Adding graphics.

README.md

@@ -19,16 +19,13 @@ widget:
 
 ## BERT-base uncased model fine-tuned on SQuAD v1
 
-This model is block sparse: the **linear** layers contains **64.0%** of the original weights.
+This model was created using the [nn_pruning](https://githbu.com/huggingface/nn_pruning) python library: the **linear** layers contains **64.0%** of the original weights.
 
 
 The model contains **36.0%** of the original weights **overall**.
 
-The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method.
-
-That means that with the [block-sparse](https://github.com/huggingface/pytorch_block_sparse) runtime it ran **1.84x** faster than an dense networks on the evaluation, at the price of some impact on the accuracy (see below).
-
-
+That means that with the [block-sparse](https://github.com/huggingface/pytorch_block_sparse) runtime it ran **1.84x as fast as BERT-base** on the evaluation, while its F1 is
+88.72, compared with 88.5 for BERT-base, a **F1 gain of 0.22**.
 
 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 equivalent model [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1).
 This model is case-insensitive: it does not make a difference between english and English.
@@ -38,9 +35,10 @@ A side-effect of the block pruning is that some of the attention heads are compl
 
 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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/pruning_info.js" id="f7197e6f-ccea-4a42-8fb9-7a8fe7136cf0"></script></div>
+<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/pruning_info.js" id="b1676015-bd1f-421f-9c37-1ae60c623ec3"></script></div>
 
 ## Density plot
+<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/density_info.js" id="1b3cca51-0673-438c-84f3-7abe6f955faa"></script></div>
 
 
 ## Details
@@ -66,10 +64,10 @@ GPU driver: 455.23.05, CUDA: 11.1
 
 **Pytorch model file size**: `379M` (original BERT: `438M`)
 
-| Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))|
-| ------ | --------- | --------- |
-| **EM** | **81.69** | **80.8** |
-| **F1** | **88.72** | **88.5** |
+| Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
+| ------ | --------- | --------- | --------- |
+| **EM** | **81.69** | **80.8** | **+0.89**|
+| **F1** | **88.72** | **88.5** | **+0.22**|
 
 ## Example Usage
 

model_card/density_info.js

@@ -0,0 +1,174 @@
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