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base_model: cointegrated/LaBSE-en-ru
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sentences:
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- been nice talking to you
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name: Pearson
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name: Spearman
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name: Pearson
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name: Spearman
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name: Pearson
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name: Spearman
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name:
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<!--
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-
##
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-
*
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|
| 706 |
-->
|
|
|
|
| 1 |
+
---
|
| 2 |
+
base_model: cointegrated/LaBSE-en-ru
|
| 3 |
+
language:
|
| 4 |
+
- ru
|
| 5 |
+
- en
|
| 6 |
+
library_name: sentence-transformers
|
| 7 |
+
metrics:
|
| 8 |
+
- pearson_cosine
|
| 9 |
+
- spearman_cosine
|
| 10 |
+
- pearson_manhattan
|
| 11 |
+
- spearman_manhattan
|
| 12 |
+
- pearson_euclidean
|
| 13 |
+
- spearman_euclidean
|
| 14 |
+
- pearson_dot
|
| 15 |
+
- spearman_dot
|
| 16 |
+
- pearson_max
|
| 17 |
+
- spearman_max
|
| 18 |
+
- negative_mse
|
| 19 |
+
pipeline_tag: sentence-similarity
|
| 20 |
+
tags:
|
| 21 |
+
- sentence-transformers
|
| 22 |
+
- sentence-similarity
|
| 23 |
+
- feature-extraction
|
| 24 |
+
- generated_from_trainer
|
| 25 |
+
- dataset_size:10975066
|
| 26 |
+
- loss:MSELoss
|
| 27 |
+
widget:
|
| 28 |
+
- source_sentence: Такие лодки строились, чтобы получить быстрый доступ к приходящим судам.
|
| 29 |
+
sentences:
|
| 30 |
+
- been nice talking to you
|
| 31 |
+
- >-
|
| 32 |
+
Нельзя ставить под сомнение притязания клиента, если не были предприняты
|
| 33 |
+
шаги.
|
| 34 |
+
- >-
|
| 35 |
+
Dharangaon Railway Station serves Dharangaon in Jalgaon district in the
|
| 36 |
+
Indian state of Maharashtra.
|
| 37 |
+
- source_sentence: >-
|
| 38 |
+
Если прилагательные смягчают этнические термины, существительные могут
|
| 39 |
+
сделать их жестче.
|
| 40 |
+
sentences:
|
| 41 |
+
- >-
|
| 42 |
+
Вслед за этим последовало секретное письмо А.Б.Чубайса об изъятии у МЦР,
|
| 43 |
+
переданного ему С.Н.Рерихом наследия.
|
| 44 |
+
- Coaches should not give young athletes a hard time.
|
| 45 |
+
- Эшкрофт хотел прослушивать сводки новостей снова и снова
|
| 46 |
+
- source_sentence: Земля была мягкой.
|
| 47 |
+
sentences:
|
| 48 |
+
- >-
|
| 49 |
+
По мере того, как самообладание покидало его, с��рдце его все больше
|
| 50 |
+
наполнялось тревогой.
|
| 51 |
+
- >-
|
| 52 |
+
Our borders and immigration system, including law enforcement, ought to send
|
| 53 |
+
a message of welcome, tolerance, and justice to members of immigrant
|
| 54 |
+
communities in the United States and in their countries of origin.
|
| 55 |
+
- >-
|
| 56 |
+
Начнут действовать льготные условия аренды земель, которые предназначены для
|
| 57 |
+
реализации инвестиционных проектов.
|
| 58 |
+
- source_sentence: >-
|
| 59 |
+
Что же касается рава Кука: мой рав лично знал его и много раз с теплотой
|
| 60 |
+
рассказывал мне о нем как о великом каббалисте.
|
| 61 |
+
sentences:
|
| 62 |
+
- Вдова Эдгара Эванса, его дети и мать получили 1500 фунтов стерлингов (
|
| 63 |
+
- Please do not make any changes to your address.
