Adding modes, graphs and metadata.
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README.md
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@@ -28,7 +28,7 @@ The model contains **28.0%** of the original weights **overall** (the embeddings
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With a simple resizing of the linear matrices it ran **1.16x as fast as BERT-base** on the evaluation.
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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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<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/density_info.js" id="
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In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for BERT-base, a **F1 drop of 0.39**.
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A side-effect of the block pruning is that some of the attention heads are completely removed: 22 heads were removed on a total of 144 (15.3%).
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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="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/pruning_info.js" id="
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## Details of the SQuAD1.1 dataset
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With a simple resizing of the linear matrices it ran **1.16x as fast as BERT-base** on the evaluation.
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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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<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/density_info.js" id="14cf4013-d609-473e-a62b-6f7429a41f44"></script></div>
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In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for BERT-base, a **F1 drop of 0.39**.
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A side-effect of the block pruning is that some of the attention heads are completely removed: 22 heads were removed on a total of 144 (15.3%).
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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="/madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1/raw/main/model_card/pruning_info.js" id="22413187-104d-479c-9a91-8249ba12fe4e"></script></div>
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## Details of the SQuAD1.1 dataset
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model_card/density_info.js
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if (element == null) {
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console.warn("Bokeh: autoload.js configured with elementid '
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}
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function embed_document(root) {
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var render_items = [{"docid":"63c71560-6caa-4f60-b5e0-9a1be29aeb92","root_ids":["1095"],"roots":{"1095":"14cf4013-d609-473e-a62b-6f7429a41f44"}}];
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| 120 |
root.Bokeh.embed.embed_items(docs_json, render_items);
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}
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model_card/images/layer_0_attention_self_query.png
CHANGED
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model_card/pruning_info.js
CHANGED
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@@ -16,9 +16,9 @@
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-
var element = document.getElementById("
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| 20 |
if (element == null) {
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| 21 |
-
console.warn("Bokeh: autoload.js configured with elementid '
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| 22 |
}
|
| 23 |
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| 24 |
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@@ -115,8 +115,8 @@
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| 115 |
(function(root) {
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| 116 |
function embed_document(root) {
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| 117 |
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| 118 |
-
var docs_json = '{"
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| 119 |
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var render_items = [{"docid":"
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| 120 |
root.Bokeh.embed.embed_items(docs_json, render_items);
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| 121 |
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}
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| 16 |
|
| 17 |
|
| 18 |
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| 19 |
+
var element = document.getElementById("22413187-104d-479c-9a91-8249ba12fe4e");
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if (element == null) {
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console.warn("Bokeh: autoload.js configured with elementid '22413187-104d-479c-9a91-8249ba12fe4e' but no matching script tag was found.")
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| 22 |
}
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| 23 |
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| 24 |
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| 115 |
(function(root) {
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| 116 |
function embed_document(root) {
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| 117 |
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| 118 |
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var docs_json = 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var render_items = [{"docid":"e28b944a-16ba-49cc-a930-4adf014f113a","root_ids":["1001"],"roots":{"1001":"22413187-104d-479c-9a91-8249ba12fe4e"}}];
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root.Bokeh.embed.embed_items(docs_json, render_items);
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