问'Seq2SeqModelOutput‘对象没有属性'logits’BART转换器

EN

bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
bart_model = BartModel.from_pretrained("facebook/bart-large", return_dict=True)

# generate chunks of text \ sentences <= 1024 tokens
def nest_sentences(document):
  nested = []
  sent = []
  length = 0
  for sentence in nltk.sent_tokenize(document):
    length += len(sentence)
    if length < 1024:
      sent.append(sentence)
    else:
      nested.append(sent)
      sent = [sentence]
      length = len(sentence)

  if sent:
    nested.append(sent)
  return nested

# generate summary on text with <= 1024 tokens
def generate_summary(nested_sentences):
  device = 'cuda'
  summaries = []
  for nested in nested_sentences:
    input_tokenized = bart_tokenizer.encode(' '.join(nested), truncation=True, return_tensors='pt')
    input_tokenized = input_tokenized.to(device)
    summary_ids = bart_model.to(device).generate(
        input_tokenized,
        length_penalty=3.0,
        min_length=30,
        max_length=100,
    )
    output = [bart_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids]
    summaries.append(output)
  summaries = [sentence for sublist in summaries for sentence in sublist]
  return summaries

nested_sentences = nest_sentences(text)

summary = generate_summary(nested_sentences)

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-15-d5aa7709bb5f> in <module>()
----> 1 summary = generate_summary(nested_sentences)

3 frames

<ipython-input-11-8554509269e0> in generate_summary(nested_sentences)
     28         length_penalty=3.0,
     29         min_length=30,
---> 30         max_length=100,
     31     )
     32     output = [bart_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids]

/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
     26         def decorate_context(*args, **kwargs):
     27             with self.__class__():
---> 28                 return func(*args, **kwargs)
     29         return cast(F, decorate_context)
     30 

/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in generate(self, input_ids, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, encoder_no_repeat_ngram_size, num_return_sequences, max_time, max_new_tokens, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, remove_invalid_values, synced_gpus, **model_kwargs)
   1061                 return_dict_in_generate=return_dict_in_generate,
   1062                 synced_gpus=synced_gpus,
-> 1063                 **model_kwargs,
   1064             )
   1065 

/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in beam_search(self, input_ids, beam_scorer, logits_processor, stopping_criteria, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, **model_kwargs)
   1799                 continue  # don't waste resources running the code we don't need
   1800 
-> 1801             next_token_logits = outputs.logits[:, -1, :]
   1802 
   1803             # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`

AttributeError: 'Seq2SeqModelOutput' object has no attribute 'logits'