running NearDup deduplication on C4, RealNews, LM1B, and Wiki-4B-en.", + "modalities": [ + "Text" + ], + "categories": [ + "Data Deduplication" + ], + "date": "7-2021", + "primary_link": "GitHub", + "paper_link": "https://arxiv.org/abs/2107.06499", + "website_link": "", + "github_link": "https://github.com/google-research/deduplicate-text-datasets", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "google-research_logo.png" + }, + { + "name": "RedPajama-Data", + "description": "Tools for: exact deduplication with bloom filter, fuzzy deduplication with LSH, calculating quality scores", + "modalities": [ + "Text" + ], + "categories": [ + "Data Deduplication" + ], + "date": "10-2023", + "primary_link": "GitHub", + "paper_link": "", + "website_link": "", + "github_link": "https://github.com/togethercomputer/RedPajama-Data", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "togethercomputer_logo.png" + }, + { + "name": "Pile", + "description": "A set of tools for deduplication with MinHashLSH", + "modalities": [ + "Text" + ], + "categories": [ + "Data Deduplication" + ], + "date": "5-2023", + "primary_link": "GitHub", + "paper_link": "https://arxiv.org/abs/2101.00027", + "website_link": "", + "github_link": "https://huggingface.co/datasets/EleutherAI/pile-standard-pythia-preshuffled", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "datasets_logo.png" + }, + { + "name": "Data Cards Playbook", + "description": "A tool to create a Data Card that thoroughly documents a new dataset.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Data Documentation" + ], + "date": "6-2022", + "primary_link": "Webpage", + "paper_link": "https://dl.acm.org/doi/fullHtml/10.1145/3531146.3533231", + "website_link": "https://sites.research.google/datacardsplaybook/", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Data Nutrition Labels", + "description": "A generic but thorough form of dataset documentation.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Data Documentation" + ], + "date": "2020", + "primary_link": "Webpage", + "paper_link": "https://arxiv.org/abs/1805.03677", + "website_link": "https://datanutrition.org/", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Data Provenance Attribution Card", + "description": "A repository to select datasets and generate a summary. 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+ "description": "A report outlining governance questions, approaches, and tooling in the BigCode project, with a focus on Data governance", + "modalities": [ + "Text" + ], + "categories": [ + "Data Governance" + ], + "date": "11-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/abs/2312.03872", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "AmIinTheStack", + "description": "A tool to let software developers check whether their code was included in TheStack dataset and opt out of inclusion in future versions", + "modalities": [ + "Text" + ], + "categories": [ + "Data Governance" + ], + "date": "9-2022", + "primary_link": "Hugging Face object", + "paper_link": "", + "website_link": "", + "github_link": "", + "huggingface_link": "https://huggingface.co/spaces/bigcode/in-the-stack", + "added_by": "Original Authors" + }, + { + "name": "StarPII: BigCode Pseudonymization Model", + "description": "A 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selecting, filtering, and visualizing popular finetuning, instruction, and alignment training datasets from Hugging Face, based on their metadata such as source, license, languages, tasks, topics, among other properties.", + "modalities": [ + "Text" + ], + "categories": [ + "Data Search, Analysis, & Exploration" + ], + "date": "Frequently Updated", + "primary_link": "Webpage", + "paper_link": "https://arxiv.org/abs/2310.16787", + "website_link": "https://www.dataprovenance.org/", + "github_link": "https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection", + "huggingface_link": "https://huggingface.co/DataProvenanceInitiative", + "added_by": "Original Authors", + "logo": "Data-Provenance-Initiative_logo.png" + }, + { + "name": "GAIA Search Tool", + "description": "A search tool over C4, the Pile, ROOTS, and the text captions of LAION, developed with Pyserini (https://github.com/castorini/pyserini).", + "modalities": [ + "Text" + ], + "categories": [ + "Data Search, 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Useful for delving deeper if the following evaluations don't meet your needs.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "10-2023", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://dpmd.ai/46CPd58", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "SimpleSafetyTests", + "description": "Small probe set (100 English text prompts) covering severe harms: child abuse, suicide, self-harm and eating disorders, scams and fraud, illegal items, and physical harm", + "modalities": [ + "Text" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "11-2023", + "primary_link": "GitHub", + "paper_link": "https://arxiv.org/abs/2311.08370", + "website_link": "", + "github_link": "https://github.com/bertiev/SimpleSafetyTests", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "bertiev_logo.png" + }, + { + "name": "SneakyPrompt", + "description": "Automated jailbreaking method to generate NSFW content even with models that have filters applied", + "modalities": [ + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "5-2023", + "primary_link": "GitHub", + "paper_link": "https://arxiv.org/abs/2305.12082", + "website_link": "", + "github_link": "https://github.com/Yuchen413/text2image_safety", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "Yuchen413_logo.png" + }, + { + "name": "StableBias", + "description": "Bias testing benchmark for Image to Text models, based on gender-occupation associations.", + "modalities": [ + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "3-2023", + "primary_link": "Hugging Face object", + "paper_link": "", + "website_link": "https://arxiv.org/abs/2303.11408", + "github_link": "", + "huggingface_link": "https://huggingface.co/spaces/society-ethics/StableBias", + "added_by": "Original Authors" + }, + { + "name": "Cerebras Model Lab", + "description": "A calculator to apply compute-optimal scaling laws for a given budget, including factoring expected total inference usage.