|
| 64 |
+
- Мы уже закончили все запланированные дела!
|
| 65 |
+
- source_sentence: See Name section.
|
| 66 |
+
sentences:
|
| 67 |
+
- >-
|
| 68 |
+
Ms. Packard is the voice of the female blood elf in the video game World of
|
| 69 |
+
Warcraft.
|
| 70 |
+
- >-
|
| 71 |
+
Основным функциональным элементом, реализующим функции управления
|
| 72 |
+
соединением, является абонентский терминал.
|
| 73 |
+
- Yeah, people who might not be hungry.
|
| 74 |
+
model-index:
|
| 75 |
+
- name: SentenceTransformer based on cointegrated/LaBSE-en-ru
|
| 76 |
+
results:
|
| 77 |
+
- task:
|
| 78 |
+
type: semantic-similarity
|
| 79 |
+
name: Semantic Similarity
|
| 80 |
+
dataset:
|
| 81 |
+
name: sts dev
|
| 82 |
+
type: sts-dev
|
| 83 |
+
metrics:
|
| 84 |
+
- type: pearson_cosine
|
| 85 |
+
value: 0.5305176535187099
|
| 86 |
+
name: Pearson Cosine
|
| 87 |
+
- type: spearman_cosine
|
| 88 |
+
value: 0.6347069834349862
|
| 89 |
+
name: Spearman Cosine
|
| 90 |
+
- type: pearson_manhattan
|
| 91 |
+
value: 0.5553415140113596
|
| 92 |
+
name: Pearson Manhattan
|
| 93 |
+
- type: spearman_manhattan
|
| 94 |
+
value: 0.6389336208598283
|
| 95 |
+
name: Spearman Manhattan
|
| 96 |
+
- type: pearson_euclidean
|
| 97 |
+
value: 0.5499910306125031
|
| 98 |
+
name: Pearson Euclidean
|
| 99 |
+
- type: spearman_euclidean
|
| 100 |
+
value: 0.6347073809507647
|
| 101 |
+
name: Spearman Euclidean
|
| 102 |
+
- type: pearson_dot
|
| 103 |
+
value: 0.5305176585564861
|
| 104 |
+
name: Pearson Dot
|
| 105 |
+
- type: spearman_dot
|
| 106 |
+
value: 0.6347078463557637
|
| 107 |
+
name: Spearman Dot
|
| 108 |
+
- type: pearson_max
|
| 109 |
+
value: 0.5553415140113596
|
| 110 |
+
name: Pearson Max
|
| 111 |
+
- type: spearman_max
|
| 112 |
+
value: 0.6389336208598283
|
| 113 |
+
name: Spearman Max
|
| 114 |
+
- task:
|
| 115 |
+
type: knowledge-distillation
|
| 116 |
+
name: Knowledge Distillation
|
| 117 |
+
dataset:
|
| 118 |
+
name: Unknown
|
| 119 |
+
type: unknown
|
| 120 |
+
metrics:
|
| 121 |
+
- type: negative_mse
|
| 122 |
+
value: -0.006337030936265364
|
| 123 |
+
name: Negative Mse
|
| 124 |
+
- task:
|
| 125 |
+
type: semantic-similarity
|
| 126 |
+
name: Semantic Similarity
|
| 127 |
+
dataset:
|
| 128 |
+
name: sts test
|
| 129 |
+
type: sts-test
|
| 130 |
+
metrics:
|
| 131 |
+
- type: pearson_cosine
|
| 132 |
+
value: 0.5042796836494269
|
| 133 |
+
name: Pearson Cosine
|
| 134 |
+
- type: spearman_cosine
|
| 135 |
+
value: 0.5986471772428711
|
| 136 |
+
name: Spearman Cosine
|
| 137 |
+
- type: pearson_manhattan
