Adding modes, graphs and metadata.
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- README.md +115 -0
- config.json +264 -0
- model_card/density_info.js +174 -0
- model_card/images/layer_0_attention_output_dense.png +0 -0
- model_card/images/layer_0_attention_self_key.png +0 -0
- model_card/images/layer_0_attention_self_query.png +0 -0
- model_card/images/layer_0_attention_self_value.png +0 -0
- model_card/images/layer_0_intermediate_dense.png +0 -0
- model_card/images/layer_0_output_dense.png +0 -0
- model_card/images/layer_10_attention_output_dense.png +0 -0
- model_card/images/layer_10_attention_self_key.png +0 -0
- model_card/images/layer_10_attention_self_query.png +0 -0
- model_card/images/layer_10_attention_self_value.png +0 -0
- model_card/images/layer_10_intermediate_dense.png +0 -0
- model_card/images/layer_10_output_dense.png +0 -0
- model_card/images/layer_11_attention_output_dense.png +0 -0
- model_card/images/layer_11_attention_self_key.png +0 -0
- model_card/images/layer_11_attention_self_query.png +0 -0
- model_card/images/layer_11_attention_self_value.png +0 -0
- model_card/images/layer_11_intermediate_dense.png +0 -0
- model_card/images/layer_11_output_dense.png +0 -0
- model_card/images/layer_12_attention_output_dense.png +0 -0
- model_card/images/layer_12_attention_self_key.png +0 -0
- model_card/images/layer_12_attention_self_query.png +0 -0
- model_card/images/layer_12_attention_self_value.png +0 -0
- model_card/images/layer_12_intermediate_dense.png +0 -0
- model_card/images/layer_12_output_dense.png +0 -0
- model_card/images/layer_13_attention_output_dense.png +0 -0
- model_card/images/layer_13_attention_self_key.png +0 -0
- model_card/images/layer_13_attention_self_query.png +0 -0
- model_card/images/layer_13_attention_self_value.png +0 -0
- model_card/images/layer_13_intermediate_dense.png +0 -0
- model_card/images/layer_13_output_dense.png +0 -0
- model_card/images/layer_14_attention_output_dense.png +0 -0
- model_card/images/layer_14_attention_self_key.png +0 -0
- model_card/images/layer_14_attention_self_query.png +0 -0
- model_card/images/layer_14_attention_self_value.png +0 -0
- model_card/images/layer_14_intermediate_dense.png +0 -0
- model_card/images/layer_14_output_dense.png +0 -0
- model_card/images/layer_15_attention_output_dense.png +0 -0
- model_card/images/layer_15_attention_self_key.png +0 -0
- model_card/images/layer_15_attention_self_query.png +0 -0
- model_card/images/layer_15_attention_self_value.png +0 -0
- model_card/images/layer_15_intermediate_dense.png +0 -0
- model_card/images/layer_15_output_dense.png +0 -0
- model_card/images/layer_16_attention_output_dense.png +0 -0
- model_card/images/layer_16_attention_self_key.png +0 -0
- model_card/images/layer_16_attention_self_query.png +0 -0
- model_card/images/layer_16_attention_self_value.png +0 -0
- model_card/images/layer_16_intermediate_dense.png +0 -0
README.md
ADDED
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---
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language: en
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thumbnail:
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license: mit
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tags:
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- question-answering
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-
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-
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datasets:
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- squad_v2
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metrics:
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- squad_v2
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widget:
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- text: "Where is the Eiffel Tower located?"
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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."
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- text: "Who is Frederic Chopin?"
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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."
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---
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## BERT-base uncased model fine-tuned on SQuAD v1
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This model was created using the [nn_pruning](https://github.com/huggingface/nn_pruning) python library: the **linear layers contains 16.0%** of the original weights.
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The model contains **24.0%** of the original weights **overall** (the embeddings account for a significant part of the model, and they are not pruned by this method).
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With a simple resizing of the linear matrices it ran **2.63x as fast as bert-large-uncased-whole-word-masking** on the evaluation.
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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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<div class="graph"><script src="/madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1/raw/main/model_card/density_info.js" id="cddd6c5c-2e1d-40c7-b172-f7d5422349a6"></script></div>
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In terms of accuracy, its **F1 is 82.57**, compared with 85.85 for , a **F1 drop of 3.28**.
