a naive model

conto.py

@@ -0,0 +1,91 @@
+from datasets import load_dataset, DatasetDict
+from evaluate import load
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
+import torch
+import numpy as np
+
+labels = {
+    'contradiction': 0,
+    'neutral': 1,
+    'entailment': 2,
+}
+
+datasets = load_dataset('seongs1024/DKK-nli')
+datasets = DatasetDict({
+    'train': datasets['train'].shard(num_shards=100, index=0),
+    'validation': datasets['validation'].shard(num_shards=100, index=0),
+})
+
+metric = load('glue', 'mnli')
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+    return metric.compute(predictions=predictions, references=labels)
+
+check_point = 'klue/roberta-small'
+model = AutoModelForSequenceClassification.from_pretrained(check_point, num_labels=3)
+tokenizer = AutoTokenizer.from_pretrained(check_point)
+
+def preprocess_function(examples):
+    return tokenizer(
+        examples['premise'],
+        examples['hypothesis'],
+        truncation=True,
+        return_token_type_ids=False,
+    )
+encoded_datasets = datasets.map(preprocess_function, batched=True)
+
+batch_size = 8
+args = TrainingArguments(
+    "test-nli",
+    evaluation_strategy="epoch",
+    save_strategy='epoch',
+    learning_rate=2e-5,
+    per_device_train_batch_size=batch_size,
+    per_device_eval_batch_size=batch_size,
+    num_train_epochs=5,
+    weight_decay=0.01,
+    load_best_model_at_end=True,
+    metric_for_best_model='accuracy',
+)
+
+trainer = Trainer(
+    model,
+    args,
+    train_dataset=encoded_datasets["train"],
+    eval_dataset=encoded_datasets["validation"],
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train()
+trainer.evaluate()
+
+trainer.save_model('./model')
+
+# features = tokenizer(
+#     [
+#         'A man is eating pizza',
+#         'A black race car starts up in front of a crowd of people.',
+#         '오늘 맛있는 밥을 먹었어 근데 이게 어떻게 맛있다 설명하기는 좀 애매해.',
+#         '나 집에 가는 중.',
+#     ],
+#     [
+#         'A man eats something',
+#         'A man is driving down a lonely road.',
+#         '오늘 맛없는 밥을 먹었어 근데 이게 어떻게 맛있다 설명하기는 좀 애매해.',
+#         '나 집에 도착했어.',
+#     ],
+#     padding=True,
+#     truncation=True,
+#     return_tensors="pt"
+# )
+# print(features)
+
+# model.eval()
+# with torch.no_grad():
+#     scores = model(**features).logits
+#     print(scores)
+#     label_mapping = ['contradiction', 'entailment', 'neutral']
+#     labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
+#     print(labels)

requirements.txt

@@ -1,3 +1,6 @@
 datasets==2.10.0
+evaluate==0.4.1
+scipy==1.7.3
+scikit-learn==1.0.2
 torch==1.9.0
 transformers==4.26.0