Create eval.csv

eval.csv

@@ -0,0 +1,32 @@
+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)