Adding model, graphs and metadata.

README.md

@@ -28,7 +28,7 @@ The model contains **24.0%** of the original weights **overall** (the embeddings
 With a simple resizing of the linear matrices it ran **2.63x as fast as bert-large-uncased-whole-word-masking** 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 82.57**, compared with 85.85 for , a **F1 drop of 3.28**.
 
@@ -38,7 +38,7 @@ 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: 190 heads were removed on a total of 384 (49.5%).
 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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