Edit model card

MobileBERT fine-tuned on SQuAD v2

MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.

This model was fine-tuned from the HuggingFace checkpoint google/mobilebert-uncased on SQuAD2.0.

Details

Dataset Split # samples
SQuAD2.0 train 130k
SQuAD2.0 eval 12.3k

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-v2.0.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json
    
    wget -O data/dev-v2.0.json  https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json
    
    export SQUAD_DIR=`pwd`/data
    
    python run_squad.py \
      --model_type mobilebert \
      --model_name_or_path google/mobilebert-uncased \
      --do_train \
      --do_eval \
      --do_lower_case \
      --version_2_with_negative \
      --train_file $SQUAD_DIR/train-v2.0.json \
      --predict_file $SQUAD_DIR/dev-v2.0.json \
      --per_gpu_train_batch_size 16 \
      --per_gpu_eval_batch_size 16 \
      --learning_rate 4e-5 \
      --num_train_epochs 5.0 \
      --max_seq_length 320 \
      --doc_stride 128 \
      --warmup_steps 1400 \
      --save_steps 2000 \
      --output_dir $SQUAD_DIR/mobilebert-uncased-warmup-squad_v2 2>&1 | tee train-mobilebert-warmup-squad_v2.log
    

It took about 3.5 hours to finish.

Results

Model size: 95M

Metric # Value # Original (Table 5)
EM 75.2 76.2
F1 78.8 79.2

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

Example Usage

from transformers import pipeline

qa_pipeline = pipeline(
    "question-answering",
    model="csarron/mobilebert-uncased-squad-v2",
    tokenizer="csarron/mobilebert-uncased-squad-v2"
)

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': 0.71434086561203, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}

Created by Qingqing Cao | GitHub | Twitter

Made with ❤️ in New York.

Downloads last month
38,562
Safetensors
Model size
24.6M params
Tensor type
F32
·
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train csarron/mobilebert-uncased-squad-v2