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README.md +12 -12
eval/eval_metrics.json +4 -0
eval/evaluate_timing.json +1 -0
eval/nbest_predictions.json.tgz +0 -0
eval/predictions.json +0 -0
eval/sparsity_report.json +1 -0
eval/speed_report.json +1 -0
model_card/density_info.js +4 -4
model_card/pruning_info.js +4 -4
model_info.json +299 -0
training/data_args.json +16 -0
training/model_args.json +7 -0
training/sparse_args.json +31 -0
training/training_args.bin +3 -0

README.md CHANGED Viewed

@@ -4,8 +4,8 @@ thumbnail:
 license: mit
 tags:
 - question-answering
-- bert
-- bert-base
 datasets:
 - squad
 metrics:
@@ -19,26 +19,26 @@ widget:
 ## BERT-base uncased model fine-tuned on SQuAD v1
-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.
 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).
-With a simple resizing of the linear matrices it ran **1.16x as fast as BERT-base** on the evaluation.
 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.
-<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>
-In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for BERT-base, a **F1 drop of 0.39**.
 ## Fine-Pruning details
-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).
 This model is case-insensitive: it does not make a difference between english and English.
 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%).
 Here is a detailed view on how the remaining heads are distributed in the network after pruning.
-<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>
 ## Details of the SQuAD1.1 dataset
@@ -60,7 +60,7 @@ GPU driver: 455.23.05, CUDA: 11.1
 ### Results
-**Pytorch model file size**: `398M` (original BERT: `438M`)
 | Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
 | ------ | --------- | --------- | --------- |
@@ -84,11 +84,11 @@ qa_pipeline = pipeline(
     tokenizer="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1"
 )
-print("BERT-base parameters: 110M")
-print(f"Parameters count (includes head pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
 qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
-print(f"Parameters count after optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
 predictions = qa_pipeline({
     '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.",
     'question': "Who is Frederic Chopin?",

 license: mit
 tags:
 - question-answering
+-
+-
 datasets:
 - squad
 metrics:
 ## BERT-base uncased model fine-tuned on SQuAD v1
+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.
 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).
+With a simple resizing of the linear matrices it ran **1.16x as fast as bert-base-uncased** on the evaluation.
 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.
+<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>
+In terms of accuracy, its **F1 is 88.11**, compared with 88.5 for bert-base-uncased, a **F1 drop of 0.39**.
 ## Fine-Pruning details
+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)
 This model is case-insensitive: it does not make a difference between english and English.
 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%).
 Here is a detailed view on how the remaining heads are distributed in the network after pruning.
+<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>
 ## Details of the SQuAD1.1 dataset
 ### Results
+**Pytorch model file size**: `398MB` (original BERT: `420MB`)
 | Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
 | ------ | --------- | --------- | --------- |
     tokenizer="madlag/bert-base-uncased-squadv1-x1.16-f88.1-d8-unstruct-v1"
 )
+print("bert-base-uncased parameters: 152.0M")
+print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
 qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
+print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
 predictions = qa_pipeline({
     '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.",
     'question': "Who is Frederic Chopin?",

eval/eval_metrics.json ADDED Viewed

	@@ -0,0 +1,4 @@

+{
+    "exact_match": 80.89877010406812,
+    "f1": 88.09526643561759
+}

eval/evaluate_timing.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"eval_elapsed_time": 94.33543854672462, "cuda_eval_elapsed_time": 86.41913686752319}

eval/nbest_predictions.json.tgz ADDED Viewed

Binary file (6.63 MB). View file

eval/predictions.json ADDED Viewed

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eval/sparsity_report.json ADDED Viewed

	@@ -0,0 +1 @@

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eval/speed_report.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"timings": {"eval_elapsed_time": 40.18306041043252, "cuda_eval_elapsed_time": 33.17768461608887}, "metrics": {"exact_match": 80.93661305581836, "f1": 88.11360890595924}}

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