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## Fine-Pruning details
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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).
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This model is case-insensitive: it does not make a difference between english and English.
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A side-effect of the block pruning is that some of the attention heads are completely removed: 190 heads were removed on a total of 384 (49.5%).
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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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<div class="graph"><script src="/madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1/raw/main/model_card/pruning_info.js" id="03ad75cf-8048-44ae-a1d6-db69021cc168"></script></div>
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## Details of the SQuAD1.1 dataset
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| Dataset | Split | # samples |
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| -------- | ----- | --------- |
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| SQuAD 2.0 | train | 130.0K |
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| SQuAD 2.0 | eval | 11.9k |
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### Fine-tuning
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- Python: `3.8.5`
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- Machine specs:
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```CPU: Intel(R) Core(TM) i7-6700K CPU
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Memory: 64 GiB
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GPUs: 1 GeForce GTX 3090, with 24GiB memory
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GPU driver: 455.23.05, CUDA: 11.1
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```
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### Results
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**Pytorch model file size**: `1084MB` (original BERT: `1228.0MB`)
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| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))| Variation |
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+
| ------ | --------- | --------- | --------- |
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| **EM** | **79.70** | **82.83** | **-4.13**|
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| **F1** | **82.57** | **85.85** | **-3.28**|
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```
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{
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"HasAns_exact": 74.8144399460189,
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"HasAns_f1": 80.555306012496,
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"HasAns_total": 5928,
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"NoAns_exact": 84.57527333894029,
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"NoAns_f1": 84.57527333894029,
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"NoAns_total": 5945,
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"best_exact": 79.70184452118251,
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"best_exact_thresh": 0.0,
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"best_f1": 82.56816761071966,
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"best_f1_thresh": 0.0,
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"exact": 79.70184452118251,
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"f1": 82.56816761071981,
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"total": 11873
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}
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```
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## Example Usage
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Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
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`pip install nn_pruning`
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Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded.
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```python
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from transformers import pipeline
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from nn_pruning.inference_model_patcher import optimize_model
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qa_pipeline = pipeline(
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"question-answering",
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model="madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1",
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tokenizer="madlag/bert-large-uncased-wwm-squadv2-x2.63-f82.6-d16-hybrid-v1"
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)
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print("bert-large-uncased-whole-word-masking parameters: 445.0M")
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print(f"Parameters count (includes only head pruning, not feed forward pruning)={int(qa_pipeline.model.num_parameters() / 1E6)}M")
