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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- text-classification |
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datasets: |
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- sst2 |
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metrics: |
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- accuracy |
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--- |
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## bert-base-uncased model fine-tuned on SST-2 |
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This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the linear layers contains **37%** of the original weights. |
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The model contains **51%** 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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<div class="graph"><script src="/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid/raw/main/model_card/density_info.js" id="2d0fc334-fe98-4315-8890-d6eaca1fa9be"></script></div> |
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In terms of perfomance, its **accuracy** is **91.17**. |
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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 task, and distilled from the model [textattack/bert-base-uncased-SST-2](https://huggingface.co/textattack/bert-base-uncased-SST-2). |
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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 method is that some of the attention heads are completely removed: 88 heads were removed on a total of 144 (61.1%). |
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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="/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid/raw/main/model_card/pruning_info.js" id="93b19d7f-c11b-4edf-9670-091e40d9be25"></script></div> |
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## Details of the SST-2 dataset |
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| Dataset | Split | # samples | |
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| -------- | ----- | --------- | |
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| SST-2 | train | 67K | |
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| SST-2 | eval | 872 | |
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### Results |
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**Pytorch model file size**: `351MB` (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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| **accuracy** | **91.17** | **92.7** | **-1.53**| |
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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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cls_pipeline = pipeline( |
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"text-classification", |
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model="echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid", |
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tokenizer="echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid", |
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) |
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print(f"Parameters count (includes only head pruning, no feed forward pruning)={int(cls_pipeline.model.num_parameters() / 1E6)}M") |
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cls_pipeline.model = optimize_model(cls_pipeline.model, "dense") |
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print(f"Parameters count after optimization={int(cls_pipeline.model.num_parameters() / 1E6)}M") |
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predictions = cls_pipeline("This restaurant is awesome") |
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print(predictions) |
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``` |
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