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 ❤️ in New York.
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