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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 +297 -0
training/data_args.json +16 -0
training/model_args.json +7 -0
training/sparse_args.json +29 -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 15.0%** of the original weights.
 The model contains **34.0%** 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 **2.32x 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-x2.32-f86.6-d15-hybrid-v1/raw/main/model_card/density_info.js" id="fd51557a-ad63-4088-bc25-67d39b0c0b2c"></script></div>
-In terms of accuracy, its **F1 is 86.64**, compared with 88.5 for BERT-base, a **F1 drop of 1.86**.
 ## 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: 63 heads were removed on a total of 144 (43.8%).
 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-x2.32-f86.6-d15-hybrid-v1/raw/main/model_card/pruning_info.js" id="2531d97a-f550-49d6-9b4e-8d344db20f2b"></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**: `368M` (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-x2.32-f86.6-d15-hybrid-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 15.0%** of the original  weights.
 The model contains **34.0%** 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 **2.32x 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-x2.32-f86.6-d15-hybrid-v1/raw/main/model_card/density_info.js" id="1ff1ba08-69d3-4a20-9f29-494033c72860"></script></div>
+In terms of accuracy, its **F1 is 86.64**, compared with 88.5 for bert-base-uncased, a **F1 drop of 1.86**.
 ## 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: 63 heads were removed on a total of 144 (43.8%).
 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-x2.32-f86.6-d15-hybrid-v1/raw/main/model_card/pruning_info.js" id="e092ee84-28af-4821-8127-11914f68e306"></script></div>
 ## Details of the SQuAD1.1 dataset
 ### Results
+**Pytorch model file size**: `368MB` (original BERT: `420MB`)
 | Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
 | ------ | --------- | --------- | --------- |
     tokenizer="madlag/bert-base-uncased-squadv1-x2.32-f86.6-d15-hybrid-v1"
 )
+print("bert-base-uncased parameters: 165.0M")
+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": 78.74172185430463,
+    "f1": 86.57976286123308
+}

eval/evaluate_timing.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"eval_elapsed_time": 86.37038454785943, "cuda_eval_elapsed_time": 78.39602122497558}

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": 23.629751751199365, "cuda_eval_elapsed_time": 16.665313415527343}, "metrics": {"exact_match": 78.77010406811732, "f1": 86.63938864881486}}

model_card/density_info.js CHANGED Viewed

@@ -16,9 +16,9 @@
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@@ -115,8 +115,8 @@
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+    "cache_dir": null,
+    "config_name": null,
+    "model_name_or_path": "bert-base-uncased",
+    "tokenizer_name": null,
+    "use_fast_tokenizer": true
+}

training/sparse_args.json ADDED Viewed

	@@ -0,0 +1,29 @@

+{
+    "ampere_pruning_method": "disabled",
+    "attention_block_cols": 32,
+    "attention_block_rows": 32,
+    "attention_lambda": 1.0,
+    "attention_output_with_dense": 0,
+    "attention_pruning_method": "sigmoied_threshold",
+    "bias_mask": true,
+    "dense_block_cols": 1,
+    "dense_block_rows": 1,
+    "dense_lambda": 1.0,
+    "dense_pruning_method": "sigmoied_threshold:1d_alt",
+    "distil_alpha_ce": 0.1,
+    "distil_alpha_teacher": 0.9,
+    "distil_teacher_name_or_path": "bert-large-uncased-whole-word-masking-finetuned-squad",
+    "distil_temperature": 2.0,
+    "final_ampere_temperature": 20.0,
+    "final_finetune": false,
+    "final_threshold": 0.1,
+    "final_warmup": 10,
+    "initial_ampere_temperature": 0.0,
+    "initial_threshold": 0,
+    "initial_warmup": 1,
+    "mask_init": "constant",
+    "mask_scale": 0.0,
+    "mask_scores_learning_rate": 0.01,
+    "regularization": "l1",
+    "regularization_final_lambda": 20
+}

training/training_args.bin ADDED Viewed

	@@ -0,0 +1,3 @@

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