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  # SpanBERT (spanbert-base-cased) fine-tuned on SQuAD v1.1
  
  
  [SpanBERT](https://github.com/facebookresearch/SpanBERT) created by [Facebook Research](https://github.com/facebookresearch) and fine-tuned on [SQuAD 1.1](https://rajpurkar.github.io/SQuAD-explorer/) for **Q&A** downstream task.
  
  ## Details of SpanBERT
  
   A pre-training method that is designed to better represent and predict spans of text.
  
  [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://arxiv.org/abs/1907.10529)
  
  ## Details of the downstream task (Q&A) - Dataset
  
  [SQuAD 1.1](https://rajpurkar.github.io/SQuAD-explorer/) contains 100,000+ question-answer pairs on 500+ articles.
  
  | Dataset  | Split | # samples |
  | -------- | ----- | --------- |
  | SQuAD1.1 | train | 87.7k     |
  | SQuAD1.1 | eval  | 10.6k     |
  
  ## Model training
  
  The model was trained on a Tesla P100 GPU and 25GB of RAM.
  The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
  
  ## Results:
  
  | Metric | # Value   |
  | ------ | --------- |
  | **EM** | **85.49** |
  | **F1** | **91.98** |
  
  ### Raw metrics:
  
  ```json
  {
    "exact": 85.49668874172185,
    "f1": 91.9845699540379,
    "total": 10570,
    "HasAns_exact": 85.49668874172185,
    "HasAns_f1": 91.9845699540379,
    "HasAns_total": 10570,
    "best_exact": 85.49668874172185,
    "best_exact_thresh": 0.0,
    "best_f1": 91.9845699540379,
    "best_f1_thresh": 0.0
  }
  ```
  
  ## Comparison:
  
  | Model                                                                                     | EM        | F1 score  |
  | ----------------------------------------------------------------------------------------- | --------- | --------- |
  | [SpanBert official repo](https://github.com/facebookresearch/SpanBERT#pre-trained-models) | -         | 92.4\* |
  | [spanbert-finetuned-squadv1](https://huggingface.co/mrm8488/spanbert-finetuned-squadv1)   | **85.49** | **91.98** |
  
  ## Model in action
  
  Fast usage with **pipelines**:
  
  ```python
  from transformers import pipeline
  
  qa_pipeline = pipeline(
      "question-answering",
      model="mrm8488/spanbert-finetuned-squadv1",
      tokenizer="mrm8488/spanbert-finetuned-squadv1"
  )
  
  qa_pipeline({
      'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
      'question': "Who has been working hard for hugginface/transformers lately?"
  
  })
  ```
  
  > 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
  