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Training: Efficiency & Resource Allocation" + ], + "date": "5-2023", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://www.cerebras.net/model-lab/", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "QLoRa", + "description": "An efficient finetuning approach that reduces memory usage while training.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Training: Efficiency & Resource Allocation" + ], + "date": "5-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/abs/2305.14314", + "website_link": "", + "github_link": "https://github.com/artidoro/qlora", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "artidoro_logo.png" + }, + { + "name": "Scaling Data-Constrained Language Models", + "description": "Demonstrates an optimal allocation of compute when dataset size is bounded", + "modalities": [ + "Text" + ], + "categories": [ + "Model Training: Efficiency & Resource Allocation" + ], + "date": "5-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/abs/2305.16264", + "website_link": "", + "github_link": "https://github.com/huggingface/datablations", + "huggingface_link": "https://huggingface.co/datablations", + "added_by": "Original Authors", + "logo": "huggingface_logo.png" + }, + { + "name": "Training Compute-Optimal Language Models", + "description": "Proposes an optimal allocation of computational budget between model and dataset size, and shows experimental design for fitting scaling laws for compute allocation in a new setting.", + "modalities": [ + "Text" + ], + "categories": [ + "Model Training: Efficiency & Resource Allocation" + ], + "date": "3-2022", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/abs/2203.15556", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "AI Incident Database", + "description": "A database of harmful incidents tied to AI systems where developers or users can submit incident reports", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Usage Monitoring" + ], + "date": "Frequently Updated", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://incidentdatabase.ai/", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "BigScience Ethical Charter", + "description": "Outlines BigScience's core values and how they promote them, which in turn guides use restrictions and communicates acceptable usage to users", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Usage Monitoring" + ], + "date": "6-2022", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://bigscience.huggingface.co/blog/bigscience-ethical-charter", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Llama 2 Responsible Use Guide", + "description": "Guidance for downstream developers on how to responsibly build with Llama 2. Includes details on how to report issues and instructions related to red-teaming and RLHF", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Usage Monitoring" + ], + "date": "12-2023", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://ai.meta.com/llama/responsible-use-guide/", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Model Gating from Hugging Face", + "description": "A resource describing how to require user credentials for model access, which may be appropriate for models trained for topics such as hate speech", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Usage Monitoring" + ], + "date": "Frequently Updated", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://huggingface.co/docs/hub/models-gated", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Model Monitoring in Practice Tutorial", + "description": "A tutorial given at FAccT and other venues describing how and why to monitor ML models. Includes a presentation on using transformer models to monitor for error detection", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Usage Monitoring" + ], + "date": "6-2022", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://sites.google.com/view/model-monitoring-tutorial", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Robust Invisible Video Watermarking with Attention", + "description": "A widely used watermark for video models ", + "modalities": [ + "Vision" + ], + "categories": [ + "Usage Monitoring" + ], + "date": "9-2029", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/abs/1909.01285", + "website_link": "", + "github_link": "https://github.com/DAI-Lab/RivaGAN", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "DAI-Lab_logo.png" + }, + { + "name": "Robust Distortion-free Watermarks for Language Models", + "description": "A watermark for autoregressive language models", + "modalities": [ + "Text" + ], + "categories": [ + "Usage Monitoring" + ], + "date": "7-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/abs/2307.15593", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "A Holistic Approach to Undesired Content Detection in the Real World", + "description": "Description of five primary categories (Sexual, Hateful, Violent, Self-harm, Harassment) with sub-categories (e.g. Sexual / sexual content involving minors). Also describes a moderation filter (the OpenAI moderation endpoint), and releases a dataset labelled for the categories.