|
| 138 |
+
value: 0.522744495080616
|
| 139 |
+
name: Pearson Manhattan
|
| 140 |
+
- type: spearman_manhattan
|
| 141 |
+
value: 0.5983901280447074
|
| 142 |
+
name: Spearman Manhattan
|
| 143 |
+
- type: pearson_euclidean
|
| 144 |
+
value: 0.522721961447153
|
| 145 |
+
name: Pearson Euclidean
|
| 146 |
+
- type: spearman_euclidean
|
| 147 |
+
value: 0.5986471095414022
|
| 148 |
+
name: Spearman Euclidean
|
| 149 |
+
- type: pearson_dot
|
| 150 |
+
value: 0.504279685613151
|
| 151 |
+
name: Pearson Dot
|
| 152 |
+
- type: spearman_dot
|
| 153 |
+
value: 0.598648155615724
|
| 154 |
+
name: Spearman Dot
|
| 155 |
+
- type: pearson_max
|
| 156 |
+
value: 0.522744495080616
|
| 157 |
+
name: Pearson Max
|
| 158 |
+
- type: spearman_max
|
| 159 |
+
value: 0.598648155615724
|
| 160 |
+
name: Spearman Max
|
| 161 |
+
---
|
| 162 |
+
|
| 163 |
+
# SentenceTransformer based on cointegrated/LaBSE-en-ru
|
| 164 |
+
|
| 165 |
+
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
|
| 166 |
+
|
| 167 |
+
## Model Details
|
| 168 |
+
|
| 169 |
+
### Model Description
|
| 170 |
+
- **Model Type:** Sentence Transformer
|
| 171 |
+
- **Base model:** [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru) <!-- at revision cf0714e606d4af551e14ad69a7929cd6b0da7f7e -->
|
| 172 |
+
- **Maximum Sequence Length:** 512 tokens
|
| 173 |
+
- **Output Dimensionality:** 768 tokens
|
| 174 |
+
- **Similarity Function:** Cosine Similarity
|
| 175 |
+
<!-- - **Training Dataset:** Unknown -->
|
| 176 |
+
<!-- - **Language:** Unknown -->
|
| 177 |
+
<!-- - **License:** Unknown -->
|
| 178 |
+
|
| 179 |
+
### Model Sources
|
| 180 |
+
|
| 181 |
+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| 182 |
+
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
|
| 183 |
+
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
| 184 |
+
|
| 185 |
+
### Full Model Architecture
|
| 186 |
+
|
| 187 |
+
```
|
| 188 |
+
SentenceTransformer(
|
| 189 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
| 190 |
+
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
| 191 |
+
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
|
| 192 |
+
(3): Normalize()
|
| 193 |
+
)
|
| 194 |
+
```
|
| 195 |
+
|
| 196 |
+
## Usage
|
| 197 |
+
|
| 198 |
+
### Direct Usage (Sentence Transformers)
|
| 199 |
+
|
| 200 |
+
First install the Sentence Transformers library:
|
| 201 |
+
|
| 202 |
+
```bash
|
| 203 |
+
pip install -U sentence-transformers
|
| 204 |
+
```
|
| 205 |
+
|
| 206 |
+
Then you can load this model and run inference.
