Delete emotions.py

emotions.py

@@ -1,235 +0,0 @@
-<<<<<<< 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