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "2-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2208.03274.pdf", + "website_link": "", + "github_link": "https://github.com/openai/moderation-api-release", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "openai_logo.png" + }, + { + "name": "Perspective API", + "description": "Perspective API for content moderation. It has three classes of categories, each with 6+ attributes. (1) Production (Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, and Threats), (2) Experimental (Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, Threat, Sexually Explicit, and Flirtation), (3) NY Times (Attack on author, Attack on commenter, Incoherent, Inflammatory, Likely to Reject, Obscene, Spam, Unsubstantial).", + "modalities": [ + "Text" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "8-2022", + "primary_link": "Paper", + "paper_link": "https://dl.acm.org/doi/pdf/10.1145/3534678.3539147", + "website_link": "https://developers.perspectiveapi.com/s/about-the-api-attributes-and-languages?language=en_US", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Mistral in-context self-reflection safety prompt", + "description": "Self-reflection prompt for use as a content moderation filter. It returns a binary value (safe/not) with 13 subcategories: Illegal, Child abuse, Hate Violence Harassment, Malware, Physical Harm, Economic Harm, Fraud, Adult, Political campaigning or lobbying, Privacy invasion, Unqualified law advice, Unqualified financial advice, Unqualified health advice", + "modalities": [ + "Text" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "10-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2310.06825.pdf", + "website_link": "https://www.promptingguide.ai/models/mistral-7b", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Google, Gemini API Safety Filters (via Vertex)", + "description": "Safety filter for Gemini models, available through Vertex. 4 safety attributes are described: Hate speech, Harassment, Sexually Explicit, and Dangerous Content. Probabilities are returned for each attribute (Negligible, Low, Medium, High). ", + "modalities": [ + "Text" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "12-2023", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/configure-safety-attributes", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Google, PaLM API Safety Filters (via Vertex)", + "description": "Safety filter for PaLM models, available through Vertex. 16 safety attributes are described (some of which are 'topical' rather than purely safety risks): Derogatory, Toxic, Violent, Sexual, Insult, Profanity, Death Harm & Tragedy, Firearms & Weapons, Public safety, Health, Religion & belief, Illicit drugs, War & conflict, Politics, Finance, Legal.", + "modalities": [ + "Text" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "3-2023", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://cloud.google.com/vertex-ai/docs/generative-ai/configure-safety-attributes-palm", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "ActiveFence's LLM Safety Review: Benchmarks and Analysis", + "description": "Description of 4 risk categories, as part of a benchmark review of LLM safety: (1) Hate, (2) Misinformation, (3) Self-harm & Suicide, (4) Child abuse & exploitation.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "07-2023", + "primary_link": "Paper", + "paper_link": "https://www.activefence.com/LLMSafety", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Anthropic content moderation prompt", + "description": "In-context prompt for assessing whether messages and responses contain inappropriate content: \"violent, illegal or pornographic activities\"", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms" + ], + "date": "12-2023", + "primary_link": "Webpage", + "paper_link": "", + "website_link": "https://docs.anthropic.com/claude/docs/content-moderation", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", + "description": "Description of 20 risk areas, as part of red teaming Anthropics' models. Two of the tags are not interpretable (\"Other\" and \"N/A - Invalid attempt\"): Discrimination & justice, Hate speech & offensive language, Violence & incitement, Non-violent unethical behaviour (e.g. lying, cheating), Bullying & harassment, Other, Theft, N/A - Invalid attempt, Soliciting personally identifiable information, Conspiracy theories & misinformation, Substance abuse & banned substances, Fraud & deception, Weapons, Adult content, Property crime & vandalism, Animal abuse, Terrorism & organized crime, Sexual exploitation & human trafficking, Self-harm, Child abuse.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "10-2022", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2209.07858.