update doc

README.md

@@ -27,22 +27,25 @@ Training
 --------
 
 ```
-python trainier.py [options]
+usage: python trainer.py --name <name> --train_filename <path> [options]
 
 optional arguments:
   -h, --help                 show this help message and exit
-  --gpus GPUS
-  --nodes NODES
-  --name NAME
-  --fast_dev_run
-  --train_filename TRAIN_FILENAME
-  --learning_rate LEARNING_RATE
-  --batch_size BATCH_SIZE
-  --epochs EPOCHS
-  --valid_size VALID_SIZE
-  --max_len MAX_LEN
-  --bert_flavor BERT_FLAVOR
-  --selected_features SELECTED_FEATURES
+  --gpus <int>               list of gpus to use (-1 = all in CUDA_VISIBLE_DEVICES)
+  --nodes <int>              number of nodes for distributed training (see pytorch_lightning doc)
+  --name <str>               experiment name
+  --fast_dev_run             run one batch to check that training works
+  --train_filename <path>    name of json file containing training/validation instances
+  --learning_rate <float>    learning rate (default=2e-5)
+  --batch_size <int>         size of batch (default=32)
+  --epochs <int>             number of epochs (default=20)
+  --valid_size_percent <int> validation set size in % (default=10)
+  --max_len <int>            max sequence length (default=256)
+  --bert_flavor <path>       pretrained bert model (default=monologg/biobert_v1.1_pubmed
+  --selected_features <list> list of features to load from input (default=title abstract)
+  --dropout <float>          dropout after bert
+  --loss <bce|f1>            choose loss function [f1, bce] (default=f1)
+  --augment_data             simulate missing abstract through augmentation (default=do not augment data)
 ```
 
 Example training command line:
@@ -51,6 +54,8 @@ Example training command line:
 python trainer.py --gpus=-1 --name test1 --train_filename ../scrappers/data/20200529/litcovid.json
 ```
 
+Logs are saved in `lightning_logs/`, best `val_loss` checkpoints in `checkpoints/`.
+
 pytorch-lightning provides a tensorboard logger. You can check it with
 ```
 tensorboard --logdir lightning_logs
@@ -60,6 +65,9 @@ Then point your browser to http://localhost:6006/.
 Generating predictions
 ----------------------
 
+Give as input a json file containing articles without a "topics" field. It will be added with predictions.
 ```
-predict.py --checkpoint checkpoints/epoch\=0-val_loss\=0.2044.ckpt --test_filename ../scrappers/data/20200529/cord19-metadata.json > predicted.json
+python predict.py --checkpoint checkpoints/epoch\=0-val_loss\=0.2044.ckpt --test_filename ../scrappers/data/20200529/cord19-metadata.json > predicted.json
 ```
+
+Note that at this time, this only works with original training data available in the same relative path as was used for training.

model.py

@@ -119,7 +119,7 @@ class Model(LightningModule):
     parser.add_argument('--learning_rate', default=2e-5, type=float, help='learning rate (default=2e-5)')
     parser.add_argument('--batch_size', default=32, type=int, help='size of batch (default=32)')
     parser.add_argument('--epochs', default=20, type=int, help='number of epochs (default=20)')
-    parser.add_argument('--valid_size_percent', default=10, type=int, help='validation set size in % (default=10)')
+    parser.add_argument('--valid_size_percent', default=10, type=int, help='validation set size in %% (default=10)')
     parser.add_argument('--max_len', default=256, type=int, help='max sequence length (default=256)')
     parser.add_argument('--bert_flavor', default='monologg/biobert_v1.1_pubmed', type=str, help='pretrained bert model (default=monologg/biobert_v1.1_pubmed')
     parser.add_argument('--selected_features', default=['title', 'abstract'], nargs='+', type=str, help='list of features to load from input (default=title abstract)')