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--- |
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language: en |
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thumbnail: |
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license: mit |
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tags: |
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- question-answering |
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- bert |
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- bert-base |
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datasets: |
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- squad |
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metrics: |
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- squad |
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widget: |
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- text: "Where is the Eiffel Tower located?" |
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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." |
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- text: "Who is Frederic Chopin?" |
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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." |
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--- |
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## BERT-base uncased model fine-tuned on SQuAD v1 |
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This model is block sparse: the **linear** layers contains **31.7%** of the original weights. |
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The model contains **47.0%** of the original weights **overall**. |
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The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method. |
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That means that with the [block-sparse](https://github.com/huggingface/pytorch_block_sparse) runtime it ran **1.12x** faster than an dense networks on the evaluation, at the price of some impact on the accuracy (see below). |
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This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the equivalent model [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1). |
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This model is case-insensitive: it does not make a difference between english and English. |
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## Pruning details |
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A side-effect of the block pruning is that some of the attention heads are completely removed: 80 heads were removed on a total of 144 (55.6%). |
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Here is a detailed view on how the remaining heads are distributed in the network after pruning. |
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## Density plot |
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<script src="/madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1/raw/main/model_card/density.js" id="79005f4a-723c-4bf8-bc7f-5ad11676be6c"></script> |
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## Details |
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| Dataset | Split | # samples | |
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| -------- | ----- | --------- | |
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| SQuAD1.1 | train | 90.6K | |
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| SQuAD1.1 | eval | 11.1k | |
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### Fine-tuning |
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- Python: `3.8.5` |
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- Machine specs: |
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```CPU: Intel(R) Core(TM) i7-6700K CPU |
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Memory: 64 GiB |
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GPUs: 1 GeForce GTX 3090, with 24GiB memory |
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GPU driver: 455.23.05, CUDA: 11.1 |
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``` |
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### Results |
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**Pytorch model file size**: `355M` (original BERT: `438M`) |
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| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| |
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| ------ | --------- | --------- | |
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| **EM** | **79.04** | **80.8** | |
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| **F1** | **86.70** | **88.5** | |
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## Example Usage |
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```python |
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from transformers import pipeline |
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qa_pipeline = pipeline( |
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"question-answering", |
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model="madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1", |
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tokenizer="madlag/bert-base-uncased-squad1.1-block-sparse-0.32-v1" |
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) |
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predictions = qa_pipeline({ |
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'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.", |
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'question': "Who is Frederic Chopin?", |
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}) |
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print(predictions) |
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``` |
