Create main.py

main.py

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+from datasets import load_dataset
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
+import numpy as np
+from sklearn.metrics import accuracy_score, f1_score
+
+# Data loading
+dataset = load_dataset("tweet_eval", "sentiment")
+
+# Model Selection
+model_name = "bert-base-uncased"
+
+# Tokenization
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=128)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+tokenized_datasets = tokenized_datasets.remove_columns(["text"])
+tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+tokenized_datasets.set_format("torch")
+
+# Model setup
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)  
+    accuracy = accuracy_score(labels, predictions)
+    f1 = f1_score(labels, predictions, average='macro')  
+    return {'accuracy': accuracy, 'f1': f1}
+
+# Training Configuration
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=1,  # Increase for better performance
+    per_device_train_batch_size=80,  # Increase if possible
+    per_device_eval_batch_size=80,
+    warmup_steps=500,  # Adjust warmup steps
+    weight_decay=0.01,  # Slightly higher weight decay
+    logging_dir='./logs',
+    learning_rate=5e-5,  # Slightly higher learning rate
+    load_best_model_at_end=True,
+    metric_for_best_model='accuracy',  # Track accuracy
+    evaluation_strategy="epoch",  # Evaluate at the end of each epoch
+    save_strategy="epoch",
+    save_total_limit=2,  # Limit saved checkpoints
+)
+
+# Training
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+    compute_metrics=compute_metrics  # Add the compute_metrics function
+)
+
+trainer.train()
+