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| <<<<<<< HEAD | |
| import pandas as pd | |
| import torch | |
| from datasets import load_dataset, Dataset | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer | |
| import numpy as np | |
| from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report | |
| # Load dataset | |
| dataset = load_dataset("go_emotions") | |
| # Print dataset columns | |
| print("Dataset Columns Before Preprocessing:", dataset["train"].column_names) | |
| # Ensure labels exist | |
| if "labels" not in dataset["train"].column_names: | |
| raise KeyError("Column 'labels' is missing! Check dataset structure.") | |
| # Load tokenizer | |
| model_checkpoint = "distilbert-base-uncased" | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) | |
| # Preprocessing function (Take only the first label for single-label classification) | |
| def preprocess_data(batch): | |
| encoding = tokenizer(batch["text"], padding="max_length", truncation=True) | |
| # Take only the first label (for single-label classification) | |
| encoding["labels"] = batch["labels"][0] if batch["labels"] else 0 # Default to 0 if empty | |
| return encoding | |
| # Tokenize dataset | |
| encoded_dataset = dataset.map(preprocess_data, batched=False, remove_columns=["text"]) | |
| # Set format for PyTorch | |
| encoded_dataset.set_format("torch") | |
| # Load model for single-label classification (28 classes) | |
| num_labels = 28 # Change based on dataset labels | |
| model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels) | |
| # Training arguments | |
| args = TrainingArguments( | |
| output_dir="./results", | |
| eval_strategy="epoch", | |
| save_strategy="epoch", | |
| save_total_limit=1, | |
| logging_strategy="no", | |
| per_device_train_batch_size=32, # Increase batch size | |
| per_device_eval_batch_size=32, | |
| num_train_epochs=2, # Reduce epochs | |
| weight_decay=0.01, | |
| load_best_model_at_end=True, | |
| fp16=True, # Mixed precision for speedup | |
| gradient_accumulation_steps=2, # Helps with large batch sizes | |
| ) | |
| # Compute metrics function | |
| def compute_metrics(eval_pred): | |
| logits, labels = eval_pred | |
| # Convert logits to class predictions | |
| predictions = np.argmax(logits, axis=-1) | |
| accuracy = accuracy_score(labels, predictions) | |
| f1 = f1_score(labels, predictions, average="weighted") | |
| return {"accuracy": accuracy, "f1": f1} | |
| # Initialize Trainer | |
| trainer = Trainer( | |
| model=model, | |
| args=args, | |
| train_dataset=encoded_dataset["train"], | |
| eval_dataset=encoded_dataset["validation"], | |
| compute_metrics=compute_metrics | |
| ) | |
| # Train model | |
| trainer.train() | |
| print("Training completed!") | |
| # Save model and tokenizer | |
| model.save_pretrained("./saved_model") | |
| tokenizer.save_pretrained("./saved_model") | |
| print("Model and tokenizer saved!") | |
| # ====== Evaluation on Test Set ====== | |
| print("\nEvaluating model on test set...") | |
| # Get test dataset | |
| test_dataset = encoded_dataset["test"] | |
| # Make predictions | |
| predictions = trainer.predict(test_dataset) | |
| logits = predictions.predictions | |
| # Convert logits to class predictions | |
| y_pred = np.argmax(logits, axis=-1) | |
| y_true = test_dataset["labels"].numpy() | |
| # Compute accuracy and F1-score | |
| accuracy = accuracy_score(y_true, y_pred) | |
| f1 = f1_score(y_true, y_pred, average="weighted") | |
| # Print evaluation results | |
| print("\nEvaluation Results:") | |
| print(f"Test Accuracy: {accuracy:.4f}") | |
| print(f"Test F1 Score: {f1:.4f}") | |
| # Print classification report | |
| print("\nClassification Report:\n", classification_report(y_true, y_pred)) | |
| # Save test results | |
| pd.DataFrame({"true_labels": y_true.tolist(), "predicted_labels": y_pred.tolist()}).to_csv("test_results.csv", index=False) | |
| print("Test results saved to 'test_results.csv'!") | |
