Fit GPT2 finetuning

run-fine-tuning.py

@@ -201,7 +201,7 @@ def predict_words(dataset):
         src = tokenizer.encode(text, return_tensors="pt", truncation=True).to(device)
         output = model.generate(src, max_length=len(src[0]) + 1, do_sample=True, top_k=0, temperature=0.8,
                                 num_return_sequences=1, no_repeat_ngram_size=2, output_scores=True)
-        probs, idxs = torch.softmax(output.scores[0][-1], dim=0).topk(50)
+        probs, idxs = torch.softmax(output.scores[0][-1], dim=0).topk(30)
         current_output = ''
         accumulated_probability = 0
         for prob, token_id in zip(probs, idxs):