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csarron/mobilebert-uncased-squad-v1 csarron/mobilebert-uncased-squad-v1
433 downloads
last 30 days

pytorch

tf

Contributed by

csarron Qingqing Cao
5 models

How to use this model directly from the 馃/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("csarron/mobilebert-uncased-squad-v1") model = AutoModelForQuestionAnswering.from_pretrained("csarron/mobilebert-uncased-squad-v1")

MobileBERT fine-tuned on SQuAD v1

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 SQuAD1.1.

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:

    # 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
    
    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 \
      --train_file $SQUAD_DIR/train-v1.1.json \
      --predict_file $SQUAD_DIR/dev-v1.1.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 \
      --output_dir $SQUAD_DIR/mobilebert-uncased-warmup-squad_v1 2>&1 | tee train-mobilebert-warmup-squad_v1.log

It took about 3 hours to finish.

Results

Model size: 95M

Metric # Value # Original (Table 5)
EM 82.6 82.9
F1 90.0 90.0

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-v1",
    tokenizer="csarron/mobilebert-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': 0.7754058241844177, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}

Created by Qingqing Cao | GitHub | Twitter

Made with 鉂わ笍 in New York.