language: en
datasets:
- squad
MobileBERT + SQuAD (v1.1) 📱❓
mobilebert-uncased fine-tuned on SQUAD v2.0 dataset for Q&A downstream task.
Details of the downstream task (Q&A) - Model 🧠
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.
The checkpoint used here is the original MobileBert Optimized Uncased English: (uncased_L-24_H-128_B-512_A-4_F-4_OPT) checkpoint.
More about the model here
Details of the downstream task (Q&A) - Dataset 📚
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD v1.1 contains 100,000+ question-answer pairs on 500+ articles.
Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
python transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path 'google/mobilebert-uncased' \
--do_eval \
--do_train \
--do_lower_case \
--train_file '/content/dataset/train-v1.1.json' \
--predict_file '/content/dataset/dev-v1.1.json' \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 5 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir '/content/output' \
--overwrite_output_dir \
--save_steps 1000
It is important to say that this models converges much faster than other ones. So, it is also cheap to fine-tune.
Test set Results 🧾
| Metric | # Value |
|---|---|
| EM | 82.33 |
| F1 | 89.64 |
| Size | 94 MB |
Model in action 🚀
Fast usage with pipelines:
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/mobilebert-uncased-finetuned-squadv1')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'Who did identified it ?'
})
# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.7885545492172241, 'start': 96}
Created by Manuel Romero/@mrm8488 | LinkedIn
Made with ♥ in Spain