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
1# after install https://github.com/huggingface/transformers 2 3cd examples/question-answering 4mkdir -p data 5 6wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json 7 8wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json 9 10python run_squad.py \ 11 --model_type bert \ 12 --model_name_or_path bert-base-uncased \ 13 --do_train \ 14 --do_eval \ 15 --do_lower_case \ 16 --train_file train-v1.1.json \ 17 --predict_file dev-v1.1.json \ 18 --per_gpu_train_batch_size 12 \ 19 --per_gpu_eval_batch_size=16 \ 20 --learning_rate 3e-5 \ 21 --num_train_epochs 2.0 \ 22 --max_seq_length 320 \ 23 --doc_stride 128 \ 24 --data_dir data \ 25 --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)
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.