# Bert-large infer

### Fine-tuning BERT on SQuAD1.0

The [`run_qa.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa.py) script
allows to fine-tune any model from our [hub](https://huggingface.co/models) (as long as its architecture has a `ForQuestionAnswering` version in the library) on a question-answering dataset (such as SQuAD, or any other QA dataset available in the `datasets` library, or your own csv/jsonlines files) as long as they are structured the same way as SQuAD. You might need to tweak the data processing inside the script if your data is structured differently.

**Note:** This script only works with models that have a fast tokenizer (backed by the 🤗 Tokenizers library) as it
uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
[this table](https://huggingface.co/transformers/index.html#supported-frameworks), if it doesn't you can still use the old version of the script which can be found [here](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering).

Note that if your dataset contains samples with no possible answers (like SQuAD version 2), you need to pass along the flag `--version_2_with_negative`.

- [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
- [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
- [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
- This fine-tuned model is available as a checkpoint under the reference [`bert-large-uncased-whole-word-masking-finetuned-squad`](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad).

This example code fine-tunes BERT on the SQuAD1.0 dataset.

```bash
python /nx/transformers/examples/pytorch/question-answering/run_qa.py \
  --model_name_or_path /models/google-bert/bert-large-uncased-whole-word-masking-finetuned-squad \
  --dataset_name squad \
  --do_train \
  --do_eval \
  --per_device_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /nx/transformers/debug_squad/
```

Training with the previously defined hyper-parameters yields the following results:

```bash
f1 = 92.56
exact_match = 85.74
```

### 多卡推理

```
bash fine-tuning_SQuAD1.0.sh
```

### result
