import torch
import transformers
from transformers import LayoutLMv2Model, LayoutLMv2Tokenizer, TrainingArguments, Trainer
from transformers import DataCollatorForTokenClassification
from datasets import load_dataset
# Load the LayoutLMv2 model and tokenizer
model_name = "microsoft/layoutlmv2-base-uncased"
model = LayoutLMv2Model.from_pretrained(model_name)
tokenizer = LayoutLMv2Tokenizer.from_pretrained(model_name)
# Load and preprocess your custom dataset
dataset = load_dataset("your_custom_dataset")
# Tokenize and format the data
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Define training arguments
training_args = TrainingArguments(
output_dir="./output_dir",
evaluation_strategy="steps",
eval_steps=500,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
save_steps=1000,
save_total_limit=2,
num_train_epochs=3,
logging_dir="./logs",
logging_steps=500,
)
# Initialize Trainer
data_collator = DataCollatorForTokenClassification(tokenizer)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
)
# Fine-tune the model
trainer.train()
# Evaluate the fine-tuned model
results = trainer.evaluate()
# Save the fine-tuned model
model.save_pretrained("./fine_tuned_layoutlmv2_model")
# Inference with the fine-tuned model
from transformers import pipeline
ner = pipeline("ner", model="./fine_tuned_layoutlmv2_model", tokenizer=tokenizer)
result = ner("Your input text goes here.")
print(result)