added fine tunning

fine_tuning.py

@@ -0,0 +1,59 @@
+import random
+import torch
+from transformers import (
+    AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
+)
+
+class DataWrapper(torch.utils.data.Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __getitem__(self, idx):
+        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+        item['labels'] = torch.tensor(self.labels[idx])
+        return item
+
+    def __len__(self):
+        return len(self.labels)
+
+def read_data(file_path):
+    with open(file_path) as f:
+        return f.readlines()
+
+def wirte_output(file_path, data):
+    with open(file_path,  'w') as writer:
+        for result in trainer.predict(data):
+            writer.write(f"{str(result)}\n")
+
+print("STEP 1 - READ DATA")
+X_train = read_data('train/in.tsv')
+y_train = read_data('train/expected.tsv')
+X_dev = read_data('dev-0/in.tsv')
+X_test = read_data('test-A/in.tsv')
+
+print("STEP 2 - SHUFFLE")
+data_train = list(zip(X_train, y_train))
+data_train = random.sample(data_train, 15000)
+
+
+print("STEP 3 - FINE TUNING")
+tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+
+train_encodings = tokenizer([text[0] for text in data_train], truncation=True, padding=True)
+train_dataset = DataWrapper(train_encodings, [int(text[1]) for text in data_train])
+
+model = AutoModelForSequenceClassification.from_pretrained("roberta-base", num_labels=2)
+args = TrainingArguments("model")
+
+device = torch.device("cpu")
+# device = torch.device("cuda")
+model.to(device)
+
+trainer = Trainer(model=model, args=args, train_dataset=train_dataset)
+trainer.train()
+
+print("STEP 4 - WRITE OUTPUT")
+wirte_output('train/out.tsv', X_train)
+wirte_output('dev-0/out.tsv', X_dev)
+wirte_output('test-A/out.tsv', X_test)

output_geval_fine.txt

@@ -0,0 +1,5 @@
+Likelihood	0.0000
+Accuracy	0.8253
+F1.0	0.7472
+Precision	0.7659
+Recall	0.7294