| ======= | |
| import pandas as pd | |
| import torch | |
| from datasets import load_dataset, Dataset | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer | |
| import numpy as np | |
| from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, classification_report | |
| # Load dataset | |
| dataset = load_dataset("go_emotions") | |
| # Print dataset columns | |
| print("Dataset Columns Before Preprocessing:", dataset["train"].column_names) | |
| # Ensure labels exist | |
| if "labels" not in dataset["train"].column_names: | |
| raise KeyError("Column 'labels' is missing! Check dataset structure.") | |
| # Load tokenizer | |
| model_checkpoint = "distilbert-base-uncased" | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) | |
| # Preprocessing function (Take only the first label for single-label classification) | |
| def preprocess_data(batch): | |
| encoding = tokenizer(batch["text"], padding="max_length", truncation=True) | |
| # Take only the first label (for single-label classification) | |
| encoding["labels"] = batch["labels"][0] if batch["labels"] else 0 # Default to 0 if empty | |
| return encoding | |
| # Tokenize dataset | |
| encoded_dataset = dataset.map(preprocess_data, batched=False, remove_columns=["text"]) | |
| # Set format for PyTorch | |
| encoded_dataset.set_format("torch") | |
| # Load model for single-label classification (28 classes) | |
| num_labels = 28 # Change based on dataset labels | |
| model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels) | |
| # Training arguments | |
| args = TrainingArguments( | |
| output_dir="./results", | |
| eval_strategy="epoch", | |
| save_strategy="epoch", | |
| save_total_limit=1, | |
| logging_strategy="no", | |
| per_device_train_batch_size=32, # Increase batch size | |
| per_device_eval_batch_size=32, | |
| num_train_epochs=2, # Reduce epochs | |
| weight_decay=0.01, | |
| load_best_model_at_end=True, | |
| fp16=True, # Mixed precision for speedup | |
| gradient_accumulation_steps=2, # Helps with large batch sizes | |
| ) | |
| # Compute metrics function | |
| def compute_metrics(eval_pred): | |
| logits, labels = eval_pred | |
| # Convert logits to class predictions | |
| predictions = np.argmax(logits, axis=-1) | |
| accuracy = accuracy_score(labels, predictions) | |
| f1 = f1_score(labels, predictions, average="weighted") | |
| return {"accuracy": accuracy, "f1": f1} | |
| # Initialize Trainer | |
| trainer = Trainer( | |
| model=model, | |
| args=args, | |
| train_dataset=encoded_dataset["train"], | |
| eval_dataset=encoded_dataset["validation"], | |
| compute_metrics=compute_metrics | |
| ) | |
| # Train model | |
| trainer.train() | |
| print("Training completed!") | |
| # Save model and tokenizer | |
| model.save_pretrained("./saved_model") | |
| tokenizer.save_pretrained("./saved_model") | |
| print("Model and tokenizer saved!") | |
| # ====== Evaluation on Test Set ====== | |
| print("\nEvaluating model on test set...") | |
| # Get test dataset | |
| test_dataset = encoded_dataset["test"] | |
| # Make predictions | |
| predictions = trainer.predict(test_dataset) | |
| logits = predictions.predictions | |
| # Convert logits to class predictions | |
| y_pred = np.argmax(logits, axis=-1) | |
| y_true = test_dataset["labels"].numpy() | |
| # Compute accuracy and F1-score | |
| accuracy = accuracy_score(y_true, y_pred) | |
| f1 = f1_score(y_true, y_pred, average="weighted") | |
| # Print evaluation results | |
| print("\nEvaluation Results:") | |
| print(f"Test Accuracy: {accuracy:.4f}") | |
| print(f"Test F1 Score: {f1:.4f}") | |
| # Print classification report | |
| print("\nClassification Report:\n", classification_report(y_true, y_pred)) | |
| # Save test results | |
| pd.DataFrame({"true_labels": y_true.tolist(), "predicted_labels": y_pred.tolist()}).to_csv("test_results.csv", index=False) | |
| print("Test results saved to 'test_results.csv'!") | |
| >>>>>>> b1313c5d084e410cadf261f2fafd8929cb149a4f | |