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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
from datasets import load_dataset
import torch
# Step 1: Load tokenizer and model (use full BERT)
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
print("Using device:", device)
# Step 2: Load your cleaned dataset
dataset = load_dataset("csv", data_files="Qbias/cleaned_qbias_balanced.csv")["train"]
# Step 3: Split into train and test
dataset = dataset.train_test_split(test_size=0.1)
# Step 4: Tokenization function
def tokenize_function(example):
return tokenizer(example["text"], padding="max_length", truncation=True, max_length=512)
# Step 5: Tokenize the dataset
tokenized_dataset = dataset.map(tokenize_function, batched=True)
# Step 6: Set the format for PyTorch
tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask",
"label"])
# Step 7: Define training arguments
training_args = TrainingArguments(
output_dir="./bert-bias-detector",
evaluation_strategy="epoch",
save_strategy="epoch",
per_device_train_batch_size=8, # Lower for full BERT on 2080 Ti
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=500,
)
# Step 8: Define Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["test"],
tokenizer=tokenizer,
)
# Step 9: Train the model
trainer.train() |