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qa_pipeline.model = optimize_model(qa_pipeline.model, "dense")
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109 |
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print(f"Parameters count after complete optimization={int(qa_pipeline.model.num_parameters() / 1E6)}M")
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110 |
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predictions = qa_pipeline({
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111 |
+
'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.",
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+
'question': "Who is Frederic Chopin?",
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+
})
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114 |
+
print("Predictions", predictions)
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115 |
+
```
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config.json
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{
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"_name_or_path": "/tmp/tmpitf3rdr5",
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"architectures": [
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"BertForQuestionAnswering"
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],
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"pruned_heads": {
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"0": [
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],
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"1": [
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"2": [
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],
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"3": [
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],
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"4": [
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],
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"5": [
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],
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"6": [
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119 |
+
9,
|
120 |
+
10,
|
121 |
+
11,
|
122 |
+
12,
|
123 |
+
13,
|
124 |
+
14
|
125 |
+
],
|
126 |
+
"8": [
|
127 |
+
3,
|
128 |
+
4,
|
129 |
+
5,
|
130 |
+
7,
|
131 |
+
8,
|
132 |
+
9,
|
133 |
+
10,
|
134 |
+
11,
|
135 |
+
12
|
136 |
+
],
|
137 |
+
"9": [
|
138 |
+
0,
|
139 |
+
1,
|
140 |
+
2,
|
141 |
+
3,
|
142 |
+
5,
|
143 |
+
6,
|
144 |
+
7,
|
145 |
+
9,
|
146 |
+
10,
|
147 |
+
13,
|
148 |
+
14,
|
149 |
+
15
|
150 |
+
],
|
151 |
+
"10": [
|
152 |
+
1,
|
153 |
+
2,
|
154 |
+
4,
|
155 |
+
5,
|
156 |
+
6,
|
157 |
+
8,
|
158 |
+
11,
|
159 |
+
13
|
160 |
+
],
|
161 |
+
"11": [
|
162 |
+
0,
|
163 |
+
2,
|
164 |
+
5,
|
165 |
+
6,
|
166 |
+
7,
|
167 |
+
8,
|
168 |
+
10,
|
169 |
+
12,
|
170 |
+
15
|
171 |
+
],
|
172 |
+
"12": [
|
173 |
+
0,
|
174 |
+
2,
|
175 |
+
6,
|
176 |
+
8,
|
177 |
+
9,
|
178 |
+
11,
|
179 |
+
13
|
180 |
+
],
|
181 |
+
"13": [
|
182 |
+
2,
|
183 |
+
6,
|
184 |
+
10,
|
185 |
+
12,
|
186 |
+
15
|
187 |
+
],
|
188 |
+
"14": [
|
189 |
+
1,
|
190 |
+
5,
|
191 |
+
6,
|
192 |
+
10,
|
193 |
+
11,
|
194 |
+
15
|
195 |
+
],
|
196 |
+
"15": [
|
197 |
+
0,
|
198 |
+
9
|
199 |
+
],
|
200 |
+
"16": [
|
201 |
+
5,
|
202 |
+
7
|
203 |
+
],
|
204 |
+
"17": [
|
205 |
+
1,
|
206 |
+
4,
|
207 |
+
8,
|
208 |
+
12,
|
209 |
+
14
|
210 |
+
],
|
211 |
+
"18": [
|
212 |
+
3,
|
213 |
+
4,
|
214 |
+
11
|
215 |
+
],
|
216 |
+
"19": [
|
217 |
+
0,
|
218 |
+
5,
|
219 |
+
12
|
220 |
+
],
|
221 |
+
"20": [
|
222 |
+
0,
|
223 |
+
4,
|
224 |
+
10,
|
225 |
+
12
|
226 |
+
],
|
227 |
+
"21": [
|
228 |
+
0,
|
229 |
+
2,
|
230 |
+
3,
|
231 |
+
4,
|
232 |
+
8,
|
233 |
+
11,
|
234 |
+
12,
|
235 |
+
15
|
236 |
+
],
|
237 |
+
"22": [
|
238 |
+
0,
|
239 |
+
1,
|
240 |
+
3,
|
241 |
+
4,
|
242 |
+
7,
|
243 |
+
9,
|
244 |
+
10,
|
245 |
+
11,
|
246 |
+
13,
|
247 |
+
15
|
248 |
+
],
|
249 |
+
"23": [
|
250 |
+
2,
|
251 |
+
4,
|
252 |
+
8,
|
253 |
+
9,
|
254 |
+
10,
|
255 |
+
13,
|
256 |
+
14,
|
257 |
+
15
|
258 |
+
]
|
259 |
+
},
|
260 |
+
"transformers_version": "4.5.1",
|
261 |
+
"type_vocab_size": 2,
|
262 |
+
"use_cache": true,
|
263 |
+
"vocab_size": 30522
|
264 |
+
}
|
model_card/density_info.js
ADDED
@@ -0,0 +1,174 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
(function() {
|
2 |
+
var fn = function() {
|
3 |
+
|
4 |
+
(function(root) {
|
5 |
+
function now() {
|
6 |
+
return new Date();
|
7 |
+
}
|
8 |
+
|
9 |
+
var force = false;
|
10 |
+
|
11 |
+
if (typeof root._bokeh_onload_callbacks === "undefined" || force === true) {
|
12 |
+
root._bokeh_onload_callbacks = [];
|
13 |
+
root._bokeh_is_loading = undefined;
|
14 |
+
}
|
15 |
+
|
16 |
+
|
17 |
+
|
18 |
+
|
19 |
+
var element = document.getElementById("cddd6c5c-2e1d-40c7-b172-f7d5422349a6");
|
20 |
+
if (element == null) {
|
21 |
+
console.warn("Bokeh: autoload.js configured with elementid 'cddd6c5c-2e1d-40c7-b172-f7d5422349a6' but no matching script tag was found.")