|
| 207 |
+
```python
|
| 208 |
+
from sentence_transformers import SentenceTransformer
|
| 209 |
+
|
| 210 |
+
# Download from the 🤗 Hub
|
| 211 |
+
model = SentenceTransformer("whitemouse84/LaBSE-en-ru-distilled-each-third-layer")
|
| 212 |
+
# Run inference
|
| 213 |
+
sentences = [
|
| 214 |
+
'See Name section.',
|
| 215 |
+
'Ms. Packard is the voice of the female blood elf in the video game World of Warcraft.',
|
| 216 |
+
'Yeah, people who might not be hungry.',
|
| 217 |
+
]
|
| 218 |
+
embeddings = model.encode(sentences)
|
| 219 |
+
print(embeddings.shape)
|
| 220 |
+
# [3, 768]
|
| 221 |
+
|
| 222 |
+
# Get the similarity scores for the embeddings
|
| 223 |
+
similarities = model.similarity(embeddings, embeddings)
|
| 224 |
+
print(similarities.shape)
|
| 225 |
+
# [3, 3]
|
| 226 |
+
```
|
| 227 |
+
|
| 228 |
+
<!--
|
| 229 |
+
### Direct Usage (Transformers)
|
| 230 |
+
|
| 231 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
| 232 |
+
|
| 233 |
+
</details>
|
| 234 |
+
-->
|
| 235 |
+
|
| 236 |
+
<!--
|
| 237 |
+
### Downstream Usage (Sentence Transformers)
|
| 238 |
+
|
| 239 |
+
You can finetune this model on your own dataset.
|
| 240 |
+
|
| 241 |
+
<details><summary>Click to expand</summary>
|
| 242 |
+
|
| 243 |
+
</details>
|
| 244 |
+
-->
|
| 245 |
+
|
| 246 |
+
<!--
|
| 247 |
+
### Out-of-Scope Use
|
| 248 |
+
|
| 249 |
+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| 250 |
+
-->
|
| 251 |
+
|
| 252 |
+
## Evaluation
|
| 253 |
+
|
| 254 |
+
### Metrics
|
| 255 |
+
|
| 256 |
+
#### Semantic Similarity
|
| 257 |
+
* Dataset: `sts-dev`
|
| 258 |
+
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 259 |
+
|
| 260 |
+
| Metric | Value |
|
| 261 |
+
|:--------------------|:-----------|
|
| 262 |
+
| pearson_cosine | 0.5305 |
|
| 263 |
+
| **spearman_cosine** | **0.6347** |
|
| 264 |
+
| pearson_manhattan | 0.5553 |
|
| 265 |
+
| spearman_manhattan | 0.6389 |
|
| 266 |
+
| pearson_euclidean | 0.55 |
|
| 267 |
+
| spearman_euclidean | 0.6347 |
|
| 268 |
+
| pearson_dot | 0.5305 |
|
| 269 |
+
| spearman_dot | 0.6347 |
|
| 270 |
+
| pearson_max | 0.5553 |
|
| 271 |
+
| spearman_max | 0.6389 |
|
| 272 |
+
|
| 273 |
+
#### Knowledge Distillation
|
| 274 |
+
|
| 275 |
+
* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
|
| 276 |
+
|
| 277 |
+
| Metric | Value |
|
| 278 |
+
|:-----------------|:------------|
|
| 279 |
+
| **negative_mse** | **-0.0063** |
|
| 280 |
+
|
| 281 |
+
#### Semantic Similarity
|
| 282 |
+
* Dataset: `sts-test`
|
| 283 |
+
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
|
| 284 |
+
|
| 285 |
+
| Metric | Value |
|
| 286 |
+
|:--------------------|:-----------|
|
| 287 |
+
| pearson_cosine | 0.5043 |
|
| 288 |
+
| **spearman_cosine** | **0.5986** |
|
| 289 |
+
| pearson_manhattan | 0.5227 |
|
| 290 |
+
| spearman_manhattan | 0.5984 |
|
| 291 |
+
