squeezebert-finetuned-squadv1

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 🧾

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 <span style="color: #e25555;">♥</span> in Spain