|
22 |
+
}
|
23 |
+
|
24 |
+
|
25 |
+
function run_callbacks() {
|
26 |
+
try {
|
27 |
+
root._bokeh_onload_callbacks.forEach(function(callback) {
|
28 |
+
if (callback != null)
|
29 |
+
callback();
|
30 |
+
});
|
31 |
+
} finally {
|
32 |
+
delete root._bokeh_onload_callbacks
|
33 |
+
}
|
34 |
+
console.debug("Bokeh: all callbacks have finished");
|
35 |
+
}
|
36 |
+
|
37 |
+
function load_libs(css_urls, js_urls, callback) {
|
38 |
+
if (css_urls == null) css_urls = [];
|
39 |
+
if (js_urls == null) js_urls = [];
|
40 |
+
|
41 |
+
root._bokeh_onload_callbacks.push(callback);
|
42 |
+
if (root._bokeh_is_loading > 0) {
|
43 |
+
console.debug("Bokeh: BokehJS is being loaded, scheduling callback at", now());
|
44 |
+
return null;
|
45 |
+
}
|
46 |
+
if (js_urls == null || js_urls.length === 0) {
|
47 |
+
run_callbacks();
|
48 |
+
return null;
|
49 |
+
}
|
50 |
+
console.debug("Bokeh: BokehJS not loaded, scheduling load and callback at", now());
|
51 |
+
root._bokeh_is_loading = css_urls.length + js_urls.length;
|
52 |
+
|
53 |
+
function on_load() {
|
54 |
+
root._bokeh_is_loading--;
|
55 |
+
if (root._bokeh_is_loading === 0) {
|
56 |
+
console.debug("Bokeh: all BokehJS libraries/stylesheets loaded");
|
57 |
+
run_callbacks()
|
58 |
+
}
|
59 |
+
}
|
60 |
+
|
61 |
+
function on_error() {
|
62 |
+
console.error("failed to load " + url);
|
63 |
+
}
|
64 |
+
|
65 |
+
for (var i = 0; i < css_urls.length; i++) {
|
66 |
+
var url = css_urls[i];
|
67 |
+
const element = document.createElement("link");
|
68 |
+
element.onload = on_load;
|
69 |
+
element.onerror = on_error;
|
70 |
+
element.rel = "stylesheet";
|
71 |
+
element.type = "text/css";
|
72 |
+
element.href = url;
|
73 |
+
console.debug("Bokeh: injecting link tag for BokehJS stylesheet: ", url);
|
74 |
+
document.body.appendChild(element);
|
75 |
+
}
|
76 |
+
|
77 |
+
const hashes = {"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js": "T2yuo9Oe71Cz/I4X9Ac5+gpEa5a8PpJCDlqKYO0CfAuEszu1JrXLl8YugMqYe3sM", "https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js": "98GDGJ0kOMCUMUePhksaQ/GYgB3+NH9h996V88sh3aOiUNX3N+fLXAtry6xctSZ6", "https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js": "89bArO+nlbP3sgakeHjCo1JYxYR5wufVgA3IbUvDY+K7w4zyxJqssu7wVnfeKCq8"};
|
78 |
+
|
79 |
+
for (var i = 0; i < js_urls.length; i++) {
|
80 |
+
var url = js_urls[i];
|
81 |
+
var element = document.createElement('script');
|
82 |
+
element.onload = on_load;
|
83 |
+
element.onerror = on_error;
|
84 |
+
element.async = false;
|
85 |
+
element.src = url;
|
86 |
+
if (url in hashes) {
|
87 |
+
element.crossOrigin = "anonymous";
|
88 |
+
element.integrity = "sha384-" + hashes[url];
|
89 |
+
}
|
90 |
+
console.debug("Bokeh: injecting script tag for BokehJS library: ", url);
|
91 |
+
document.head.appendChild(element);
|
92 |
+
}
|
93 |
+
};
|
94 |
+
|
95 |
+
function inject_raw_css(css) {
|
96 |
+
const element = document.createElement("style");
|
97 |
+
element.appendChild(document.createTextNode(css));
|
98 |
+
document.body.appendChild(element);
|
99 |
+
}
|
100 |
+
|
101 |
+
|
102 |
+
var js_urls = ["https://cdn.bokeh.org/bokeh/release/bokeh-2.2.3.min.js", "https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.3.min.js", "https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.3.min.js"];
|
103 |
+
var css_urls = [];
|
104 |
+
|
105 |
+
|
106 |
+
var inline_js = [
|
107 |
+
function(Bokeh) {
|
108 |
+
Bokeh.set_log_level("info");
|
109 |
+
},
|
110 |
+
|
111 |
+
function(Bokeh) {
|
112 |
+
(function() {
|
113 |
+
var fn = function() {
|
114 |
+
Bokeh.safely(function() {
|
115 |
+
(function(root) {
|
116 |
+
function embed_document(root) {
|
117 |
+
|
118 |
+
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var render_items = [{"docid":"6f8350c7-6159-4d2b-8e5f-df1c89f733ab","root_ids":["1095"],"roots":{"1095":"cddd6c5c-2e1d-40c7-b172-f7d5422349a6"}}];
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120 |
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root.Bokeh.embed.embed_items(docs_json, render_items);
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121 |
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122 |
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}
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123 |
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if (root.Bokeh !== undefined) {
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124 |
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embed_document(root);
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125 |
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} else {
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126 |
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var attempts = 0;
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127 |
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var timer = setInterval(function(root) {
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if (root.Bokeh !== undefined) {
|
129 |
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clearInterval(timer);
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130 |
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embed_document(root);
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132 |
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attempts++;
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133 |
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if (attempts > 100) {
|
134 |
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clearInterval(timer);
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135 |
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console.log("Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing");
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136 |
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}
|
138 |
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}, 10, root)
|
139 |
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}
