Test a fine-tuned BERT-QA model

I have fine-tuned a BERT-QA model on SQuAD and it produced a pytorch_model.bin file. Now, I want to load this fine-tuned model and evaluate on SQuAD. How can I do that? I am using the run_squad.py script.

[wasiahmad]

I noticed the following snippet in the code. (which I have edited to solve my problem)

if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
    # Save a trained model, configuration and tokenizer
    model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self

    # If we save using the predefined names, we can load using `from_pretrained`
    output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
    output_config_file = os.path.join(args.output_dir, CONFIG_NAME)

    torch.save(model_to_save.state_dict(), output_model_file)
    model_to_save.config.to_json_file(output_config_file)
    tokenizer.save_vocabulary(args.output_dir)

    # Load a trained model and vocabulary that you have fine-tuned
    model = BertForQuestionAnswering.from_pretrained(args.output_dir)
    tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
else:
    model = BertForQuestionAnswering.from_pretrained(args.bert_model)

So, if we want to load the fine-tuned model only for prediction, need to load it from args.output_dir. But the current code loads from args.bert_model when we use squad.py only for prediction.

[smartcatdog]

@Swathygsb https://github.com/kamalkraj/BERT-SQuAD inference on bert-squad model

thx for your sharing, and there is inference on bert-squad model by tensorflow? 3Q~