Add transformers fine-tuning

fine_tuning.py

@@ -0,0 +1,56 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
+import random
+import torch
+
+with open('train/in.tsv') as f:
+    data_train_X = f.readlines()
+
+with open('train/expected.tsv') as f:
+    data_train_Y = f.readlines()
+
+with open('dev-0/in.tsv') as f:
+    data_dev_X = f.readlines()
+
+with open('test-A/in.tsv') as f:
+    data_test_X = f.readlines()
+
+class CustomDataset(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)
+
+data_train = list(zip(data_train_X, data_train_Y))
+data_train = random.sample(data_train, 150000)
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+train_encodings = tokenizer([text[0] for text in data_train], truncation=True, padding=True)
+
+train_dataset = CustomDataset(train_encodings, [int(text[1]) for text in data_train])
+
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
+
+training_args = TrainingArguments("test_trainer")
+trainer = Trainer(
+    model=model, args=training_args, train_dataset=train_dataset)
+trainer.train()
+
+with open('train/out.tsv', 'w') as writer:
+    for result in trainer.predict(data_train_X):
+        writer.write(str(result) + '\n')
+
+with open('dev-0/out.tsv', 'w') as writer:
+    for result in trainer.predict(data_dev_X):
+        writer.write(str(result) + '\n')
+
+with open('test-A/out.tsv', 'w') as writer:
+    for result in trainer.predict(data_test_X):
+        writer.write(str(result) + '\n')
+