echarlaix
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Adding model, graphs and metadata.

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  1. README.md +44 -0
  2. config.json +140 -0
  3. eval/eval_results_sst2.json +4 -0
  4. eval/sparsity_report.json +1 -0
  5. model_card/density_info.js +174 -0
  6. model_card/images/layer_0_attention_output_dense.png +0 -0
  7. model_card/images/layer_0_attention_self_key.png +0 -0
  8. model_card/images/layer_0_attention_self_query.png +0 -0
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  11. model_card/images/layer_0_output_dense.png +0 -0
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  14. model_card/images/layer_10_attention_self_query.png +0 -0
  15. model_card/images/layer_10_attention_self_value.png +0 -0
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  18. model_card/images/layer_11_attention_output_dense.png +0 -0
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  20. model_card/images/layer_11_attention_self_query.png +0 -0
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  28. model_card/images/layer_1_intermediate_dense.png +0 -0
  29. model_card/images/layer_1_output_dense.png +0 -0
  30. model_card/images/layer_2_attention_output_dense.png +0 -0
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  32. model_card/images/layer_2_attention_self_query.png +0 -0
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  34. model_card/images/layer_2_intermediate_dense.png +0 -0
  35. model_card/images/layer_2_output_dense.png +0 -0
  36. model_card/images/layer_3_attention_output_dense.png +0 -0
  37. model_card/images/layer_3_attention_self_key.png +0 -0
  38. model_card/images/layer_3_attention_self_query.png +0 -0
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  50. model_card/images/layer_5_attention_self_query.png +0 -0
README.md ADDED
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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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+ - sst-2
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+ metrics:
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+ - accuracy
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+ ---
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+
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+ ## bert-base-uncased model fine-tuned on SST-2
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+
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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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+
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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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+
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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="3f521877-7c9e-4846-9411-2b1d61d3b9ab"></script></div>
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+
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+ In terms of perfomance, its **accuracy** is **91.17**.
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+
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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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+
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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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+
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+ ## Details of the SST-2 dataset
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+
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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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+
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+ ### Results
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+
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+ **Pytorch model file size**: `351MB` (original BERT: `420MB`)
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+
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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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+
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+ "num_attention_heads": 12,
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