Adding model, graphs and metadata.

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- text-classification
+datasets:
+- sst-2
+metrics:
+- accuracy
+---
+
+## bert-base-uncased model fine-tuned on SST-2
+
+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.
+
+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).
+
+<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>
+
+In terms of perfomance, its **accuracy** is **91.17**.
+
+## Fine-Pruning details
+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).
+This model is case-insensitive: it does not make a difference between english and English.
+
+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%).
+Here is a detailed view on how the remaining heads are distributed in the network after pruning.
+<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>
+
+## Details of the SST-2 dataset
+
+| Dataset  | Split | # samples |
+| -------- | ----- | --------- |
+|  SST-2   | train |    67K    |
+|  SST-2   | eval  |    872    |
+
+### Results
+
+**Pytorch model file size**: `351MB` (original BERT: `420MB`)
+
+| Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
+| ------ | --------- | --------- | --------- |
+| **accuracy** | **91.17** | **92.7** | **-1.53**|
+

config.json

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+{
+  "_name_or_path": "/tmp/tmp7fwsqmie",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "finetuning_task": "sst2",
+  "gradient_checkpointing": false,
+  "heads_count": 56,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
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+  "transformers_version": "4.4.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

eval/eval_results_sst2.json

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+{
+    "eval_accuracy": 0.911697247706422,
+    "eval_loss": 0.45370951294898987
+}

eval/sparsity_report.json

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