src/train.py

import pandas as pd
from datasets import Dataset
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, Trainer, TrainingArguments

# Load datasets
train_df = pd.read_csv("data/train.csv")
test_df = pd.read_csv("data/test.csv")

train_dataset = Dataset.from_pandas(train_df)
test_dataset = Dataset.from_pandas(test_df)

# Tokenizer
tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")

def tokenize(batch):
    return tokenizer(batch["text"], padding=True, truncation=True, max_length=128)

train_dataset = train_dataset.map(tokenize, batched=True)
test_dataset = test_dataset.map(tokenize, batched=True)

# Model
num_labels = len(train_df["label"].unique())
model = DistilBertForSequenceClassification.from_pretrained(
    "distilbert-base-uncased",
    num_labels=num_labels
)

# Training args
training_args = TrainingArguments(
    output_dir="models/distilbert",
    eval_strategy="epoch",  # Updated from evaluation_strategy
    save_strategy="epoch",
    logging_dir="logs",
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=test_dataset
)

trainer.train()
trainer.save_model("models/distilbert")

print("✅ Model trained and saved at models/distilbert")