echarlaix
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Adding graph.

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  1. README.md +2 -2
  2. model_card/density_info.js +4 -17
README.md CHANGED
@@ -15,7 +15,7 @@ This model was created using the [nn_pruning](https://github.com/huggingface/nn_
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  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).
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- <div class="graph"><script src="/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid/raw/main/model_card/density_info.js" id="ca8aceb6-0975-4d02-b19f-4c1a5df8bf71"></script></div>
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  In terms of perfomance, its **accuracy** is **91.17**.
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@@ -25,7 +25,7 @@ This model is case-insensitive: it does not make a difference between english an
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  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%).
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  Here is a detailed view on how the remaining heads are distributed in the network after pruning.
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  ## Details of the SST-2 dataset
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  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).
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+ <div class="graph"><script src="/echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid/raw/main/model_card/density_info.js" id="2d0fc334-fe98-4315-8890-d6eaca1fa9be"></script></div>
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  In terms of perfomance, its **accuracy** is **91.17**.
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  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%).
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  Here is a detailed view on how the remaining heads are distributed in the network after pruning.
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+ <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>
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  ## Details of the SST-2 dataset
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model_card/density_info.js CHANGED
@@ -16,9 +16,9 @@
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119
+ var render_items = [{"docid":"26a004c6-8fab-4f02-8b46-12f868198a5e","root_ids":["1108"],"roots":{"1108":"2d0fc334-fe98-4315-8890-d6eaca1fa9be"}}];
 
 
 
 
 
 
 
 
 
 
 
 
 
120
  root.Bokeh.embed.embed_items(docs_json, render_items);
121
 
122
  }