Changing legend.
Browse files- README.md +5 -4
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- model_card/pruning_info.js +4 -4
README.md
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@@ -23,11 +23,12 @@ This model was created using the [nn_pruning](https://github.com/huggingface/nn_
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The model contains **36.0%** of the original weights **overall** (remaining weights are mostly in the input/ouput embeddings).
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88.72, compared with 88.5 for BERT-base, a **F1 gain of 0.22**.
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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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/density_info.js" id="
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## Fine-Pruning details
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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).
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A side-effect of the block pruning is that some of the attention heads are completely removed: 48 heads were removed on a total of 144 (33.3%).
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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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/pruning_info.js" id="
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## Details of the SQuAD1.1 dataset
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The model contains **36.0%** of the original weights **overall** (remaining weights are mostly in the input/ouput embeddings).
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With a simple resizing of the linear matrices it ran **1.84x 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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/density_info.js" id="6a33cbcb-db1a-4e7a-a6dc-1c353babcf55"></script></div>
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In terms of accuracy, its **F1 is 88.72**, compared with 88.5 for BERT-base, a **F1 gain of 0.22**.
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## Fine-Pruning details
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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).
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A side-effect of the block pruning is that some of the attention heads are completely removed: 48 heads were removed on a total of 144 (33.3%).
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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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/pruning_info.js" id="12c50081-be7a-4612-a727-67c9c9309cc9"></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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var element = document.getElementById("
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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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model_card/pruning_info.js
CHANGED
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@@ -16,9 +16,9 @@
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| 17 |
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@@ -115,8 +115,8 @@
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| 115 |
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| 116 |
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| 115 |
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function embed_document(root) {
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