language: en
datasets:
- squad
SqueezeBERT + SQuAD (v1.1)
squeezebert-uncased fine-tuned on SQUAD v1.1 for Q&A downstream task.
Details of SqueezeBERT
This model, squeezebert-uncased, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in this paper. This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with grouped convolutions.
The authors found that SqueezeBERT is 4.3x faster than bert-base-uncased on a Google Pixel 3 smartphone.
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 /content/transformers/examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path squeezebert/squeezebert-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 15 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/output_dir \
--overwrite_output_dir \
--save_steps 2000
Test set Results 🧾
| Metric | # Value |
|---|---|
| EM | 76.66 |
| F1 | 85.83 |
Model Size: 195 MB
Model in action 🚀
Fast usage with pipelines:
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/squeezebert-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': 0.6988425850868225, 'start': 96}
Created by Manuel Romero/@mrm8488 | LinkedIn
Made with ♥ in Spain