我正在尝试重新创建这些：Hugging Face: Question Answering Task和Hugging Face: Question Answering NLP Course。
我在**model.fit（）**部分遇到了这个ResourceExhaustedError。

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ResourceExhaustedError                    Traceback (most recent call last)
Cell In[14], line 1
----> 1 model.fit(x=tf_train_set, batch_size=16, validation_data=tf_validation_set, epochs=3, callbacks=[callback])

个字符

这里列出了一堆文件

Node: 'tf_distil_bert_for_question_answering/distilbert/transformer/layer_._4/attention/dropout_14/dropout/random_uniform/RandomUniform'
OOM when allocating tensor with shape[16,12,384,384] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
     [[{{node tf_distil_bert_for_question_answering/distilbert/transformer/layer_._4/attention/dropout_14/dropout/random_uniform/RandomUniform}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
 [Op:__inference_train_function_9297]

型
我已经尝试降低batch_size. model.fit(x=tf_train_set, batch_size=16, validation_data=tf_validation_set, epochs=3, callbacks=[callback])
我还尝试限制GPU的内存增长限制GPU内存增长
以下是colab笔记本电脑：Colab: Question Answering Task和Colab: Question Answering NLP Course