python - 保存微调的 Tensorflow 模型时列出超出范围的索引

Question

我正在尝试使用 Tensorflow 微调来自 Huggingface 的预训练 BERT 模型。一切运行顺利，模型构建和训练没有错误。但是，当我尝试保存模型时，它会因错误“IndexError:列表索引超出范围”而停止。我正在使用带有 TPU 的 Google Colab。

如有任何帮助，我们将不胜感激!

代码:

import tensorflow as tf
from tensorflow.keras import activations, optimizers, losses
from transformers import TFBertModel

def create_model(max_sequence, model_name, num_labels):
    bert_model = TFBertModel.from_pretrained(model_name)
    input_ids = tf.keras.layers.Input(shape=(max_sequence,), dtype=tf.int32, name='input_ids')
    attention_mask = tf.keras.layers.Input((max_sequence,), dtype=tf.int32, name='attention_mask')
    output = bert_model([input_ids, attention_mask])[0]
    output = output[:, 0, :]
    output = tf.keras.layers.Dense(num_labels, activation='sigmoid')(output)
    model = tf.keras.models.Model(inputs=[input_ids, attention_mask], outputs=output)
    return model

with strategy.scope():
  model = create_model(20, 'bert-base-uncased', 1)
  opt = optimizers.Adam(learning_rate=3e-5)
  loss = 'binary_crossentropy'
  model.compile(optimizer=opt, loss=loss, metrics=['accuracy'])

model.fit(tfdataset_train, batch_size=32, epochs=2)
SAVE_PATH = 'path/to/save/location'
model.save(SAVE_PATH)

错误:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-22-255116b49022> in <module>()
      1 SAVE_PATH = 'path/to/save/location'
----> 2 model.save(SAVE_PATH,save_format='tf')

50 frames
/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py in input_processing(func, config, input_ids, **kwargs)
    372                     output[tensor_name] = input
    373                 else:
--> 374                     output[parameter_names[i]] = input
    375             elif isinstance(input, allowed_types) or input is None:
    376                 output[parameter_names[i]] = input

IndexError: list index out of range

用形状绘制的模型: Tensorflow Model

Answer 1

解决办法是改变:

output = bert_model([input_ids, attention_mask])[0]

到

output = bert_model.bert([input_ids, attention_mask])[0]

引用:https://github.com/huggingface/transformers/issues/3627

我对您发布的解决方案投了赞成票，但后来我在训练时发现模型存在问题。它收敛不好。