|
140 |
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141 |
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});
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142 |
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};
|
143 |
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if (document.readyState != "loading") fn();
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else document.addEventListener("DOMContentLoaded", fn);
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145 |
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})();
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146 |
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},
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147 |
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function(Bokeh) {
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148 |
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|
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|
150 |
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|
151 |
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];
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|
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function run_inline_js() {
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155 |
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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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157 |
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}
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158 |
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159 |
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}
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160 |
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161 |
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if (root._bokeh_is_loading === 0) {
|
162 |
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console.debug("Bokeh: BokehJS loaded, going straight to plotting");
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163 |
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run_inline_js();
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164 |
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} else {
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165 |
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load_libs(css_urls, js_urls, function() {
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166 |
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console.debug("Bokeh: BokehJS plotting callback run at", now());
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167 |
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run_inline_js();
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168 |
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});
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}
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170 |
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171 |
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model_card/images/layer_0_attention_self_key.png
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model_card/images/layer_0_attention_self_query.png
ADDED
model_card/images/layer_0_attention_self_value.png
ADDED
model_card/images/layer_0_intermediate_dense.png
ADDED
model_card/images/layer_0_output_dense.png
ADDED
model_card/images/layer_10_attention_output_dense.png
ADDED
model_card/images/layer_10_attention_self_key.png
ADDED
model_card/images/layer_10_attention_self_query.png
ADDED
model_card/images/layer_10_attention_self_value.png
ADDED
model_card/images/layer_10_intermediate_dense.png
ADDED
model_card/images/layer_10_output_dense.png
ADDED
model_card/images/layer_11_attention_output_dense.png
ADDED
model_card/images/layer_11_attention_self_key.png
ADDED
model_card/images/layer_11_attention_self_query.png
ADDED
model_card/images/layer_11_attention_self_value.png
ADDED
model_card/images/layer_11_intermediate_dense.png
ADDED
model_card/images/layer_11_output_dense.png
ADDED
model_card/images/layer_12_attention_output_dense.png
ADDED
model_card/images/layer_12_attention_self_key.png
ADDED
model_card/images/layer_12_attention_self_query.png
ADDED
model_card/images/layer_12_attention_self_value.png
ADDED
model_card/images/layer_12_intermediate_dense.png
ADDED
model_card/images/layer_12_output_dense.png
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model_card/images/layer_13_attention_output_dense.png
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model_card/images/layer_13_attention_self_query.png
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model_card/images/layer_13_intermediate_dense.png
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model_card/images/layer_14_attention_output_dense.png
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model_card/images/layer_14_attention_self_key.png
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model_card/images/layer_14_attention_self_query.png
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model_card/images/layer_14_attention_self_value.png
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model_card/images/layer_14_intermediate_dense.png
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model_card/images/layer_15_attention_output_dense.png
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model_card/images/layer_15_attention_self_value.png
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model_card/images/layer_16_attention_output_dense.png
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model_card/images/layer_16_attention_self_query.png
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model_card/images/layer_16_attention_self_value.png
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model_card/images/layer_16_intermediate_dense.png
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