Adding model, graphs and metadata.

README.md

@@ -33,7 +33,7 @@ The model contains **43.0%** of the original weights **overall** (the embeddings
 With a simple resizing of the linear matrices it ran **1.96x as fast as bert-base-uncased** 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.
 
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 In terms of accuracy, its **F1 is 88.33**, compared with 88.5 for bert-base-uncased, a **F1 drop of 0.17**.
 
@@ -43,7 +43,7 @@ This model is case-insensitive: it does not make a difference between english an
 
 A side-effect of the block pruning is that some of the attention heads are completely removed: 55 heads were removed on a total of 144 (38.2%).
 Here is a detailed view on how the remaining heads are distributed in the network after pruning.
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 ## Details of the SQuAD1.1 dataset
 

model_card/density_info.js

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model_card/pruning_info.js

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