csarron/roberta-base-squad-v1

RoBERTa-base fine-tuned on SQuAD v1

This model was fine-tuned from the HuggingFace RoBERTa base checkpoint on SQuAD1.1. This model is case-sensitive: it makes a difference between english and English.

Details

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:

  # 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_energy_squad.py \
    --model_type roberta \
    --model_name_or_path roberta-base \
    --do_train \
    --do_eval \
    --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/roberta-base-squad-v1 2>&1 | tee train-roberta-base-squad-v1.log
  

It took about 2 hours to finish.

Results

Model size: 477M

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

Example Usage

from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="csarron/roberta-base-squad-v1",
    tokenizer="csarron/roberta-base-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': 0.8625259399414062, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}

Created by Qingqing Cao | GitHub | Twitter

Made with ❤️ in New York.