Create app.py

app.py

@@ -0,0 +1,29 @@
+from transformers import GTP2Tokenizer, TrainingArguments, Trainer, GPT2LMHeadModel
+from datasets import load_dataset
+
+dataset = load_dataset("sst2")
+
+for row in dataset['train']:
+    print(row)
+
+for i, row in enumerate(dataset):
+    prep_text = f"<|startoftext|> {rwo['sentence']}<|pad|>Sentiment: {rwo['label']}<|endoftext|>"
+    encodings_dict = tokenizer(prep_txt)
+
+tokenizer = GTP2Tokenizer.from_pretrained('gpt2', bos_token='<|startoftext|>', eos_token='<|endoftext|>', pad_token='<|pad|>')
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+train_args = TrainingArguments(output_dir='results', num_train_epochs = 1, warmup_steps =100, weight_decay = 0.01)
+
+Trainer(model='gpt2', args=train_args,train_dataset=train_dataset)
+
+model.eval()
+
+prompt = f'<|startoftext|>Tweet: {text}\nSentiment:'
+tokenizer_text = tokenizer(prompt, return_tensors="pt").input_ids
+output = model.generate(tokenized_text)
+predicted_text = tokenizer.decode(output)
+
+
+
+