Adding model, graphs and metadata.

README.md

@@ -33,7 +33,7 @@ The model contains **43.0%** of the original weights **overall** (the embeddings
 With a simple resizing of the linear matrices it ran **1.96x 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.
 
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 In terms of accuracy, its **F1 is 88.33**, compared with 88.5 for BERT-base, a **F1 drop of 0.17**.
 
@@ -43,7 +43,7 @@ This model is case-insensitive: it does not make a difference between english an
 
 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.
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 ## Details of the SQuAD1.1 dataset
 

eval/eval_metrics.json

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+    "f1": 88.30542455786251
+}

eval/evaluate_timing.json

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eval/sparsity_report.json

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eval/speed_report.json

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model_card/density_info.js

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training/data_args.json

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training/model_args.json

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+    "use_fast_tokenizer": true
+}

training/sparse_args.json

@@ -0,0 +1,35 @@
+{
+    "ampere_pruning_method": "disabled",
+    "attention_block_cols": 32,
+    "attention_block_rows": 32,
+    "attention_lambda": 1.0,
+    "attention_output_with_dense": 0,
+    "attention_pruning_method": "sigmoied_threshold",
+    "bias_mask": true,
+    "dense_block_cols": 1,
+    "dense_block_rows": 1,
+    "dense_lambda": 1.0,
+    "dense_pruning_method": "sigmoied_threshold:1d_alt",
+    "distil_alpha_ce": 0.1,
+    "distil_alpha_teacher": 0.9,
+    "distil_teacher_name_or_path": "bert-large-uncased-whole-word-masking-finetuned-squad",
+    "distil_temperature": 2.0,
+    "final_ampere_temperature": 20.0,
+    "final_finetune": false,
+    "final_threshold": 0.1,
+    "final_warmup": 10,
+    "gelu_patch": 1,
+    "gelu_patch_steps": 50000,
+    "initial_ampere_temperature": 0.0,
+    "initial_threshold": 0,
+    "initial_warmup": 1,
+    "layer_norm_patch": 1,
+    "layer_norm_patch_start_delta": 0.99,
+    "layer_norm_patch_steps": 50000,
+    "linear_min_parameters": 0,
+    "mask_init": "constant",
+    "mask_scale": 0.0,
+    "mask_scores_learning_rate": 0.01,
+    "regularization": "l1",
+    "regularization_final_lambda": 10
+}

training/training_args.bin

@@ -0,0 +1,3 @@
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+oid sha256:bba1354c4bd39bece4bdb12520826a23ef47fbf45aada70a4aedae1f61422d95
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