Adding model, graphs and metadata.

README.md

@@ -4,8 +4,8 @@ thumbnail:
 license: mit
 tags:
 - question-answering
-- bert
-- bert-base
+- 
+- 
 datasets:
 - squad
 metrics:
@@ -19,7 +19,7 @@ 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 30.0%** of the original weights.
+This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 30.0%** of the original  weights.
 
 This model **CANNOT be used without using nn_pruning `optimize_model`** function, as it uses NoNorms instead of LayerNorms and this is not currently supported by the Transformers library.
 
@@ -30,20 +30,20 @@ This does not need special handling, as it is supported by the Transformers libr
 
 The model contains **45.0%** 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 **2.01x as fast as BERT-base** on the evaluation.
+With a simple resizing of the linear matrices it ran **2.01x 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-x2.01-f89.2-d30-hybrid-rewind-opt-v1/raw/main/model_card/density_info.js" id="cb19239a-7547-4049-88a2-ceaf6bd7ab24"></script></div>
+<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1/raw/main/model_card/density_info.js" id="c3b978cc-6d18-4fd0-a24b-e4369569d64d"></script></div>
 
-In terms of accuracy, its **F1 is 89.19**, compared with 88.5 for BERT-base, a **F1 gain of 0.69**.
+In terms of accuracy, its **F1 is 89.19**, compared with 88.5 for bert-base-uncased, a **F1 gain of 0.69**.
 
 ## 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 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: 55 heads were removed on a total of 144 (38.2%).
 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-x2.01-f89.2-d30-hybrid-rewind-opt-v1/raw/main/model_card/pruning_info.js" id="5bcb346e-3b5a-484b-89c2-a504aeed0c7a"></script></div>
+<div class="graph"><script src="/madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1/raw/main/model_card/pruning_info.js" id="7de38b6d-774c-4313-a5a4-8e32f554d9ec"></script></div>
 
 ## Details of the SQuAD1.1 dataset
 
@@ -65,7 +65,7 @@ GPU driver: 455.23.05, CUDA: 11.1
 
 ### Results
 
-**Pytorch model file size**: `374M` (original BERT: `438M`)
+**Pytorch model file size**: `374MB` (original BERT: `420MB`)
 
 | Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
 | ------ | --------- | --------- | --------- |
@@ -89,11 +89,11 @@ qa_pipeline = pipeline(
     tokenizer="madlag/bert-base-uncased-squadv1-x2.01-f89.2-d30-hybrid-rewind-opt-v1"
 )
 
-print("BERT-base parameters: 110M")
-print(f"Parameters count (includes head pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
+print("bert-base-uncased parameters: 200.0M")
+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 optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
+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

@@ -0,0 +1,4 @@
+{
+    "exact_match": 82.21381267738883,
+    "f1": 89.18801010717891
+}

eval/evaluate_timing.json

@@ -0,0 +1 @@
+{"eval_elapsed_time": 98.45825179666281, "cuda_eval_elapsed_time": 90.40731259918213}

eval/sparsity_report.json

@@ -0,0 +1 @@
+{"total": 108882626, "nnz": 49662908, "linear_total": 84934656, "linear_nnz": 25746432, "layers": {"0": {"total": 7086912, "nnz": 2116894, "linear_total": 7077888, "linear_nnz": 2110464, "linear_attention_total": 2359296, "linear_attention_nnz": 1376256, "linear_dense_total": 4718592, "linear_dense_nnz": 734208}, "1": {"total": 7086528, "nnz": 1803218, "linear_total": 7077888, "linear_nnz": 1797120, "linear_attention_total": 2359296, "linear_attention_nnz": 983040, "linear_dense_total": 4718592, "linear_dense_nnz": 814080}, "2": {"total": 7087296, "nnz": 2797913, "linear_total": 7077888, "linear_nnz": 2790912, "linear_attention_total": 2359296, "linear_attention_nnz": 1769472, "linear_dense_total": 4718592, "linear_dense_nnz": 1021440}, "3": {"total": 7087296, "nnz": 2741044, "linear_total": 7077888, "linear_nnz": 2734080, "linear_attention_total": 2359296, "linear_attention_nnz": 1769472, "linear_dense_total": 4718592, "linear_dense_nnz": 964608}, "4": {"total": 7087488, "nnz": 2808736, "linear_total": 7077888, "linear_nnz": 2801664, "linear_attention_total": 2359296, "linear_attention_nnz": 1966080, "linear_dense_total": 4718592, "linear_dense_nnz": 835584}, "5": {"total": 7086912, "nnz": 2239854, "linear_total": 7077888, "linear_nnz": 2233344, "linear_attention_total": 2359296, "linear_attention_nnz": 1376256, "linear_dense_total": 4718592, "linear_dense_nnz": 857088}, "6": {"total": 7087296, "nnz": 2516642, "linear_total": 7077888, "linear_nnz": 2509824, "linear_attention_total": 2359296, "linear_attention_nnz": 1769472, "linear_dense_total": 4718592, "linear_dense_nnz": 740352}, "7": {"total": 7086912, "nnz": 1946287, "linear_total": 7077888, "linear_nnz": 1939968, "linear_attention_total": 2359296, "linear_attention_nnz": 1376256, "linear_dense_total": 4718592, "linear_dense_nnz": 563712}, "8": {"total": 7087296, "nnz": 2058616, "linear_total": 7077888, "linear_nnz": 2052096, "linear_attention_total": 2359296, "linear_attention_nnz": 1769472, "linear_dense_total": 4718592, "linear_dense_nnz": 282624}, "9": {"total": 7086720, "nnz": 1386755, "linear_total": 7077888, "linear_nnz": 1380864, "linear_attention_total": 2359296, "linear_attention_nnz": 1179648, "linear_dense_total": 4718592, "linear_dense_nnz": 201216}, "10": {"total": 7086528, "nnz": 1494281, "linear_total": 7077888, "linear_nnz": 1488384, "linear_attention_total": 2359296, "linear_attention_nnz": 983040, "linear_dense_total": 4718592, "linear_dense_nnz": 505344}, "11": {"total": 7086720, "nnz": 1913946, "linear_total": 7077888, "linear_nnz": 1907712, "linear_attention_total": 2359296, "linear_attention_nnz": 1179648, "linear_dense_total": 4718592, "linear_dense_nnz": 728064}}, "total_sparsity": 54.38858353765275, "linear_sparsity": 69.68677662037037, "pruned_heads": {"0": [0, 2, 4, 5, 6], "1": [0, 2, 3, 5, 6, 7, 8], "2": [8, 4, 7], "3": [2, 4, 6], "4": [1, 2], "5": [1, 2, 6, 7, 11], "6": [3, 10, 2], "7": [1, 3, 6, 7, 11], "8": [0, 3, 4], "9": [1, 4, 5, 7, 9, 10], "10": [1, 2, 4, 5, 6, 7, 8], "11": [0, 5, 7, 8, 10, 11]}}

eval/speed_report.json

@@ -0,0 +1 @@
+{"timings": {"eval_elapsed_time": 26.312922549434006, "cuda_eval_elapsed_time": 19.22297591018677}, "metrics": {"exact_match": 82.21381267738883, "f1": 89.18874369381042}}

model_card/density_info.js

@@ -16,9 +16,9 @@
       
       
     
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model_card/pruning_info.js

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training/model_args.json

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