DeBERTa classifier

simple_transformers.py

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+from simpletransformers.classification import ClassificationModel, ClassificationArgs
+import pandas as pd
+import logging
+import torch
+
+logging.basicConfig(level=logging.INFO)
+transformer_logger = logging.getLogger("transformers")
+transformer_logger.setLevel(logging.WARNING)
+
+
+train_df = pd.read_csv("train/train.tsv", sep="\t")
+print(train_df)
+
+dev_df = pd.read_csv("dev-0/dev.tsv", sep="\t")
+print(dev_df)
+
+
+args = {
+    'train_batch_size': 32,
+    'learning_rate': 2e-5,
+    'evaluate_during_training': True,
+    'save_steps': 1000,
+    'evaluate_during_training_steps': 1000,
+    'evaluate_during_training_verbose': True,
+    'overwrite_output_dir': True,
+    'save_eval_checkpoints': True,
+    'use_early_stopping': True,
+    'early_stopping_patience': 5,
+    'num_train_epochs': 3
+}
+
+model = ClassificationModel("deberta", "microsoft/deberta-base", use_cuda=True, args=args)
+
+model.train_model(train_df, eval_df=dev_df)

simple_transformers_eval.py

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+from simpletransformers.classification import ClassificationModel
+import pandas as pd
+
+model = ClassificationModel("deberta", "outputs/best_model")
+
+dev_df = pd.read_csv("dev-0/dev.tsv", sep="\t")
+
+result, model_outputs, wrong_predictions = model.eval_model(dev_df)
+print(result)
+tp = result["tp"]
+fp = result["fp"]
+tn = result["tn"]
+fn = result["fn"]
+print(f"Accuracy: {(tp+tn)/(tp+fp+tn+fn)}")
+precision = tp/(tp+fp)
+print(f"Precision: {precision}")
+recall = tp/(tp+fn)
+print(f"Recall: {recall}")
+print(f"F1-score: {2*precision*recall/(precision+recall)}")