madlag commited on
Commit
86c732c
1 Parent(s): 395b875

Adding model, graphs and metadata.

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README.md CHANGED
@@ -4,8 +4,8 @@ thumbnail:
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  license: mit
5
  tags:
6
  - question-answering
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- - bert
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- - bert-base
9
  datasets:
10
  - squad
11
  metrics:
@@ -19,26 +19,26 @@ widget:
19
 
20
  ## BERT-base uncased model fine-tuned on SQuAD v1
21
 
22
- This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 8.0%** of the original weights.
23
 
24
 
25
 
26
  The model contains **28.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
27
 
28
- With a simple resizing of the linear matrices it ran **1.16x as fast as BERT-base** on the evaluation.
29
  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.
30
 
31
- <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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33
- In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for BERT-base, a **F1 drop of 0.39**.
34
 
35
  ## Fine-Pruning details
36
- 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 model [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad).
37
  This model is case-insensitive: it does not make a difference between english and English.
38
 
39
  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%).
40
  Here is a detailed view on how the remaining heads are distributed in the network after pruning.
41
- <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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43
  ## Details of the SQuAD1.1 dataset
44
 
@@ -60,7 +60,7 @@ GPU driver: 455.23.05, CUDA: 11.1
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61
  ### Results
62
 
63
- **Pytorch model file size**: `398M` (original BERT: `438M`)
64
 
65
  | Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
66
  | ------ | --------- | --------- | --------- |
@@ -84,11 +84,11 @@ qa_pipeline = pipeline(
84
  tokenizer="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1"
85
  )
86
 
87
- print("BERT-base parameters: 110M")
88
- print(f"Parameters count (includes head pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
89
  qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
90
 
91
- print(f"Parameters count after optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
92
  predictions = qa_pipeline({
93
  '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.",
94
  'question': "Who is Frederic Chopin?",
 
4
  license: mit
5
  tags:
6
  - question-answering
7
+ -
8
+ -
9
  datasets:
10
  - squad
11
  metrics:
 
19
 
20
  ## BERT-base uncased model fine-tuned on SQuAD v1
21
 
22
+ This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 8.0%** of the original weights.
23
 
24
 
25
 
26
  The model contains **28.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
27
 
28
+ With a simple resizing of the linear matrices it ran **1.16x as fast as bert-base-uncased** on the evaluation.
29
  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.
30
 
31
+ <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="c60d09ec-81ff-4d6f-b616-c3ef09b2175d"></script></div>
32
 
33
+ In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for bert-base-uncased, a **F1 drop of 0.39**.
34
 
35
  ## Fine-Pruning details
36
+ This model was fine-tuned from the HuggingFace [model](https://huggingface.co/bert-base-uncased) checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad)
37
  This model is case-insensitive: it does not make a difference between english and English.
38
 
39
  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%).
40
  Here is a detailed view on how the remaining heads are distributed in the network after pruning.
41
+ <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="55528c8b-d5f5-46a5-a35a-dad93725f7e5"></script></div>
42
 
43
  ## Details of the SQuAD1.1 dataset
44
 
 
60
 
61
  ### Results
62
 
63
+ **Pytorch model file size**: `398MB` (original BERT: `420MB`)
64
 
65
  | Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
66
  | ------ | --------- | --------- | --------- |
 
84
  tokenizer="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1"
85
  )
86
 
87
+ print("bert-base-uncased parameters: 152.0M")
88
+ print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
89
  qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
90
 
91
+ print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
92
  predictions = qa_pipeline({
93
  '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.",
94
  'question': "Who is Frederic Chopin?",
eval/eval_metrics.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "exact_match": 80.89877010406812,
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+ "f1": 88.09526643561759
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+ }
eval/evaluate_timing.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"eval_elapsed_time": 94.33543854672462, "cuda_eval_elapsed_time": 86.41913686752319}
eval/nbest_predictions.json.tgz ADDED
Binary file (6.63 MB). View file
 
eval/predictions.json ADDED
The diff for this file is too large to render. See raw diff
 
