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from transformers import Trainer, AutoModelForSequenceClassification, AutoTokenizer
from datasets import load_dataset, load_metric
import json
# Load configuration
with open('../config/config.json') as f:
config = json.load(f)
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained('../model')
tokenizer = AutoTokenizer.from_pretrained(config['model_name'])
# Load dataset
dataset = load_dataset('csv', data_files={'test': '../data/test.csv'})
tokenized_datasets = dataset.map(lambda x: tokenizer(x['text'], padding="max_length", truncation=True), batched=True)
# Evaluation
metric = load_metric("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = logits.argmax(axis=-1)
return metric.compute(predictions=predictions, references=labels)
trainer = Trainer(
model=model,
tokenizer=tokenizer,
compute_metrics=compute_metrics
)
results = trainer.evaluate(tokenized_datasets['test'])
print(results)
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