train.py

import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer
from datasets import load_dataset, load_metric

# Load dataset
dataset = load_dataset("conll2003")

# Load tokenizer
model_checkpoint = "dbmdz/bert-large-cased-finetuned-conll03-english"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

# Tokenize the dataset
def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
    return tokenized_inputs

tokenized_datasets = dataset.map(tokenize_and_align_labels, batched=True)

# Load model
model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=9)

# Training arguments
training_args = TrainingArguments(
    output_dir="./ner_model",
    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=3,
    weight_decay=0.01,
)

# Load metric
metric = load_metric("seqeval")

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

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

# Train model
trainer.train()

# Save model
trainer.save_model("./ner_model")
tokenizer.save_pretrained("./ner_model")