notebooks/hf_with_torchtext_gen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to run this notebook, you will need to install the huggingface library with the following command: `pip install transformers`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/torchtext39/lib/python3.9/site-packages/tqdm-4.64.0-py3.9.egg/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from transformers import T5ForConditionalGeneration, T5Tokenizer, BartForConditionalGeneration, BartTokenizer, GPT2LMHeadModel, GPT2Tokenizer\n",
    "from torchtext.prototype.generate import GenerationUtil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t5 = T5ForConditionalGeneration.from_pretrained(\"t5-base\")\n",
    "bart = BartForConditionalGeneration.from_pretrained(\"facebook/bart-large-cnn\")\n",
    "gpt2 = GPT2LMHeadModel.from_pretrained(\"gpt2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/torchtext39/lib/python3.9/site-packages/transformers/models/t5/tokenization_t5.py:164: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
      "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
      "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
      "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
      "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['owning a dog is good for you, according to studies. a dog is']\n"
     ]
    }
   ],
   "source": [
    "# Testing Huggingface's T5\n",
    "test_sequence = [\"summarize: studies have shown that owning a dog is good for you\"]\n",
    "generative_hf_t5 = GenerationUtil(t5, is_encoder_decoder=True, is_huggingface_model=True)\n",
    "t5_tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n",
    "test_sequence_tk = t5_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
    "tokens = generative_hf_t5.generate(test_sequence_tk, max_len=20, pad_idx=t5.config.pad_token_id)\n",
    "print(t5_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PG. PG&E said it scheduled the blackouts in response to forecasts for high winds.']\n"
     ]
    }
   ],
   "source": [
    "# Testing Huggingface's BART\n",
    "test_sequence = [\"PG&E stated it scheduled the blackouts in response to forecasts for high winds \"\n",
    "    \"amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were \"\n",
    "    \"scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.\"]\n",
    "generative_hf_bart = GenerationUtil(bart, is_encoder_decoder=True, is_huggingface_model=True)\n",
    "bart_tokenizer = BartTokenizer.from_pretrained(\"facebook/bart-large-cnn\")\n",
    "test_sequence_tk = bart_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
    "tokens = generative_hf_bart.generate(test_sequence_tk, max_len=20, pad_idx=bart.config.pad_token_id)\n",
    "print(bart_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to\"]\n"
     ]
    }
   ],
   "source": [
    "# Testing Huggingface's GPT2\n",
    "test_sequence = [\"I enjoy walking with my cute dog\"]\n",
    "generative_hf_gpt2 = GenerationUtil(gpt2, is_encoder_decoder=False, is_huggingface_model=True)\n",
    "gpt2_tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
    "test_sequence_tk = gpt2_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
    "tokens = generative_hf_gpt2.generate(test_sequence_tk, max_len=20, pad_idx=gpt2.config.pad_token_id)\n",
    "print(gpt2_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
   ]
  }
 ],
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