| pearson_euclidean | 0.5227 |
|
| 292 |
+
| spearman_euclidean | 0.5986 |
|
| 293 |
+
| pearson_dot | 0.5043 |
|
| 294 |
+
| spearman_dot | 0.5986 |
|
| 295 |
+
| pearson_max | 0.5227 |
|
| 296 |
+
| spearman_max | 0.5986 |
|
| 297 |
+
|
| 298 |
+
<!--
|
| 299 |
+
## Bias, Risks and Limitations
|
| 300 |
+
|
| 301 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
| 302 |
+
-->
|
| 303 |
+
|
| 304 |
+
<!--
|
| 305 |
+
### Recommendations
|
| 306 |
+
|
| 307 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| 308 |
+
-->
|
| 309 |
+
|
| 310 |
+
## Training Details
|
| 311 |
+
|
| 312 |
+
### Training Dataset
|
| 313 |
+
|
| 314 |
+
#### Unnamed Dataset
|
| 315 |
+
|
| 316 |
+
|
| 317 |
+
* Size: 10,975,066 training samples
|
| 318 |
+
* Columns: <code>sentence</code> and <code>label</code>
|
| 319 |
+
* Approximate statistics based on the first 1000 samples:
|
| 320 |
+
| | sentence | label |
|
| 321 |
+
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------|
|
| 322 |
+
| type | string | list |
|
| 323 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 26.93 tokens</li><li>max: 139 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
|
| 324 |
+
* Samples:
|
| 325 |
+
| sentence | label |
|
| 326 |
+
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
|
| 327 |
+
| <code>It is based on the Java Persistence API (JPA), but it does not strictly follow the JSR 338 Specification, as it implements different design patterns and technologies.</code> | <code>[-0.012331949546933174, -0.04570527374744415, -0.024963658303022385, -0.03620213270187378, 0.022556383162736893, ...]</code> |
|
| 328 |
+
| <code>Покупаем вторичное сырье в Каунасе (Переработка вторичного сырья) - Алфенас АНД КО, ЗАО на Bizorg.</code> | <code>[-0.07498518377542496, -0.01913534104824066, -0.01797042042016983, 0.048263177275657654, -0.00016611881437711418, ...]</code> |
|
| 329 |
+
| <code>At the Equal Justice Conference ( EJC ) held in March 2001 in San Diego , LSC and the Project for the Future of Equal Justice held the second Case Management Software pre-conference .</code> | <code>[0.03870972990989685, -0.0638347640633583, -0.01696585863828659, -0.043612319976091385, -0.048241738229990005, ...]</code> |
|
| 330 |
+
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
|
| 331 |
+
|
| 332 |
+
### Evaluation Dataset
|
| 333 |
+
|
| 334 |
+
#### Unnamed Dataset
|
| 335 |
+
|
| 336 |
+
|
| 337 |
+
* Size: 10,000 evaluation samples
|
| 338 |
+
* Columns: <code>sentence</code> and <code>label</code>
|
| 339 |
+
* Approximate statistics based on the first 1000 samples:
|
| 340 |
+
| | sentence | label |
|
| 341 |
+
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------|
|
| 342 |
+
| type | string | list |
|
| 343 |
+
| details | <ul><li>min: 5 tokens</li><li>mean: 24.18 tokens</li><li>max: 111 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
