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from transformers import BertTokenizer, BertForTokenClassification, Trainer, TrainingArguments
from transformers import DataCollatorForTokenClassification
from torch.utils.data import Dataset, DataLoader
# Load your labeled dataset here
# Replace 'your_dataset' with your actual dataset
# Define a custom dataset
class TableDetectionDataset(Dataset):
def __init__(self, data):
self.data = data
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
# Tokenize the data
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokenized_datasets = TableDetectionDataset(tokenizer(your_dataset, padding="max_length", truncation=True))
# Define model and data collator
model = BertForTokenClassification.from_pretrained("your_model_name", num_labels=2) # Assuming binary classification (table vs. non-table)
data_collator = DataCollatorForTokenClassification(tokenizer)
# Fine-tune the model
training_args = TrainingArguments(
output_dir="./table_detection_model",
overwrite_output_dir=True,
num_train_epochs=3,
per_device_train_batch_size=8,
save_steps=100,
save_total_limit=2,
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=tokenized_datasets,
)
trainer.train()
# Save the fine-tuned model
model.save_pretrained("./table_detection_model")
# Optionally, evaluate the model on a validation dataset
validation_dataset = tokenized_datasets # Replace with your validation dataset
results = trainer.evaluate(eval_dataset=validation_dataset)
print("Results:", results) to share with others. Click here ...
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