Initial revision of model.

README.md

@@ -11,7 +11,7 @@ datasets:
 metrics:
 - squad
 widget:
-- text: "Where is located the Eiffel Tower ?"
+- text: "Where is the Eiffel Tower located?"
   context: "The Eiffel Tower is a wrought-iron lattice tower on the Champ de Mars in Paris, France. It is named after the engineer Gustave Eiffel, whose company designed and built the tower."
 - text: "Who is Frederic Chopin?"
   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."
@@ -19,23 +19,30 @@ widget:
 
 ## BERT-base uncased model fine-tuned on SQuAD v1
 
-This model is [block-sparse](https://github.com/huggingface/pytorch_block_sparse).
+This model is block sparse: the **linear** layers contains **20.2%** of the original weights.
 
-That means that with the right runtime it can run roughly 3x faster than an dense network, with 25% of the original weights. 
 
-This of course has some impact on the accuracy (see below).
+The model contains **38.1%** of the original weights **overall**.
 
-It uses a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method.
+The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method.
 
-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).
+That means that with the [block-sparse](https://github.com/huggingface/pytorch_block_sparse) runtime it ran **1.39x** faster than an dense networks on the evaluation, at the price of some impact on the accuracy (see below).
+
+
+
+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.
 
 ## Pruning details
+A side-effect of the block pruning is that some of the attention heads are completely removed: 90 heads were removed on a total of 144 (62.5%).
+
+Here is a detailed view on how the remaining heads are distributed in the network after pruning.
+
 ![Pruning details](https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1/raw/main/model_card/pruning.svg)
 
 ## Density plot
 
-<script src="/madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1/raw/main/model_card/density.js" id="3de0a8fc-24f0-438c-aa5d-20c74f98a17d"></script>
+<script src="/madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1/raw/main/model_card/density.js" id="61a5e601-a8d3-463f-98db-8fbf642810fe"></script>
 
 ## Details
 
@@ -44,32 +51,27 @@ This model is case-insensitive: it does not make a difference between english an
 | SQuAD1.1 | train | 90.6K      |
 | SQuAD1.1 | eval  | 11.1k     |
 
-
 ### Fine-tuning
 - Python: `3.8.5`
 
 - Machine specs: 
 
-  `CPU: Intel(R) Core(TM) i7-6700K CPU`
-  
-  `Memory: 64 GiB`
-
-  `GPUs: 1 GeForce GTX 3090, with 24GiB memory`
-  
-  `GPU driver: 455.23.05, CUDA: 11.1`
+```CPU: Intel(R) Core(TM) i7-6700K CPU
+Memory: 64 GiB
+GPUs: 1 GeForce GTX 3090, with 24GiB memory
+GPU driver: 455.23.05, CUDA: 11.1
+```
 
 
 ### Results
 
-**Model size**: `418M`
+**Pytorch model file size**: `347M` (original BERT: `438M`)
 
 | Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))|
 | ------ | --------- | --------- |
 | **EM** | **76.98** | **80.8** |
 | **F1** | **85.45** | **88.5** |
 
-Note that the above results didn't involve any hyperparameter search.
-
 ## Example Usage
 
 ```python
@@ -77,8 +79,8 @@ from transformers import pipeline
 
 qa_pipeline = pipeline(
     "question-answering",
-    model="madlag/bert-base-uncased-squad-v1-sparse0.25",
-    tokenizer="madlag/bert-base-uncased-squad-v1-sparse0.25"
+    model="madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1",
+    tokenizer="madlag/bert-base-uncased-squad1.1-block-sparse-0.20-v1"
 )
 
 predictions = qa_pipeline({
@@ -86,4 +88,5 @@ predictions = qa_pipeline({
     'question': "Who is Frederic Chopin?",
 })
 
-print(predictions)
+print(predictions)
+```

model_card/density.js

@@ -16,9 +16,9 @@
       
       
     
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-          console.warn("Bokeh: autoload.js configured with elementid '3de0a8fc-24f0-438c-aa5d-20c74f98a17d' but no matching script tag was found.")
+          console.warn("Bokeh: autoload.js configured with elementid '61a5e601-a8d3-463f-98db-8fbf642810fe' but no matching script tag was found.")
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