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "BEAVERTAILS: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset", + "description": "Description of 14 risk areas, as part of a QA dataset for aligning models and evaluating their safety: Hate Speech, Offensive Language, Discrimination, Stereotype, Injustice, Violence, Aiding and Abetting, Incitement, Financial Crime, Property Crime, Theft, Privacy Violation, Drug Abuse, Weapons, Banned Substance, Non-Violent Unethical Behavior, Sexually Explicit, Adult Content, Controversial Topics, Politics, Misinformation Re. ethics, laws and safety, Terrorism, Organized Crime, Self-Harm, Animal Abuse, Child Abuse", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "10-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2307.04657.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Safety Assessment of Chinese Large Language Models", + "description": "Description of 8 risk areas (called \"safety scenarios)\": Insult, Unfairness and Discrimination, Crimes and Illegal Activities, Sensitive Topics, Physical Harm, Mental health, Privacy and Property, Ethics and Morality. Six \"instruction attacks\" are also described: Goal hijacking, Prompt leaking, RolePlay Instruction, Unsafe Instruction Topic, Inquiry with Unsafe Opinion, Reverse Exposure.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "4-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2304.10436.pdf", + "website_link": "", + "github_link": "https://github.com/thu-coai/Safety-Prompts", + "huggingface_link": "", + "added_by": "Original Authors", + "logo": "thu-coai_logo.png" + }, + { + "name": "DECODINGTRUST: A Comprehensive Assessment of Trustworthiness in GPT Models", + "description": "Description of 8 evaluation areas: toxicity, stereotypes bias, adversarial robustness, out-of-distribution robustness, robustness against adversarial demonstrations, privacy, machine ethics, fairness.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "1-2024", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2306.11698.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "A Unified Typology of Harmful Content", + "description": "Taxonomy of harmful online content. There are 4 primary categories, which each have subcategories: (1) Hate and harassment (Doxxing, Identity attack, Identity misrepresentation, Insult, Sexual aggression, Threat of violence; (2) Self-inflicted harm (Eating disorder promotion, self-harm), (3) Ideological harm (Extremism Terrorism & Organized crime, Misinformation), (4) Exploitation (Adult sexual services, Child sexual abuse material, Scams).", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "Taxonomy of harmful online content. There are 4 primary categories, which each have subcategories: (1) Hate and harassment (Doxxing, Identity attack, Identity misrepresentation, Insult, Sexual aggression, Threat of violence; (2) Self-inflicted harm (Eating disorder promotion, self-harm), (3) Ideological harm (Extremism Terrorism & Organized crime, Misinformation), (4) Exploitation (Adult sexual services, Child sexual abuse material, Scams).", + "primary_link": "Paper", + "paper_link": "https://aclanthology.org/2020.alw-1.16.pdf", + "website_link": "https://docs.cohere.com/docs/content-moderation-with-classify", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements", + "description": "Description of 7 risk areas, as part of a survey on LLM risks: Toxicity and Abusive Content, Unfairness and Discrimination, Ethics and Morality Issues, Controversial Opinions, Misleading Information, Privacy and Data Leakage, Malicious Use and Unleashing AI Agents.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "11-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2302.09270.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Llama 2: Open Foundation and Fine-Tuned Chat Models", + "description": "Description of 3 risk areas, as part of the safety checks for releasing Llama2: (1) illicit and criminal activities (terrorism, theft, huam trafficking), (2) hateful and harmful activities (defamation, self-harm, eating disorders, discrimination), and (3) unqualified advice (medical, financial and legal advice). Other risk categories are described as part of red teaming and soliciting feedback.", + "modalities": [ + "Text" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "7-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2307.09288.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Ethical and social risks of harm from Language Models", + "description": "Two-tier taxonomy of risks, comprising both classification groups (of which there are 6) and associated harms (3 or 4 for each classification group). The classification groups are: (1) Discrimination, Exclusion and Toxicity, (2) Information Hazards, (3) Misinformation Harms, (4) Malicious Uses, (5) Human-Computer Interaction Harms, and (6) Automation, access, and environmental harms.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "12-2021", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2112.04359.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Sociotechnical Safety Evaluation of Generative AI Systems", + "description": "Two-tier taxonomy of risks, comprising both classification groups (of which there are 6) and associated harms (3 or 4 for each classification group). The classification groups are: (1) Representation and Toxicity Harms, (2) Misinformation Harms, (3) Information & Society Harms, (4) Malicious Use, (5) Human Autonomy & Integrity Harms, and (6) Socioeconomic & Environmental Harms.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "10-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2310.11986.