train.py

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
import os

# ✅ Dataset
dataset = load_dataset("HanxiGuo/BiScope_Data")

# ✅ Base model
BASE_MODEL = "distilbert-base-uncased"
MODEL_REPO = "yagnik12/AI_Text_Detecter_HanxiGuo_BiScope-Data"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

def tokenize(batch):
    return tokenizer(batch["text"], truncation=True, padding="max_length", max_length=256)

tokenized = dataset.map(tokenize, batched=True)

# ✅ Model
model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=2)

# ✅ Training setup
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    num_train_epochs=1,   # start small for demo
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    push_to_hub=True,
    hub_model_id=MODEL_REPO,
    hub_token=os.getenv("HF_TOKEN"),
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized["train"],
    eval_dataset=tokenized["test"],
    tokenizer=tokenizer,
)

# ✅ Train & push
trainer.train()
trainer.push_to_hub()
print(f"✅ Model pushed to https://huggingface.co/{MODEL_REPO}")