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  ## BERT-base uncased model fine-tuned on SQuAD v1
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- This model is [block-sparse](https://github.com/huggingface/pytorch_block_sparse).
 
 
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- That means that with the right runtime it can run roughly 3x faster than an dense network, with 25% of the original weights.
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- This of course has some impact on the accuracy (see below).
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-
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- It uses a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method.
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  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).
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  This model is case-insensitive: it does not make a difference between english and English.
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  ## Pruning details
 
 
 
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  ![Pruning details](https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1/raw/main/model_card/pruning.svg)
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  ## Density plot
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-
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- <script src="/madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1/raw/main/model_card/density.js" id="4af67900-bd92-4310-973e-0cf20443efff"></script>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Details
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  ## BERT-base uncased model fine-tuned on SQuAD v1
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+ This model is [block-sparse](https://github.com/huggingface/pytorch_block_sparse): the linear layers contains 12.5% of the original weights.
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+ The model contains 32.1% of the original weights overall.
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+ The training use a modified version of Victor Sanh [Movement Pruning](https://arxiv.org/abs/2005.07683) method.
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+ That means that with the right runtime it can run faster than an dense networks, at the price of some impact on the accuracy (see below).
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  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).
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  This model is case-insensitive: it does not make a difference between english and English.
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  ## Pruning details
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+ A side-effect of the block pruning is that some of the attention heads are completely removed: in this case, 97 heads were removed on a total of 144 (67.4%).
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+ Here is a detailed view on how the remaining heads are distributed in the network after pruning.
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+
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  ![Pruning details](https://huggingface.co/madlag/bert-base-uncased-squad1.1-block-sparse-0.13-v1/raw/main/model_card/pruning.svg)
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  ## Density plot
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+ root.Bokeh.embed.embed_items(docs_json, render_items);
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+
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+ }
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+ if (root.Bokeh !== undefined) {
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+ embed_document(root);
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+ } else {
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+ var attempts = 0;
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+ var timer = setInterval(function(root) {
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+ if (root.Bokeh !== undefined) {
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+ clearInterval(timer);
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+ embed_document(root);
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+ } else {
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+ attempts++;
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+ if (attempts > 100) {
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+ clearInterval(timer);
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+ console.log("Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing");
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+ }
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+ }
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+ }, 10, root)
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+ }
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+ })(window);
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+ });
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+ };
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+ if (document.readyState != "loading") fn();
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+ else document.addEventListener("DOMContentLoaded", fn);
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+ })();
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+ },
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+ function(Bokeh) {
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+
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+
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+ }
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+ ];
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+
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+ function run_inline_js() {
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+
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+ for (var i = 0; i < inline_js.length; i++) {
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+ inline_js[i].call(root, root.Bokeh);
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+ }
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+
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+ }
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+
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+ if (root._bokeh_is_loading === 0) {
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+ console.debug("Bokeh: BokehJS loaded, going straight to plotting");
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+ run_inline_js();
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+ } else {
203
+ load_libs(css_urls, js_urls, function() {
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+ console.debug("Bokeh: BokehJS plotting callback run at", now());
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+ run_inline_js();
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+ });
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+ }
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+ }(window));
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+ };
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+ if (document.readyState != "loading") fn();
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+ else document.addEventListener("DOMContentLoaded", fn);
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+ })();
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  ## Details
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