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment", + "description": "Two-tier taxonomy of risks, with seven major categories of LLM trustworthiness, each of which has several associated sub-categories: (1) Reliability, (2) Safety, (3) Fairness, (4) Resistance to Misuse, (5) Explainability and Reasoning, (6) Social Norms, and (7) Robustness.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "8-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2308.05374.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets", + "description": "Description of 8 risk areas, as part of describing methods for aligning models: (1) Abuse, Violence and Threat (inclusive of self-harm), (2) Health (phyiscal and mental), (3) Human characteristics and behaviour, (4) Injustice and inequality (incl, discrimination, harmful stereotypes), (5) Political opinion and destabilization, (6) Relationships (romantic, familial friendships), (7) Sexual activity (inclusive of pornography), (8) Terrorism (inclusive of white supremacy).", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "6-2021", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2106.10328.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction", + "description": "Description of 5 categories of harm, with detailed subcategories: (1) Representational harms, (2) Allocative harms, (3) Quality of Service harms, (4) Interpersonal harms, and (5) Social system harms. ", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "7-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2210.05791.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks", + "description": "Taxonomy of 12 privacy risks, based on reviewing 321 privacy-related incidents, filtered from the AI, Algorithmic and Automation Incident and Controversy Repository (AIAAIC) Database. Risks are split into those that are created by AI (Identification, Distortion, Exposure, Aggregation, Phrenology/Physiognomy) and those that are exacerbated by AI (Intrusion, Surveillance, Exclusion, Secondary Use, Insecurity, Increased Accessibility).", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "10-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2310.07879.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "The Ethical Implications of Generative Audio Models: A Systematic Literature Review", + "description": "Taxonomy of 12 \"negative broader impacts\" from generative models involving speech and music.", + "modalities": [ + "Speech" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "7-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2307.05527.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "An Overview of Catastrophic AI Risks", + "description": "Taxonomy of 4 catastrophic AI risks, with subcategories: (1) Malicious use (Bioterrrorism, Uncontrolled AI agents, AI capabilities for propaganda, Censorship and surveillance), (2) AI race (Autonomous weapons, Cyberwarfare, Automated human labour [mass unemployment and dependence on AI systems], (3) Organizational risks (AI accidentally leaked/stolen), (4) Rogue AIs (Proxy gaming, Goal drift, Power-seeking, Deception).", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "9-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2306.12001.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation", + "description": "Taxonomy of 3 AI security risks, with subcategories: (1) Digital Security, Physical Security, Political Security.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "2-2018", + "primary_link": "Paper", + "paper_link": "https://img1.wsimg.com/blobby/go/3d82daa4-97fe-4096-9c6b-376b92c619de/downloads/MaliciousUseofAI.pdf?ver=1553030594217", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Open-sourcing highly capable foundation models", + "description": "Description of risks from malicious use of AI: Influence operations, Surveillance and population control, Scamming and spear phishing, Cyber attacks, Biological and chemical weapons development. Some \"extreme risks\" are also described in the paper (e.g. disruption to key societal functions).", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "9-2023", + "primary_link": "Paper", + "paper_link": "https://cdn.governance.ai/Open-Sourcing_Highly_Capable_Foundation_Models_2023_GovAI.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "How Does Access Impact Risk? Assessing AI Foundation Model Risk Along a Gradient of Access ", + "description": "Description of risks from open-sourcing models, including five instances of malicious use: (1) Fraud and other crime schemes, (2) Undermining of social cohesion and democratic processes, (3) Human rights abuses, (4) Disruption of critical infrastructure, and (5) State conflict. ", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "12-2023", + "primary_link": "Paper", + "paper_link": "https://securityandtechnology.org/wp-content/uploads/2023/12/How-Does-Access-Impact-Risk-Assessing-AI-Foundation-Model-Risk-Along-A-Gradient-of-Access-Dec-2023.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "OpenAI Preparedness Framework (Beta)", + "description": "Description of 4 catastrophic AI risks: (1) Cybersecurity, (2) Chemical, Biological, Nuclear and Radiological (CBRN) threats, (3) Persuasion, and (4) Model autonomy. The paper also highlights the risk of \"unknown unknowns\".", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "12-2023", + "primary_link": "Paper", + "paper_link": "https://cdn.openai.com/openai-preparedness-framework-beta.