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from datasets import load_dataset
from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
import torch
# Load the dataset
dataset = load_dataset("ilyada/web_accessibility_dataset")
# Load pre-trained model and tokenizer
model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Split the dataset into train and test
train_test_split = tokenized_datasets["train"].train_test_split(test_size=0.2)
train_dataset = train_test_split['train']
test_dataset = train_test_split['test']
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
push_to_hub=True, # This enables pushing the model to Hugging Face Hub
hub_model_id="ilyada/web_accessibility_model", # LLM generated dataset
hub_strategy="end",
)
# Initialize the Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=test_dataset,
)
# Train the model
trainer.train()
# Evaluate the model
results = trainer.evaluate()
print(results)
# Push model to Hugging Face Hub
trainer.push_to_hub() |