readme: ---
  language: en
  datasets:
  - squad
  ---
  
  # MobileBERT + SQuAD (v1.1) 📱❓
  
  [mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) fine-tuned on [SQUAD v2.0 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) 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](https://arxiv.org/abs/2004.02984)
  
  ## Details of the downstream task (Q&A) - Dataset 📚
  
  **S**tanford **Q**uestion **A**nswering **D**ataset (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:
  
  ```bash
  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**:
  
  ```python
  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](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
  
  > Made with <span style="color: #e25555;">&hearts;</span> in Spain
  