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Anthropic's Responsible Scaling Policy", + "description": "Framework with four tiers of model capability, ffrom ASL-1 (smaller models) to ASL-4 (speculative), with increasing risk as models' capability increases. It also describes 4 catastrophic AI risks: (1) Misuse risks, (2) CBRN risks, (3) Cyber risks, and (4) Autonomy and replication risks.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "9-2023", + "primary_link": "Paper", + "paper_link": "https://www-files.anthropic.com/production/files/responsible-scaling-policy-1.0.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Model evaluation for extreme risks", + "description": "Framework of 9 dangerous capabilities of AI models: (1) Cyber-offense, (2) Deception, (3) Persuasion & manipulation, (4) Politial strategy, (5) Weapons acquisition, (6) Long-horizon planning, (7) AI development, (8) Situational awareness, (9) Self-proliferation.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "9-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2305.15324.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "Frontier AI Regulation: Managing Emerging Risks to Public Safety", + "description": "Description of \"sufficiently dangerous capabilities\" of AI models to cause serious harm and disruption on a global scale, such as synthesing new biological or chemical weapons and evading human control through means of deception and obfuscation.", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "11-2023", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2307.03718.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "The Fallacy of AI Functionality", + "description": "Taxonomy of four AI failure points: (1) Impossible tasks (either Conceptually impossible or Practically impossible), (2) Engineering failures (Design failures, Implementation failures, Missing Safety Features), (3) Post-Deployment Failures (Robustness Issues, Failure under Adversarial Attacks, Unanticipated Intractions, (4) Communication Failures (Falsified or Overstated Capabilities, Misrepresented Capabilities).", + "modalities": [ + "Text", + "Speech", + "Vision" + ], + "categories": [ + "Model Evaluation: Risks & Harms Taxonomies" + ], + "date": "7-2022", + "primary_link": "Paper", + "paper_link": "https://arxiv.org/pdf/2206.09511.pdf", + "website_link": "", + "github_link": "", + "huggingface_link": "", + "added_by": "Original Authors" + }, + { + "name": "TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI", + "description": "Framework of 3 potential harms from AI: (1) Harm to people (individual harm, Group/community harm, Societal harm), (2) Harm to an Organisation or Enterprise, (3) Harm to a system. 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vercel.json diff --git a/.editorconfig b/.editorconfig new file mode 100755 index 0000000..a257bcd --- /dev/null +++ b/.editorconfig @@ -0,0 +1,14 @@ +; https://editorconfig.org + +root = true + +[*] +charset = utf-8 +end_of_line = lf +indent_size = 2 +indent_style = space +trim_trailing_whitespace = true +insert_final_newline = true + +[*.md] +trim_trailing_whitespace = false diff --git a/README.md b/.github/README.md similarity index 93% rename from README.md rename to .github/README.md index 47aa0dd..6d67223 100644 --- a/README.md +++ b/.github/README.md @@ -5,7 +5,7 @@ Resources and recommendations for best practices in developing and releasing mod [**Cheatsheet**](https://fmcheatsheet.org) | [**Contribute Resources**](#add-to-cheatsheet) | [**Paper**](https://github.com/allenai/fm-cheatsheet/blob/main/app/resources/paper.pdf) | [**Contact and Citation**](#contact-and-citation)

- The Foundation Model Development Cheatsheet + The Foundation Model Development Cheatsheet

## Add to Cheatsheet @@ -40,4 +40,4 @@ Contact [slongpre@media.mit.edu](mailto:slongpre@media.mit.edu) for questions ab ``` Citation coming soon. -``` \ No newline at end of file +``` diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000..155bf0d --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,75 @@ +# Sample workflow for building and deploying a Hugo site to GitHub Pages +name: Deploy Hugo site to Pages + +on: + # Runs on pushes targeting the default branch + push: + branches: ["main"] + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Environment variables available to all jobs and steps in this workflow +env: + HUGO_ENV: production + HUGO_VERSION: "0.115.4" + GO_VERSION: "1.20.5" + NODE_VERSION: "18.15.0" + TINA_CLIENT_ID: ${{ vars.TINA_CLIENT_ID }} + TINA_TOKEN: ${{ vars.TINA_TOKEN }} + +jobs: + # Build job + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Node.js + uses: actions/setup-node@v3 + with: + node-version: ${{ env.NODE_VERSION }} + + - name: Install Hugo + run: | + curl -LO "https://github.com/gohugoio/hugo/releases/download/v${{ env.HUGO_VERSION }}/hugo_extended_${{ env.HUGO_VERSION }}_Linux-64bit.tar.gz" + tar -xvf hugo_extended_${{ env.HUGO_VERSION }}_Linux-64bit.tar.gz + sudo mv hugo /usr/local/bin/ + rm hugo_extended_${{ env.HUGO_VERSION }}_Linux-64bit.tar.gz + hugo version + + - name: Install Go + run: | + curl -LO "https://dl.google.com/go/go${{ env.GO_VERSION }}.linux-amd64.tar.gz" + sudo tar -C /usr/local -xzf go${{ env.GO_VERSION }}.linux-amd64.tar.gz + echo "export PATH=$PATH:/usr/local/go/bin" >> $GITHUB_ENV + rm go${{ env.GO_VERSION }}.linux-amd64.tar.gz + go version + + - name: Setup Project + run: npm run project-setup + + - name: Install npm dependencies + run: npm install + + - name: Publish to GitHub Pages + run: npm run