train.py

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

# 1. Load BiScope dataset
dataset = load_dataset("HanxiGuo/BiScope_Data")

# 2. Tokenizer
MODEL = "microsoft/deberta-v3-small"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

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

encoded_dataset = dataset.map(preprocess, batched=True)

# 3. Load model
model = AutoModelForSequenceClassification.from_pretrained(MODEL, num_labels=2)

# 4. Metrics
accuracy = evaluate.load("accuracy")

def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = logits.argmax(axis=-1)
    return accuracy.compute(predictions=predictions, references=labels)

# 5. Training args
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=2,
    weight_decay=0.01,
    push_to_hub=True,  # ✅ Upload to HF
    hub_model_id="your-username/biscope-detector"
)

# 6. Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=encoded_dataset["train"],
    eval_dataset=encoded_dataset["validation"],
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)

# 7. Train & Push
trainer.train()
trainer.push_to_hub()