fix some bugs with import

whustan · Aug 20, 2019 · af1a597 · af1a597
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diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,9 @@
+# Created by .ignore support plugin (hsz.mobi)
+### Example user template template
+### Example user template
+
+# IntelliJ project files
+.idea
+*.iml
+out
+gen
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
diff --git a/README.md b/README.md
@@ -1,2 +1,80 @@
 # LexiconNER
 This is the implementation of "Named Entity Recognition using Positive-Unlabeled Learning" published at ACL2019.
+
+###Set up and run
+Download glove.6B.100d.txt
+### Environment
+pytorch 1.1.0
+python 3.6.4
+cuda 8.0
+### Instructions for running code
+#### Phrase one <train bnPU model>
+**Train**
+Print parameters
+`run feature_pu_model.py  --h`
+optional arguments:
+  -h, --help            show this help message and exit
+  --lr LR               learning rate
+  --beta BETA           beta of pu learning (default 0.0)
+  --gamma GAMMA         gamma of pu learning (default 1.0)
+  --drop_out DROP_OUT   dropout rate
+  --m M                 class balance rate
+  --flag FLAG           entity type (PER/LOC/ORG/MISC)
+  --dataset DATASET     name of the dataset
+  --batch_size BATCH_SIZE
+                    	batch size for training and testing
+  --print_time PRINT_TIME
+                    	epochs for printing result
+  --pert PERT           percentage of data use for training
+  --type TYPE           pu learning type (bnpu/bpu/upu)
+e.g.)
+Train on PER type of conll2003 dataset:
+`python feature_pu_model.py --dataset conll2003 --type PER`
+** Evaluating**
+python feature_pu_model_evl.py --model saved_model/bnpu_conll2003_PER_lr_0.0001_prior_0.3_beta_0.0_gamma_1.0_percent_1.0 --flag PER --dataset conll2003 --output 1
+replace the model name from the training
+python final_evl.py
+Get the final result on all the entity type. Remember to revise the filenames to be the output file name of evaluating.
+
+#### Phrase two <train adaPU model>
+**dictionary generation**
+`run python ada_dict_generation.py -h`
+optional arguments:
+  -h, --help            show this help message and exit
+  --beta BETA           learning rate
+  --gamma GAMMA         gamma of pu learning (default 1.0)
+  --drop_out DROP_OUT   dropout rate
+  --m M                 class balance rate
+  --flag FLAG           entity type (PER/LOC/ORG/MISC)
+  --dataset DATASET     name of the dataset
+  --lr LR               learning rate
+  --batch_size BATCH_SIZE
+                        batch size for training and testing
+  --iter ITER           iteration time
+  --unlabeled UNLABELED
+                        use unlabeled data or not
+  --pert PERT           percentage of data use for training
+  --model MODEL         saved model name
+e.g.)
+`python ada_dict_generation.py --model saved_model/bnpu_conll2003_PER_lr_0.0001_prior_0.3_beta_0.0_gamma_1.0_percent_1.0 --flag PER --iter 1`
+**adaptive training**
+`run python adaptive_pu_model.py -h
+optional arguments:
+  -h, --help            show this help message and exit
+  --beta BETA           beta of pu learning (default 0.0)
+  --gamma GAMMA         gamma of pu learning (default 1.0)
+  --drop_out DROP_OUT   dropout rate
+  --m M                 class balance rate
+  --p P                 estimate value of prior
+  --flag FLAG           entity type (PER/LOC/ORG/MISC)
+  --dataset DATASET     name of the dataset
+  --lr LR               learning rate
+  --batch_size BATCH_SIZE
+                        batch size for training and testing
+  --output OUTPUT       write the test result, set 1 for writing result to
+                        file
+  --model MODEL         saved model name
+  --iter ITER           iteration time
+e.g.)
+`python adaptive\_pu\_model.py --model saved\_model/bnpu\_conll2003\_PER\_lr\_0.0001\_prior\_0.3\_beta\_0.0\_gamma\_1.0\_percent\_1.0 --flag PER --iter 1`
+Replace saved model names and iteration times when doing adaptive learning. And in the same iteration the iter number in dictionary generation and adaptive learning should be same.