build + + - name: Upload artifact + uses: actions/upload-pages-artifact@v1 + with: + path: ./public + + # Deployment job + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + needs: build + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v2 diff --git a/.gitignore b/.gitignore old mode 100644 new mode 100755 index 152ff01..3fce3df --- a/.gitignore +++ b/.gitignore @@ -1,81 +1,21 @@ -# Python cache files -__pycache__ -**/*.pyc - -.skiff/webapp.yaml -*.code-workspace - -# build artifacts - -.eggs/ -.mypy_cache -*.egg-info/ -build/ -dist/ -pip-wheel-metadata/ - - -# dev tools - -.envrc -.python-version -.idea -.venv/ -.vscode/ -/*.iml - - -# jupyter notebooks - -.ipynb_checkpoints - - -# miscellaneous - -.cache/ -doc/_build/ -*.swp +Thumbs.db .DS_Store - - -# python - -*.pyc -*.pyo -__pycache__ - - -# testing and continuous integration - -.coverage -.pytest_cache/ -.benchmarks - -# documentation build artifacts - -docs/build -site/ - -# runs -/runs/ -/wandb/ -/scratch/ - -# compiled rust extension -target/ -*.so - -# macOS metadata -.DS_Store - -# ignoring test output -/tests/work/ -/python/dolma/core/warc - -# ignore vscode directory -.vscode - -# ignore temporary directories -/tmp/ -/temp/ - +.dist +.tmp +.lock +.sass-cache +npm-debug.log +node_modules +builds +package-lock.json +public +resources +.hugo_build.lock +jsconfig.json +hugo_stats.json +hugo_stats_decoded.json +go.sum +yarn.lock +/static/formHandle.php +/push.sh +/.idea/ diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml new file mode 100644 index 0000000..dfb1456 --- /dev/null +++ b/.gitlab-ci.yml @@ -0,0 +1,38 @@ +stages: + - build + +variables: + HUGO_ENV: production + HUGO_VERSION: "0.115.4" + GO_VERSION: "1.20.5" + NODE_VERSION: "18.16.1" + +cache: + paths: + - node_modules/ + +default: + image: node:${NODE_VERSION} + before_script: + - echo "USING NODE ${NODE_VERSION}" + - apt-get update && apt-get install -y curl + - curl -LO "https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_Linux-64bit.tar.gz" + - tar -xvf hugo_extended_${HUGO_VERSION}_Linux-64bit.tar.gz + - mv hugo /usr/local/bin/ + - rm hugo_extended_${HUGO_VERSION}_Linux-64bit.tar.gz + - echo "HUGO ${HUGO_VERSION} INSTALLED" + - curl -LO "https://dl.google.com/go/go${GO_VERSION}.linux-amd64.tar.gz" + - tar -C /usr/local -xzf go${GO_VERSION}.linux-amd64.tar.gz + - export PATH=$PATH:/usr/local/go/bin + - rm go${GO_VERSION}.linux-amd64.tar.gz + - echo "GO ${GO_VERSION} INSTALLED" + - npm install + +pages: + stage: build + script: + - npm run project-setup + - npm run build + artifacts: + paths: + - public diff --git a/.jshintrc b/.jshintrc new file mode 100644 index 0000000..873eef9 --- /dev/null +++ b/.jshintrc @@ -0,0 +1,59 @@ +{ + "maxerr": 50, + "bitwise": true, + "camelcase": false, + "curly": true, + "eqeqeq": true, + "forin": true, + "freeze": true, + "immed": true, + "indent": 2, + "latedef": true, + "newcap": false, + "noarg": true, + "noempty": true, + "nonbsp": true, + "nonew": true, + "plusplus": false, + "undef": true, + "unused": false, + "strict": true, + "maxparams": false, + "maxdepth": 4, + "maxstatements": false, + "maxcomplexity": false, + "maxlen": 400, + "browser": true, + "devel": true, + "asi": false, + "boss": false, + "debug": false, + "eqnull": false, + "es3": false, + "es5": false, + "esversion": 12, + "moz": false, + "evil": true, + "expr": true, + "funcscope": false, + "globalstrict": false, + "iterator": false, + "lastsemic": false, + "laxbreak": false, + "laxcomma": false, + "loopfunc": true, + "multistr": true, + "noyield": false, + "notypeof": false, + "proto": false, + "scripturl": false, + "shadow": false, + "sub": false, + "supernew": false, + "validthis": false, + "globals": { + "jQuery": false, + "google": false, + "$": false + } +} diff --git a/.markdownlint.json b/.markdownlint.json new file mode 100755 index 0000000..7d41e3f --- /dev/null +++ b/.markdownlint.json @@ -0,0 +1,5 @@ +{ + "MD033": false, + "MD034": false, + "MD013": false +} diff --git a/.prettierrc b/.prettierrc new file mode 100755 index 0000000..ee30508 --- /dev/null +++ b/.prettierrc @@ -0,0 +1,13 @@ +{ + "plugins": ["prettier-plugin-go-template"], + "overrides": [ + { + "files": ["*.html"], + "options": { + "parser": "go-template", + "goTemplateBracketSpacing": true, + "bracketSameLine": true + } + } + ] +} diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..aeeaaa8 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,57 @@ +# Stage 1: Build stage +FROM node:20.12.2 as builder + +# Set environment variables +ENV HUGO_ENV production +ENV HUGO_VERSION 0.115.4 +ENV GO_VERSION 1.22.2 + +ENV BASE_URL http://localhost +ENV BASE_PORT 8080 + +# Install Hugo +RUN apt-get update && apt-get install -y curl \ + && curl -LO "https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_Linux-64bit.tar.gz" \ + && tar -xvf hugo_extended_${HUGO_VERSION}_Linux-64bit.tar.gz \ + && mv hugo /usr/local/bin/ \ + && rm hugo_extended_${HUGO_VERSION}_Linux-64bit.tar.gz \ + && echo "Hugo ${HUGO_VERSION} installed" + +# Install Go +RUN curl -LO "https://dl.google.com/go/go${GO_VERSION}.linux-amd64.tar.gz" \ + && tar -C /usr/local -xzf go${GO_VERSION}.linux-amd64.tar.gz \ + && rm go${GO_VERSION}.linux-amd64.tar.gz \ + && echo "export PATH=$PATH:/usr/local/go/bin" >> /etc/profile \ + && . /etc/profile \ + && echo "Go ${GO_VERSION} installed" + +# Export the PATH variable +RUN export PATH=$PATH:/usr/local/go/bin + +# Set working directory +WORKDIR /app + +# Copy package.json