Update README.md

README.md

@@ -25,6 +25,90 @@ It achieves the following results on the evaluation set:
 - epoch: 3.41
 - step: 1100
 
+
+Multi Corp Training,
+
+  model = AutoModelForTokenClassification.from_pretrained(
+      "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", num_labels=41, id2label=id2label, label2id=label2id
+  )
+
+  training_args = TrainingArguments(
+      report_to = 'wandb',                     # enable logging to W&B
+      output_dir = runname,    # output directory/ name for huggingface hub
+      learning_rate=5e-5,
+      per_device_train_batch_size=16,
+      per_device_eval_batch_size=16,
+      weight_decay=0.01,
+      evaluation_strategy = 'steps',          # check evaluation metrics at each epoch
+      max_steps = 2000,
+      logging_steps = 25,                    # we will log every 25 steps
+      eval_steps = 25,                      # we will perform evaluation every 25 steps
+      save_steps = 25,
+      load_best_model_at_end=True,
+      metric_for_best_model = 'eval_loss',
+      greater_is_better = False,
+      push_to_hub=True,
+      run_name = runname             # name of the W&B run
+  )
+
+  trainer = Trainer(
+      model=model,
+      args=training_args,
+      train_dataset=tokenized_data["train"],
+      eval_dataset=tokenized_data["validation"],
+      tokenizer=tokenizer,
+      data_collator=data_collator,
+      compute_metrics=compute_metrics,
+      callbacks = [EarlyStoppingCallback(early_stopping_patience=6)]
+  )
+
+[1101/2000 1:00:33 < 49:32, 0.30 it/s, Epoch 3.41/7] 
+
+25 	0.836100 	0.201612 	0.000000 	0.000000 	0.000000 	0.973546
+50 	0.149500 	0.154239 	0.233246 	0.124420 	0.162277 	0.972420
+75 	0.136300 	0.138105 	0.145299 	0.094708 	0.114671 	0.972385
+100 	0.129900 	0.123477 	0.425243 	0.203343 	0.275126 	0.975886
+125 	0.103100 	0.118570 	0.297553 	0.321727 	0.309168 	0.974136
+150 	0.117300 	0.113230 	0.393373 	0.214949 	0.277995 	0.977039
+175 	0.117500 	0.106183 	0.320082 	0.291551 	0.305151 	0.975930
+200 	0.093800 	0.102443 	0.353604 	0.291551 	0.319593 	0.975297
+225 	0.091900 	0.105976 	0.446684 	0.318942 	0.372156 	0.977127
+250 	0.088700 	0.093393 	0.439173 	0.335190 	0.380200 	0.977734
+275 	0.113300 	0.097715 	0.522222 	0.218199 	0.307793 	0.977637
+300 	0.092900 	0.085730 	0.473552 	0.349118 	0.401924 	0.979405
+325 	0.085700 	0.091731 	0.380009 	0.409471 	0.394190 	0.976960
+350 	0.081700 	0.086656 	0.554161 	0.389508 	0.457470 	0.980162
+375 	0.062400 	0.083441 	0.538000 	0.374652 	0.441708 	0.980769
+400 	0.077500 	0.085072 	0.486742 	0.477252 	0.481950 	0.978869
+425 	0.073000 	0.078521 	0.516658 	0.467967 	0.491108 	0.981103
+450 	0.081000 	0.077073 	0.552381 	0.430826 	0.484090 	0.981288
+475 	0.075100 	0.078478 	0.483887 	0.446147 	0.464251 	0.980408
+500 	0.062800 	0.073298 	0.550633 	0.484680 	0.515556 	0.982247
+525 	0.060600 	0.069571 	0.542723 	0.536676 	0.539683 	0.982608
+550 	0.063900 	0.071559 	0.539832 	0.506500 	0.522635 	0.981983
+575 	0.060700 	0.068333 	0.564646 	0.519034 	0.540881 	0.982546
+600 	0.062900 	0.072810 	0.602013 	0.416435 	0.492316 	0.981886
+625 	0.051300 	0.071469 	0.550901 	0.525070 	0.537675 	0.982335
+650 	0.059500 	0.067657 	0.553466 	0.478180 	0.513076 	0.982528
+675 	0.047500 	0.067443 	0.594739 	0.566852 	0.580461 	0.983663
+700 	0.052100 	0.065269 	0.564447 	0.546890 	0.555529 	0.983039
+725 	0.041500 	0.067790 	0.593516 	0.552461 	0.572253 	0.983672
+750 	0.046300 	0.067922 	0.609038 	0.538069 	0.571358 	0.983461
+775 	0.054300 	0.064636 	0.646725 	0.582173 	0.612753 	0.984499
+800 	0.049500 	0.067722 	0.650905 	0.517642 	0.576674 	0.983830
+825 	0.043100 	0.069327 	0.630043 	0.471216 	0.539177 	0.982880
+850 	0.048000 	0.063814 	0.631025 	0.528784 	0.575398 	0.984068
+875 	0.042500 	0.064527 	0.644913 	0.582637 	0.612195 	0.984543
+900 	0.043500 	0.065475 	0.608295 	0.490251 	0.542931 	0.983522
+925 	0.039200 	0.066043 	0.635938 	0.566852 	0.599411 	0.984323
+950 	0.046800 	0.062491 	0.646930 	0.547818 	0.593263 	0.984719
+975 	0.043700 	0.061204 	0.634625 	0.585422 	0.609032 	0.984543
+1000 	0.032000 	0.066377 	0.643390 	0.560353 	0.599007 	0.984349
+1025 	0.038100 	0.064764 	0.666482 	0.559424 	0.608279 	0.984745
+1050 	0.035300 	0.065642 	0.635359 	0.587279 	0.610374 	0.984464
+1075 	0.032800 	0.064835 	0.657262 	0.584030 	0.618486 	0.984587
+1100 	0.031700 	0.065726 	0.639810 	0.626741 	0.633208 	0.984710
+
 ## Model description
 
 More information needed