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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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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 was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 8.0%** of the original weights. |
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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). |
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With a simple resizing of the linear matrices it ran **1.16x as fast as bert-base-uncased** on the evaluation. |
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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. |
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<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> |
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In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for bert-base-uncased, a **F1 drop of 0.39**. |
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## Fine-Pruning details |
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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) |
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This model is case-insensitive: it does not make a difference between english and English. |
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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%). |
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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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<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> |
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## Details of the SQuAD1.1 dataset |
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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**: `398MB` (original BERT: `420MB`) |
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| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation | |
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| ------ | --------- | --------- | --------- | |
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| **EM** | **80.94** | **80.8** | **+0.14**| |
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| **F1** | **88.11** | **88.5** | **-0.39**| |
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## Example Usage |
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Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns. |
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`pip install nn_pruning` |
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Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded. |
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```python |
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from transformers import pipeline |
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from nn_pruning.inference_model_patcher import optimize_model |
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qa_pipeline = pipeline( |
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"question-answering", |
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model="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1", |
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tokenizer="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1" |
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) |
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print("bert-base-uncased parameters: 152.0M") |
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print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M") |
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qa_pipeline.model = optimize_model(qa_pipeline.model, "dense") |
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print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M") |
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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", predictions) |
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``` |