and package-lock.json +COPY package*.json ./ + +# Install project dependencies +RUN npm install + +# Copy project files +COPY . . + +# Run project setup and build +RUN PATH=$PATH:/usr/local/go/bin hugo --gc --minify --templateMetrics --templateMetricsHints --forceSyncStatic --baseURL=$BASE_URL:$BASE_PORT -e production --minify + + +# Stage 2: Serve stage +FROM nginx:alpine + +# Copy built site from builder stage +COPY --from=builder /app/public /usr/share/nginx/html + +# Expose port 80 +EXPOSE 80 + +# Command to start nginx +CMD ["nginx", "-g", "daemon off;"] diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..30343a1 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2023 - Present, Zeon Studio + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/amplify.yml b/amplify.yml new file mode 100644 index 0000000..d6498c9 --- /dev/null +++ b/amplify.yml @@ -0,0 +1,29 @@ +version: 1 +frontend: + phases: + preBuild: + commands: + - yum install -y curl + - curl -LO "https://github.com/gohugoio/hugo/releases/download/v0.115.4/hugo_extended_0.115.4_Linux-64bit.tar.gz" + - tar -xvf hugo_extended_0.115.4_Linux-64bit.tar.gz + - mv hugo /usr/local/bin/ + - rm hugo_extended_0.115.4_Linux-64bit.tar.gz + - echo "HUGO 0.115.4 INSTALLED" + - curl -LO "https://dl.google.com/go/go1.20.5.linux-amd64.tar.gz" + - tar -C /usr/local -xzf go1.20.5.linux-amd64.tar.gz + - export PATH=$PATH:/usr/local/go/bin + - rm go1.20.5.linux-amd64.tar.gz + - echo "GO 1.20.5 INSTALLED" + - npm install + build: + commands: + - npm run project-setup + - npm run build + artifacts: + # IMPORTANT - Please verify your build output directory + baseDirectory: /public + files: + - "**/*" + cache: + paths: + - node_modules/**/* diff --git a/assets/icons.py b/assets/icons.py new file mode 100644 index 0000000..5d1ab2f --- /dev/null +++ b/assets/icons.py @@ -0,0 +1,79 @@ +import os +import requests +import json +from urllib.parse import urlparse + +def get_github_profile_logo(github_link, save_dir, github_token): + if github_link == "": + return None + + # Extracting username and repository from GitHub link + parsed_url = urlparse(github_link) + path_parts = parsed_url.path.strip("/").split("/") + if len(path_parts) < 1: + print(f"Invalid GitHub link: {github_link}") + return None + username = path_parts[0] + + # Making a request to GitHub API to get user information + api_url = f"https://api.github.com/users/{username}" + headers = { + 'Authorization': f'token {github_token}', + 'User-Agent': 'Your-User-Agent' # Replace 'Your-User-Agent' with your actual user agent + } + response = requests.get(api_url, headers=headers) + + if response.status_code == 200: + user_info = response.json() + profile_logo_url = user_info['avatar_url'] + + # Downloading the profile logo and saving it to the specified directory + logo_filename = f"{username}_logo.png" + logo_filepath = os.path.join(save_dir, logo_filename) + with open(logo_filepath, 'wb') as f: + f.write(requests.get(profile_logo_url).content) + + return logo_filepath + else: + print(f"Failed to fetch profile logo for {github_link}") + print(f"GitHub API response: {response.status_code} - {response.reason}") + return None + + +def update_resources_json(resources, github_profile_logos, resources_file): + for i, resource in enumerate(resources): + if i < len(github_profile_logos): # Check index to avoid out of bounds + logo_path = github_profile_logos[i] + resource['logo'] = os.path.basename(logo_path) + else: + resource['logo'] = "" # Or provide a default placeholder + + with open(resources_file, 'w') as f: + json.dump(resources, f, indent=2) + +def main(): + resources_file = '/home/iguana/WebstormProjects/fm-cheatsheet/assets/resources.json' + save_dir = '/home/iguana/WebstormProjects/fm-cheatsheet/assets/images/gh-icons' + github_token = '' # Replace 'Your-GitHub-Token' with your actual GitHub token + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + with open(resources_file) as f: + resources = json.load(f) + + for resource in resources: + github_link = resource.get('github_link') + if github_link: # Only process resources with a GitHub link + profile_logo_path = get_github_profile_logo(github_link, save_dir, github_token) + if profile_logo_path: + resource['logo'] = os.path.basename(profile_logo_path) + # Update resources.json immediately + with open(resources_file, 'w') as f: + json.dump(resources, f, indent=2) + + print("GitHub profile logos saved to:", save_dir) # All logos saved + print("resources.json updated with logo property") # resources.json is already updated + +if __name__ == "__main__": + main() diff --git a/assets/images/aimodelsorg-replace.png b/assets/images/aimodelsorg-replace.png new file mode 100644 index 0000000000000000000000000000000000000000..2c2963c51837098dad03f210114bd27a77c33062 GIT binary patch literal 851061 zcmV)SK(fDyP)EX>4Tx04R}tkv&MmKp2MKwn{}R4rUN>$WWauNEH!WvXpmh$_#gc4tyP$adr6T5(EZ{#A0t3`7iiWU=lj@knkPW$8MxBh{%Ql5{v^HL z)}lv1|2A-O-PV*n;Bp5Td@^KHcBK%lV4(oKpV2qvfPq_}cg^ct>l~*KK!$pix&aOj zfzcvmulu~ar?YSW*0lQf1H~3{$dban+5i9m24YJ`L;(K){{a7>y{D4^000SaNLh0L z01FcU01FcV0GgZ_00007bV*G`2j~L|3k)AKHU#4U000?uMObu0Z*6U5Zgc=ca%Ew3 zWn>_CX>@2HM@dakSAh-}001BWNklId}&dXbq#YG&A7-Boo?X6}e^Ko5Ys!#!f3B1dgjop~~M?n{I_ z99URuf&Rn)_P?tF5K*WCP(T4BCJ+E3fQdi^00e~s0GJ8zpA!*??W@+GasA$Y2ez+F z1SNpE{~fo#0ob1}F#rM*0ZeLN?PG;xxa$}AY~8)M2cfw@u!d9;XNg!{SJuuwNi*E_F~r$s@M*}QSAF73Q_x= z{~G>hP!I%wszS)Fs{l~D(DpsZZ-Cthf?oi?eu{A2*sfXjYhPgBX9AL6zXTvd!KD2@ 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