readme: ---
  language: en
  thumbnail:
  license: mit
  tags:
  - question-answering
  - 
  - 
  datasets:
  - squad
  metrics:
  - squad
  widget:
  - text: "Where is the Eiffel Tower located?"
    context: "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower."
  - text: "Who is Frederic Chopin?"
    context: "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano."
  ---
  
  ## BERT-base uncased model fine-tuned on SQuAD v1
  
  This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 8.0%** of the original  weights.
  
  
  
  The model contains **28.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
  
  With a simple resizing of the linear matrices it ran **1.16x as fast as bert-base-uncased** on the evaluation.
  This is possible because the pruning method lead to structured matrices: to visualize them, hover below on the plot to see the non-zero/zero parts of each matrix.
  
  <div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/density_info.js" id="c60d09ec-81ff-4d6f-b616-c3ef09b2175d"></script></div>
  
  In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for bert-base-uncased, a **F1 drop of 0.39**.
  
  ## Fine-Pruning details
  This model was fine-tuned from the HuggingFace [model](https://huggingface.co/bert-base-uncased) checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad)
  This model is case-insensitive: it does not make a difference between english and English.
  
  A side-effect of the block pruning is that some of the attention heads are completely removed: 22 heads were removed on a total of 144 (15.3%).
  Here is a detailed view on how the remaining heads are distributed in the network after pruning.
  <div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/pruning_info.js" id="55528c8b-d5f5-46a5-a35a-dad93725f7e5"></script></div>
  
  ## Details of the SQuAD1.1 dataset
  
  | Dataset  | Split | # samples |
  | -------- | ----- | --------- |
  | SQuAD1.1 | train | 90.6K      |
  | SQuAD1.1 | eval  | 11.1k     |
  
  ### Fine-tuning
  - Python: `3.8.5`
  
  - Machine specs:
  
  ```CPU: Intel(R) Core(TM) i7-6700K CPU
  Memory: 64 GiB
  GPUs: 1 GeForce GTX 3090, with 24GiB memory
  GPU driver: 455.23.05, CUDA: 11.1
  ```
  
  ### Results
  
  **Pytorch model file size**: `398MB` (original BERT: `420MB`)
  
  | Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
  | ------ | --------- | --------- | --------- |
  | **EM** | **80.94** | **80.8** | **+0.14**|
  | **F1** | **88.11** | **88.5** | **-0.39**|
  
  ## Example Usage
  Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
  
  `pip install nn_pruning`
  
  Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded.
  
  ```python
  from transformers import pipeline
  from nn_pruning.inference_model_patcher import optimize_model
  
  qa_pipeline = pipeline(
      "question-answering",
      model="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1",
      tokenizer="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1"
  )
  
  print("bert-base-uncased parameters: 152.0M")
  print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
  qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
  
  print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
  predictions = qa_pipeline({
      'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
      'question': "Who is Frederic Chopin?",
  })
  print("Predictions", predictions)
  ```