csarron/bert-base-uncased-squad-v1

BERT-base uncased model fine-tuned on SQuAD v1

This model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1. This model is case-insensitive: it does not make a difference between english and English.

Details

Dataset	Split	# samples
SQuAD1.1	train	90.6K
SQuAD1.1	eval	11.1k

Fine-tuning

• Python: 3.7.5

• Machine specs:

  CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz

  Memory: 32 GiB

  GPUs: 2 GeForce GTX 1070, each with 8GiB memory

  GPU driver: 418.87.01, CUDA: 10.1

• script:

  ```shell

after install https://github.com/huggingface/transformers

cd examples/question-answering mkdir -p data

wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json

wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json

python run_squad.py
--model_type bert
--model_name_or_path bert-base-uncased
--do_train
--do_eval
--do_lower_case
--train_file train-v1.1.json
--predict_file dev-v1.1.json
--per_gpu_train_batch_size 12
--per_gpu_eval_batch_size=16
--learning_rate 3e-5
--num_train_epochs 2.0
--max_seq_length 320
--doc_stride 128
--data_dir data
--output_dir data/bert-base-uncased-squad-v1 2>&1 | tee train-energy-bert-base-squad-v1.log

It took about 2 hours to finish.

### Results

**Model size**: `418M`

| Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))|
| ------ | --------- | --------- |
| **EM** | **80.9** | **80.8** |
| **F1** | **88.2** | **88.5** |

Note that the above results didn't involve any hyperparameter search.

## Example Usage


```python
from transformers import pipeline

qa_pipeline = pipeline(
  "question-answering",
  model="csarron/bert-base-uncased-squad-v1",
  tokenizer="csarron/bert-base-uncased-squad-v1"
)

predictions = qa_pipeline({
  'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
  'question': "What day was the game played on?"
})

print(predictions)
# output:
# {'score': 0.8730505704879761, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}

Created by Qingqing Cao | GitHub | Twitter

Made with ❤️ in New York.