Adding modes, graphs and metadata.

README.md

@@ -4,12 +4,12 @@ thumbnail:
 license: mit
 tags:
 - question-answering
-- bert
-- bert-base
+- 
+- 
 datasets:
-- squad
+- squad_v2
 metrics:
-- squad
+- squad_v2
 widget:
 - 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."
@@ -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 25.0%** of the original weights.
+This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 25.0%** of the original  weights.
 
 
 
 The model contains **32.0%** 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 **0.69x as fast as BERT-base** on the evaluation.
+With a simple resizing of the linear matrices it ran **0.69x as fast as ** 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-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1/raw/main/model_card/density_info.js" id="4f712c39-37ac-428b-8251-761d1dd509a3"></script></div>
+<div class="graph"><script src="/madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1/raw/main/model_card/density_info.js" id="9d30472c-1d51-4fe9-bf7d-d6c8db3dd8be"></script></div>
 
-In terms of accuracy, its **F1 is 83.22**, compared with 85.85 for BERT-base, a **F1 drop of 2.63**.
+In terms of accuracy, its **F1 is 83.22**, compared with 85.85 for , a **F1 drop of 2.63**.
 
 ## 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 [madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2](https://huggingface.co/madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2).
+This model was fine-tuned from the HuggingFace [model](https://huggingface.co/bert-large-uncased-whole-word-masking)  uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer), and distilled from the model [madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2](https://huggingface.co/madlag/bert-large-uncased-whole-word-masking-finetuned-squadv2).
 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: 155 heads were removed on a total of 384 (40.4%).
 Here is a detailed view on how the remaining heads are distributed in the network after pruning.
-<div class="graph"><script src="/madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1/raw/main/model_card/pruning_info.js" id="96cf0a80-f137-4589-a631-b6c81043e7f5"></script></div>
+<div class="graph"><script src="/madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1/raw/main/model_card/pruning_info.js" id="720cac75-f22b-4118-8196-f5ffc3a7ce21"></script></div>
 
 ## Details of the SQuAD1.1 dataset
 
@@ -60,14 +60,16 @@ GPU driver: 455.23.05, CUDA: 11.1
 
 ### Results
 
-**Pytorch model file size**: `1119M` (original BERT: `438M`)
+**Pytorch model file size**: `1119M` (original BERT: ``)
 
 | Metric | # Value   | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
 | ------ | --------- | --------- | --------- |
 | **EM** | **80.19** | **82.83** | **-3.64**|
 | **F1** | **83.22** | **85.85** | **-2.63**|
 
-````
+```
+{'HasAns_exact': 76.48448043184885, 'HasAns_f1': 82.55514100819374, 'HasAns_total': 5928, 'NoAns_exact': 83.8856181665265, 'NoAns_f1': 83.8856181665265, 'NoAns_total': 5945, 'best_exact': 80.19034784805862, 'best_exact_thresh': 0.0, 'best_f1': 83.22133208932635, 'best_f1_thresh': 0.0, 'exact': 80.19034784805862, 'f1': 83.22133208932645, 'total': 11873, 'main_metric': 83.22133208932645}
+```
 
 ## Example Usage
 Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
@@ -86,7 +88,7 @@ qa_pipeline = pipeline(
     tokenizer="madlag/bert-large-uncased-wwm-squadv2-x2.15-f83.2-d25-hybrid-v1"
 )
 
-print("BERT-base parameters: 110M")
+print("BERT-base parameters: ")
 print(f"Parameters count (includes head pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
 qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
 

model_card/density_info.js

@@ -16,9 +16,9 @@
       
       
     
-      var element = document.getElementById("4f712c39-37ac-428b-8251-761d1dd509a3");
+      var element = document.getElementById("9d30472c-1d51-4fe9-bf7d-d6c8db3dd8be");
         if (element == null) {
-          console.warn("Bokeh: autoload.js configured with elementid '4f712c39-37ac-428b-8251-761d1dd509a3' but no matching script tag was found.")
+          console.warn("Bokeh: autoload.js configured with elementid '9d30472c-1d51-4fe9-bf7d-d6c8db3dd8be' but no matching script tag was found.")
         }
       
     
@@ -115,8 +115,8 @@
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model_card/pruning_info.js

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