|
| 344 |
+
* Samples:
|
| 345 |
+
| sentence | label |
|
| 346 |
+
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
|
| 347 |
+
| <code>The Canadian Canoe Museum is a museum dedicated to canoes located in Peterborough, Ontario, Canada.</code> | <code>[-0.05444105342030525, -0.03650881350040436, -0.041163671761751175, -0.010616903193295002, -0.04094529151916504, ...]</code> |
|
| 348 |
+
| <code>И мне нравилось, что я одновременно зарабатываю и смотрю бои».</code> | <code>[-0.03404555842280388, 0.028203096240758896, -0.056121889501810074, -0.0591997392475605, -0.05523117259144783, ...]</code> |
|
| 349 |
+
| <code>Ну, а на следующий день, разумеется, Президент Кеннеди объявил блокаду Кубы, и наши корабли остановили у кубинских берегов направлявшийся на Кубу российский корабль, и у него на борту нашли ракеты.</code> | <code>[-0.008193841204047203, 0.00694894278421998, -0.03027420863509178, -0.03290146216750145, 0.01425305474549532, ...]</code> |
|
| 350 |
+
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
|
| 351 |
+
|
| 352 |
+
### Training Hyperparameters
|
| 353 |
+
#### Non-Default Hyperparameters
|
| 354 |
+
|
| 355 |
+
- `eval_strategy`: steps
|
| 356 |
+
- `per_device_train_batch_size`: 64
|
| 357 |
+
- `per_device_eval_batch_size`: 64
|
| 358 |
+
- `learning_rate`: 0.0001
|
| 359 |
+
- `num_train_epochs`: 1
|
| 360 |
+
- `warmup_ratio`: 0.1
|
| 361 |
+
- `fp16`: True
|
| 362 |
+
- `load_best_model_at_end`: True
|
| 363 |
+
|
| 364 |
+
#### All Hyperparameters
|
| 365 |
+
<details><summary>Click to expand</summary>
|
| 366 |
+
|
| 367 |
+
- `overwrite_output_dir`: False
|
| 368 |
+
- `do_predict`: False
|
| 369 |
+
- `eval_strategy`: steps
|
| 370 |
+
- `prediction_loss_only`: True
|
| 371 |
+
- `per_device_train_batch_size`: 64
|
| 372 |
+
- `per_device_eval_batch_size`: 64
|
| 373 |
+
- `per_gpu_train_batch_size`: None
|
| 374 |
+
- `per_gpu_eval_batch_size`: None
|
| 375 |
+
- `gradient_accumulation_steps`: 1
|
| 376 |
+
- `eval_accumulation_steps`: None
|
| 377 |
+
- `torch_empty_cache_steps`: None
|
| 378 |
+
- `learning_rate`: 0.0001
|
| 379 |
+
- `weight_decay`: 0.0
|
| 380 |
+
- `adam_beta1`: 0.9
|
| 381 |
+
- `adam_beta2`: 0.999
|
| 382 |
+
- `adam_epsilon`: 1e-08
|
| 383 |
+
- `max_grad_norm`: 1.0
|
| 384 |
+
- `num_train_epochs`: 1
|
| 385 |
+
- `max_steps`: -1
|
| 386 |
+
- `lr_scheduler_type`: linear
|
| 387 |
+
- `lr_scheduler_kwargs`: {}
|
| 388 |
+
- `warmup_ratio`: 0.1
|
| 389 |
+
- `warmup_steps`: 0
|
| 390 |
+
- `log_level`: passive
|
| 391 |
+
- `log_level_replica`: warning
|
| 392 |
+
- `log_on_each_node`: True
|
| 393 |
+
- `logging_nan_inf_filter`: True
|
| 394 |
+
- `save_safetensors`: True
|
| 395 |
+
- `save_on_each_node`: False
|
| 396 |
+
- `save_only_model`: False
|
| 397 |
+
- `restore_callback_states_from_checkpoint`: False
|
| 398 |
+
- `no_cuda`: False
