Adding modes, graphs and metadata.

README.md

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+---
+language: en
+thumbnail:
+license: mit
+tags:
+- question-answering
+- bert
+- bert-base
+datasets:
+- squad
+metrics:
+- squad
+widget:
+- text: "Where is the Eiffel Tower located?"
+  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."
+- text: "Who is Frederic Chopin?"
+  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."
+---
+
+## BERT-base uncased model fine-tuned on SQuAD v1
+
+This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 15.0%** of the original weights.
+
+
+
+The model contains **34.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
+
+With a simple resizing of the linear matrices it ran **2.32x as fast as BERT-base** on the evaluation.
+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.
+
+<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1/raw/main/model_card/density_info.js" id="fd51557a-ad63-4088-bc25-67d39b0c0b2c"></script></div>
+
+In terms of accuracy, its **F1 is 86.64**, compared with 88.5 for BERT-base, a **F1 drop of 1.86**.
+
+## Fine-Pruning details
+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 model [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad).
+This model is case-insensitive: it does not make a difference between english and English.
+
+A side-effect of the block pruning is that some of the attention heads are completely removed: 63 heads were removed on a total of 144 (43.8%).
+Here is a detailed view on how the remaining heads are distributed in the network after pruning.
+<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1/raw/main/model_card/pruning_info.js" id="2531d97a-f550-49d6-9b4e-8d344db20f2b"></script></div>
+
+## Details of the SQuAD1.1 dataset
+
+| Dataset  | Split | # samples |
+| -------- | ----- | --------- |
+| SQuAD1.1 | train | 90.6K      |
+| SQuAD1.1 | eval  | 11.1k     |
+
+### Fine-tuning
+- Python: `3.8.5`
+
+- Machine specs:
+
+```CPU: Intel(R) Core(TM) i7-6700K CPU
+Memory: 64 GiB
+GPUs: 1 GeForce GTX 3090, with 24GiB memory
+GPU driver: 455.23.05, CUDA: 11.1
+```
+
+### Results
+
+**Pytorch model file size**: `368M` (original BERT: `438M`)
+
+| Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
+| ------ | --------- | --------- | --------- |
+| **EM** | **78.77** | **80.8** | **-2.03**|
+| **F1** | **86.64** | **88.5** | **-1.86**|
+
+## Example Usage
+Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
+
+`pip install nn_pruning`
+
+Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded.
+
+```python
+from transformers import pipeline
+from nn_pruning.inference_model_patcher import optimize_model
+
+qa_pipeline = pipeline(
+    "question-answering",
+    model="madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1",
+    tokenizer="madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1"
+)
+
+print("BERT-base parameters: 110M")
+print(f"Parameters count (includes head pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
+qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
+
+print(f"Parameters count after optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
+predictions = qa_pipeline({
+    '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.",
+    'question': "Who is Frederic Chopin?",
+})
+print("Predictions", predictions)
+```

config.json

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+{
+  "_name_or_path": "/tmp/tmpspdgp5f3",
+  "architectures": [
+    "BertForQuestionAnswering"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "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",
+  "pruned_heads": {
+    "0": [
+      0,
+      2,
+      4,
+      5,
+      6,
+      7,
+      11
+    ],
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+    "11": [
+      0,
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+      10,
+      11
+    ]
+  },
+  "transformers_version": "4.4.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model_card/density_info.js

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