Allocation of 93763584 exceeds 10% of free system memory

🤗Transformers
1

I tried to use the question and answering model.

I used bert-base-uncased and distilbert-base-uncased-distilled-squad Both give an errors.

This is what I tried to code

from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering
import tensorflow as tf

tokenizer = DistilBertTokenizer.from_pretrained("bert-base-uncased")
model = TFDistilBertForQuestionAnswering.from_pretrained("bert-base-uncased")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

inputs = tokenizer(question, text, return_tensors="tf")
outputs = model(**inputs)

answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
tokenizer.decode(predict_answer_tokens)

Errors:

Error for distilbert-base-uncased-distilled-squad

Allocation of 93763584 exceeds 10% of free system memory.
All model checkpoint layers were used when initializing TFDistilBertForQuestionAnswering.

All the layers of TFDistilBertForQuestionAnswering were initialized from the model checkpoint at distilbert-base-uncased-distilled-squad.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertForQuestionAnswering for predictions without further training.

Error for bert-base-uncased

Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFDistilBertForQuestionAnswering: [‘mlm___cls’, ‘nsp___cls’, ‘bert’]

  • This IS expected if you are initializing TFDistilBertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
  • This IS NOT expected if you are initializing TFDistilBertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
    Some layers of TFDistilBertForQuestionAnswering were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: [‘distilbert’, ‘qa_outputs’, ‘dropout_37’]
    You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.