|
| 399 |
+
- `use_cpu`: False
|
| 400 |
+
- `use_mps_device`: False
|
| 401 |
+
- `seed`: 42
|
| 402 |
+
- `data_seed`: None
|
| 403 |
+
- `jit_mode_eval`: False
|
| 404 |
+
- `use_ipex`: False
|
| 405 |
+
- `bf16`: False
|
| 406 |
+
- `fp16`: True
|
| 407 |
+
- `fp16_opt_level`: O1
|
| 408 |
+
- `half_precision_backend`: auto
|
| 409 |
+
- `bf16_full_eval`: False
|
| 410 |
+
- `fp16_full_eval`: False
|
| 411 |
+
- `tf32`: None
|
| 412 |
+
- `local_rank`: 0
|
| 413 |
+
- `ddp_backend`: None
|
| 414 |
+
- `tpu_num_cores`: None
|
| 415 |
+
- `tpu_metrics_debug`: False
|
| 416 |
+
- `debug`: []
|
| 417 |
+
- `dataloader_drop_last`: False
|
| 418 |
+
- `dataloader_num_workers`: 0
|
| 419 |
+
- `dataloader_prefetch_factor`: None
|
| 420 |
+
- `past_index`: -1
|
| 421 |
+
- `disable_tqdm`: False
|
| 422 |
+
- `remove_unused_columns`: True
|
| 423 |
+
- `label_names`: None
|
| 424 |
+
- `load_best_model_at_end`: True
|
| 425 |
+
- `ignore_data_skip`: False
|
| 426 |
+
- `fsdp`: []
|
| 427 |
+
- `fsdp_min_num_params`: 0
|
| 428 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| 429 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
| 430 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| 431 |
+
- `deepspeed`: None
|
| 432 |
+
- `label_smoothing_factor`: 0.0
|
| 433 |
+
- `optim`: adamw_torch
|
| 434 |
+
- `optim_args`: None
|
| 435 |
+
- `adafactor`: False
|
| 436 |
+
- `group_by_length`: False
|
| 437 |
+
- `length_column_name`: length
|
| 438 |
+
- `ddp_find_unused_parameters`: None
|
| 439 |
+
- `ddp_bucket_cap_mb`: None
|
| 440 |
+
- `ddp_broadcast_buffers`: False
|
| 441 |
+
- `dataloader_pin_memory`: True
|
| 442 |
+
- `dataloader_persistent_workers`: False
|
| 443 |
+
- `skip_memory_metrics`: True
|
| 444 |
+
- `use_legacy_prediction_loop`: False
|
| 445 |
+
- `push_to_hub`: False
|
| 446 |
+
- `resume_from_checkpoint`: None
|
| 447 |
+
- `hub_model_id`: None
|
| 448 |
+
- `hub_strategy`: every_save
|
| 449 |
+
- `hub_private_repo`: False
|
| 450 |
+
- `hub_always_push`: False
|
| 451 |
+
- `gradient_checkpointing`: False
|
| 452 |
+
- `gradient_checkpointing_kwargs`: None
|
| 453 |
+
- `include_inputs_for_metrics`: False
|
| 454 |
+
- `eval_do_concat_batches`: True
|
| 455 |
+
- `fp16_backend`: auto
|
| 456 |
+
- `push_to_hub_model_id`: None
|
| 457 |
+
- `push_to_hub_organization`: None
|
| 458 |
+
- `mp_parameters`:
|
| 459 |
+
- `auto_find_batch_size`: False
|
| 460 |
+
- `full_determinism`: False
|
| 461 |
+
- `torchdynamo`: None
|
| 462 |
+
- `ray_scope`: last
|
| 463 |
+
- `ddp_timeout`: 1800
|
| 464 |
+
- `torch_compile`: False
|
| 465 |
+
- `torch_compile_backend`: None
|
| 466 |
+
- `torch_compile_mode`: None
|
| 467 |
+
- `dispatch_batches`: None
|
| 468 |
+
- `split_batches`: None
|
| 469 |
+
- `include_tokens_per_second`: False
|
| 470 |
+
- `include_num_input_tokens_seen`: False
|