eval/sparsity_report.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"total": 108893186, "nnz": 30895354, "linear_total": 84934656, "linear_nnz": 6956106, "layers": {"0": {"total": 7087872, "nnz": 860873, "linear_total": 7077888, "linear_nnz": 852146, "linear_attention_total": 2359296, "linear_attention_nnz": 108638, "linear_dense_total": 4718592, "linear_dense_nnz": 743508}, "1": {"total": 7087872, "nnz": 960245, "linear_total": 7077888, "linear_nnz": 951459, "linear_attention_total": 2359296, "linear_attention_nnz": 171340, "linear_dense_total": 4718592, "linear_dense_nnz": 780119}, "2": {"total": 7087872, "nnz": 986901, "linear_total": 7077888, "linear_nnz": 977699, "linear_attention_total": 2359296, "linear_attention_nnz": 221074, "linear_dense_total": 4718592, "linear_dense_nnz": 756625}, "3": {"total": 7087872, "nnz": 973427, "linear_total": 7077888, "linear_nnz": 964137, "linear_attention_total": 2359296, "linear_attention_nnz": 258229, "linear_dense_total": 4718592, "linear_dense_nnz": 705908}, "4": {"total": 7087872, "nnz": 858037, "linear_total": 7077888, "linear_nnz": 848474, "linear_attention_total": 2359296, "linear_attention_nnz": 255136, "linear_dense_total": 4718592, "linear_dense_nnz": 593338}, "5": {"total": 7087872, "nnz": 720395, "linear_total": 7077888, "linear_nnz": 711055, "linear_attention_total": 2359296, "linear_attention_nnz": 179994, "linear_dense_total": 4718592, "linear_dense_nnz": 531061}, "6": {"total": 7087872, "nnz": 580963, "linear_total": 7077888, "linear_nnz": 571558, "linear_attention_total": 2359296, "linear_attention_nnz": 165167, "linear_dense_total": 4718592, "linear_dense_nnz": 406391}, "7": {"total": 7087872, "nnz": 421032, "linear_total": 7077888, "linear_nnz": 412421, "linear_attention_total": 2359296, "linear_attention_nnz": 139907, "linear_dense_total": 4718592, "linear_dense_nnz": 272514}, "8": {"total": 7087872, "nnz": 285536, "linear_total": 7077888, "linear_nnz": 277031, "linear_attention_total": 2359296, "linear_attention_nnz": 113253, "linear_dense_total": 4718592, "linear_dense_nnz": 163778}, "9": {"total": 7087872, "nnz": 162775, "linear_total": 7077888, "linear_nnz": 156105, "linear_attention_total": 2359296, "linear_attention_nnz": 84915, "linear_dense_total": 4718592, "linear_dense_nnz": 71190}, "10": {"total": 7087872, "nnz": 143162, "linear_total": 7077888, "linear_nnz": 136669, "linear_attention_total": 2359296, "linear_attention_nnz": 67316, "linear_dense_total": 4718592, "linear_dense_nnz": 69353}, "11": {"total": 7087872, "nnz": 103286, "linear_total": 7077888, "linear_nnz": 97352, "linear_attention_total": 2359296, "linear_attention_nnz": 41497, "linear_dense_total": 4718592, "linear_dense_nnz": 55855}}, "total_sparsity": 71.62783537254572, "linear_sparsity": 91.81004983407479, "pruned_heads": {"0": [9], "1": [], "2": [8], "3": [2, 4], "4": [], "5": [1], "6": [2, 3], "7": [1, 7], "8": [0], "9": [1, 4, 5, 7, 10], "10": [1, 2, 4], "11": [8, 11, 5, 7]}}
eval/speed_report.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"timings": {"eval_elapsed_time": 40.18306041043252, "cuda_eval_elapsed_time": 33.17768461608887}, "metrics": {"exact_match": 80.93661305581836, "f1": 88.11360890595924}}
model_card/density_info.js CHANGED
@@ -16,9 +16,9 @@
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- var element = document.getElementById("14cf4013-d609-473e-a62b-6f7429a41f44");
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  if (element == null) {
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- console.warn("Bokeh: autoload.js configured with elementid '14cf4013-d609-473e-a62b-6f7429a41f44' but no matching script tag was found.")
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  }
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@@ -115,8 +115,8 @@
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119
- var render_items = [{"docid":"63c71560-6caa-4f60-b5e0-9a1be29aeb92","root_ids":["1095"],"roots":{"1095":"14cf4013-d609-473e-a62b-6f7429a41f44"}}];
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  root.Bokeh.embed.embed_items(docs_json, render_items);
121
 
122
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