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README.md +12 -5
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 +295 -0
training/data_args.json +16 -0
training/model_args.json +6 -0
training/sparse_args.json +29 -0
training/training_args.bin +3 -0

README.md CHANGED Viewed

@@ -19,24 +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 64.0%** of the original weights.
-The model contains **36.0%** of the original weights **overall** (remaining weights are mostly in the input/ouput embeddings).
 With a simple resizing of the linear matrices it ran **1.84x 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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/density_info.js" id="6a33cbcb-db1a-4e7a-a6dc-1c353babcf55"></script></div>
 In terms of accuracy, its **F1 is 88.72**, compared with 88.5 for BERT-base, a **F1 gain of 0.22**.
 ## 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 equivalent model [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1).
 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: 48 heads were removed on a total of 144 (33.3%).
 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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/pruning_info.js" id="12c50081-be7a-4612-a727-67c9c9309cc9"></script></div>
 ## Details of the SQuAD1.1 dataset
@@ -66,6 +68,11 @@ GPU driver: 455.23.05, CUDA: 11.1
 | **F1** | **88.72** | **88.5** | **+0.22**|
 ## Example Usage
 ```python
 from transformers import pipeline

 ## 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 36.0%** of the original weights.
+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).
 With a simple resizing of the linear matrices it ran **1.84x 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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/density_info.js" id="4d42d80b-b77b-473a-88be-4fc356398a81"></script></div>
 In terms of accuracy, its **F1 is 88.72**, compared with 88.5 for BERT-base, a **F1 gain of 0.22**.
 ## 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 [csarron/bert-base-uncased-squad-v1](https://huggingface.co/csarron/bert-base-uncased-squad-v1).
 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: 48 heads were removed on a total of 144 (33.3%).
 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-x1.84-f88.7-d36-hybrid-filled-v1/raw/main/model_card/pruning_info.js" id="6d8fc181-a288-4ff2-8846-d16d82b0da33"></script></div>
 ## Details of the SQuAD1.1 dataset
 | **F1** | **88.72** | **88.5** | **+0.22**|
 ## Example Usage
+Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
+`pip install nn_pruning`
+Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded.
 ```python
 from transformers import pipeline

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

+{
+    "exact_match": 81.69347209082308,
+    "f1": 88.72194531479171
+}

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1	+ {"eval_elapsed_time": 21.117456380976364}

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+ {"total": 108883970, "nnz": 54738530, "linear_total": 84934656, "linear_nnz": 30818304, "layers": {"0": {"total": 7086912, "nnz": 2508829, "linear_total": 7077888, "linear_nnz": 2502144, "linear_attention_total": 2359296, "linear_attention_nnz": 1376256, "linear_dense_total": 4718592, "linear_dense_nnz": 1125888}, "1": {"total": 7086528, "nnz": 2275077, "linear_total": 7077888, "linear_nnz": 2268672, "linear_attention_total": 2359296, "linear_attention_nnz": 983040, "linear_dense_total": 4718592, "linear_dense_nnz": 1285632}, "2": {"total": 7087296, "nnz": 3300512, "linear_total": 7077888, "linear_nnz": 3293184, "linear_attention_total": 2359296, "linear_attention_nnz": 1769472, "linear_dense_total": 4718592, "linear_dense_nnz": 1523712}, "3": {"total": 7087296, "nnz": 3332789, "linear_total": 7077888, "linear_nnz": 3325440, "linear_attention_total": 2359296, "linear_attention_nnz": 1769472, "linear_dense_total": 4718592, "linear_dense_nnz": 1555968}, "4": {"total": 7087680, "nnz": 3787869, "linear_total": 7077888, "linear_nnz": 3780096, "linear_attention_total": 2359296, "linear_attention_nnz": 2162688, "linear_dense_total": 4718592, "linear_dense_nnz": 1617408}, "5": {"total": 7087488, "nnz": 3488090, "linear_total": 7077888, "linear_nnz": 3480576, "linear_attention_total": 2359296, "linear_attention_nnz": 1966080, "linear_dense_total": 4718592, "linear_dense_nnz": 1514496}, "6": {"total": 7087296, "nnz": 2911651, "linear_total": 7077888, "linear_nnz": 2904576, "linear_attention_total": 2359296, "linear_attention_nnz": 1769472, "linear_dense_total": 4718592, "linear_dense_nnz": 1135104}, "7": {"total": 7087104, "nnz": 2427432, "linear_total": 7077888, "linear_nnz": 2420736, "linear_attention_total": 2359296, "linear_attention_nnz": 1572864, "linear_dense_total": 4718592, "linear_dense_nnz": 847872}, "8": {"total": 7087488, "nnz": 2447541, "linear_total": 7077888, "linear_nnz": 2440704, "linear_attention_total": 2359296, "linear_attention_nnz": 1966080, "linear_dense_total": 4718592, "linear_dense_nnz": 474624}, "9": {"total": 7086720, "nnz": 1394440, "linear_total": 7077888, "linear_nnz": 1388544, "linear_attention_total": 2359296, "linear_attention_nnz": 1179648, "linear_dense_total": 4718592, "linear_dense_nnz": 208896}, "10": {"total": 7086912, "nnz": 1551278, "linear_total": 7077888, "linear_nnz": 1545216, "linear_attention_total": 2359296, "linear_attention_nnz": 1376256, "linear_dense_total": 4718592, "linear_dense_nnz": 168960}, "11": {"total": 7086528, "nnz": 1474300, "linear_total": 7077888, "linear_nnz": 1468416, "linear_attention_total": 2359296, "linear_attention_nnz": 983040, "linear_dense_total": 4718592, "linear_dense_nnz": 485376}}, "total_sparsity": 49.72765045212808, "linear_sparsity": 63.71527777777778, "pruned_heads": {"0": [2, 4, 5, 6, 7], "1": [0, 2, 3, 5, 6, 7, 8], "2": [8, 4, 7], "3": [2, 4, 6], "4": [2], "5": [1, 2], "6": [2, 3, 7], "7": [11, 3, 6, 7], "8": [0, 4], "9": [1, 4, 5, 7, 9, 10], "10": [1, 4, 5, 6, 7], "11": [0, 2, 5, 6, 7, 8, 11]}}

eval/speed_report.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"timings": {"eval_elapsed_time": 28.213609586004168, "cuda_eval_elapsed_time": 20.951393741607667}, "metrics": {"exact_match": 81.69347209082308, "f1": 88.72194531479171}}

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