improve comments for examples

src/transformers/modeling_utils.py

@@ -614,7 +614,7 @@ class PreTrainedModel(nn.Module):
             model = AutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
             input_context = 'The dog'
             input_ids = torch.tensor(tokenizer.encode(input_context)).unsqueeze(0)  # encode input context
-            outputs = model.generate(input_ids=input_ids, do_sample=True, num_beams=5, num_return_sequences=3)  # generate 3 independent sequences using beam search decoding (5 beams) from initial context 'The dog'
+            outputs = model.generate(input_ids=input_ids, do_sample=True, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
             for i in range(3): #  3 output sequences were generated
                 print('Generated {}: {}'.format(i, tokenizer.decode(outputs[0][i], skip_special_tokens=True)))
 
@@ -622,7 +622,7 @@ class PreTrainedModel(nn.Module):
             model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
             input_context = 'The dog'
             input_ids = torch.tensor(tokenizer.encode(input_context)).unsqueeze(0)  # encode input context
-            outputs = model.generate(input_ids=input_ids, max_length=40, do_sample=True, temperature=0.7, bos_token_id=tokenizer.bos_token_id, eos_token_ids=tokenizer.eos_token_id, num_beams=3)  # generate sequences using beam search decoding (3 beams)
+            outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, bos_token_id=tokenizer.bos_token_id, eos_token_ids=tokenizer.eos_token_id, num_beams=3)  # generate sequences using greedy beam search decoding (3 beams)
             print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
 
             tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer