Adding model, graphs and metadata.
Browse files- README.md +12 -12
- eval/nbest_predictions.json.tgz +0 -0
- model_card/density_info.js +4 -4
- model_card/pruning_info.js +4 -4
README.md
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license: mit
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tags:
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- question-answering
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datasets:
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- squad
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metrics:
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## BERT-base uncased model fine-tuned on SQuAD v1
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This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 36.0%** of the original
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The model contains **50.0%** 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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With a simple resizing of the linear matrices it ran **1.84x as fast as
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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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In terms of accuracy, its **F1 is 88.72**, compared with 88.5 for
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## Fine-Pruning details
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This model was fine-tuned from the HuggingFace [
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This model is case-insensitive: it does not make a difference between english and English.
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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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### Results
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**Pytorch model file size**: `
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| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
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| ------ | --------- | --------- | --------- |
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tokenizer="madlag/bert-base-uncased-squadv1-x1.84-f88.7-d36-hybrid-filled-v1"
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)
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print("
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print(f"Parameters count (includes head pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
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qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
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print(f"Parameters count after optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
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predictions = qa_pipeline({
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'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
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'question': "Who is Frederic Chopin?",
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license: mit
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tags:
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- question-answering
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-
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-
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datasets:
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- squad
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metrics:
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## BERT-base uncased model fine-tuned on SQuAD v1
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This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 36.0%** of the original weights.
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The model contains **50.0%** 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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With a simple resizing of the linear matrices it ran **1.84x as fast as /home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune** 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="3aca15eb-8def-482c-800a-d9f8a6e8cea5"></script></div>
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In terms of accuracy, its **F1 is 88.72**, compared with 88.5 for /home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune, 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 [model](https://huggingface.co//home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune) checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1)
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This model is case-insensitive: it does not make a difference between english and English.
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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="95fe9d1f-98f7-40e1-a28f-b90d0da0f1a8"></script></div>
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## Details of the SQuAD1.1 dataset
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### Results
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**Pytorch model file size**: `379MB` (original BERT: `420MB`)
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| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
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| ------ | --------- | --------- | --------- |
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tokenizer="madlag/bert-base-uncased-squadv1-x1.84-f88.7-d36-hybrid-filled-v1"
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)
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print("/home/lagunas/devel/hf/nn_pruning/nn_pruning/analysis/tmp_finetune parameters: 218.0M")
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print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
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qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
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print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
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predictions = qa_pipeline({
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'context': "Frédéric François Chopin, born Fryderyk Franciszek Chopin (1 March 1810 – 17 October 1849), was a Polish composer and virtuoso pianist of the Romantic era who wrote primarily for solo piano.",
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'question': "Who is Frederic Chopin?",
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eval/nbest_predictions.json.tgz
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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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root.Bokeh.embed.embed_items(docs_json, render_items);
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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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| 16 |
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| 17 |
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| 18 |
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| 19 |
-
var element = document.getElementById("
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| 20 |
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| 21 |
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console.warn("Bokeh: autoload.js configured with elementid '
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| 22 |
}
|
| 23 |
|
| 24 |
|
|
@@ -115,8 +115,8 @@
|
|
| 115 |
(function(root) {
|
| 116 |
function embed_document(root) {
|
| 117 |
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| 118 |
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var docs_json = '{"
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| 119 |
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| 16 |
|
| 17 |
|
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var element = document.getElementById("95fe9d1f-98f7-40e1-a28f-b90d0da0f1a8");
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if (element == null) {
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}
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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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