| 471 |
+
- `neftune_noise_alpha`: None
|
| 472 |
+
- `optim_target_modules`: None
|
| 473 |
+
- `batch_eval_metrics`: False
|
| 474 |
+
- `eval_on_start`: False
|
| 475 |
+
- `eval_use_gather_object`: False
|
| 476 |
+
- `batch_sampler`: batch_sampler
|
| 477 |
+
- `multi_dataset_batch_sampler`: proportional
|
| 478 |
+
|
| 479 |
+
</details>
|
| 480 |
+
|
| 481 |
+
### Training Logs
|
| 482 |
+
<details><summary>Click to expand</summary>
|
| 483 |
+
|
| 484 |
+
| Epoch | Step | Training Loss | loss | negative_mse | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|
| 485 |
+
|:----------:|:--------:|:-------------:|:----------:|:------------:|:-----------------------:|:------------------------:|
|
| 486 |
+
| 0 | 0 | - | - | -0.2381 | 0.4206 | - |
|
| 487 |
+
| 0.0058 | 1000 | 0.0014 | - | - | - | - |
|
| 488 |
+
| 0.0117 | 2000 | 0.0009 | - | - | - | - |
|
| 489 |
+
| 0.0175 | 3000 | 0.0007 | - | - | - | - |
|
| 490 |
+
| 0.0233 | 4000 | 0.0006 | - | - | - | - |
|
| 491 |
+
| **0.0292** | **5000** | **0.0005** | **0.0004** | **-0.0363** | **0.6393** | **-** |
|
| 492 |
+
| 0.0350 | 6000 | 0.0004 | - | - | - | - |
|
| 493 |
+
| 0.0408 | 7000 | 0.0004 | - | - | - | - |
|
| 494 |
+
| 0.0467 | 8000 | 0.0003 | - | - | - | - |
|
| 495 |
+
| 0.0525 | 9000 | 0.0003 | - | - | - | - |
|
| 496 |
+
| 0.0583 | 10000 | 0.0003 | 0.0002 | -0.0207 | 0.6350 | - |
|
| 497 |
+
| 0.0641 | 11000 | 0.0003 | - | - | - | - |
|
| 498 |
+
| 0.0700 | 12000 | 0.0003 | - | - | - | - |
|
| 499 |
+
| 0.0758 | 13000 | 0.0002 | - | - | - | - |
|
| 500 |
+
| 0.0816 | 14000 | 0.0002 | - | - | - | - |
|
| 501 |
+
| 0.0875 | 15000 | 0.0002 | 0.0002 | -0.0157 | 0.6328 | - |
|
| 502 |
+
| 0.0933 | 16000 | 0.0002 | - | - | - | - |
|
| 503 |
+
| 0.0991 | 17000 | 0.0002 | - | - | - | - |
|
| 504 |
+
| 0.1050 | 18000 | 0.0002 | - | - | - | - |
|
| 505 |
+
| 0.1108 | 19000 | 0.0002 | - | - | - | - |
|
| 506 |
+
| 0.1166 | 20000 | 0.0002 | 0.0001 | -0.0132 | 0.6317 | - |
|
| 507 |
+
| 0.1225 | 21000 | 0.0002 | - | - | - | - |
|
| 508 |
+
| 0.1283 | 22000 | 0.0002 | - | - | - | - |
|
| 509 |
+
| 0.1341 | 23000 | 0.0002 | - | - | - | - |
|
| 510 |
+
| 0.1400 | 24000 | 0.0002 | - | - | - | - |
|
| 511 |
+
| 0.1458 | 25000 | 0.0002 | 0.0001 | -0.0118 | 0.6251 | - |
|
| 512 |
+
| 0.1516 | 26000 | 0.0002 | - | - | - | - |
|
| 513 |
+
| 0.1574 | 27000 | 0.0002 | - | - | - | - |
|
| 514 |
+
| 0.1633 | 28000 | 0.0002 | - | - | - | - |
|
| 515 |
+
| 0.1691 | 29000 | 0.0002 | - | - | - | - |
|
| 516 |
+
| 0.1749 | 30000 | 0.0002 | 0.0001 | -0.0109 | 0.6304 | - |
|
| 517 |
+
| 0.1808 | 31000 | 0.0002 | - | - | - | - |
|
| 518 |
+
| 0.1866 | 32000 | 0.0002 | - | - | - | - |
|
| 519 |
+
| 0.1924 | 33000 | 0.0002 | - | - | - | - |
|
| 520 |
+
| 0.1983 | 34000 | 0.0001 | - | - | - | - |
|
| 521 |
+
| 0.2041 | 35000 | 0.0001 | 0.0001 | -0.0102 | 0.6280 | - |
|
| 522 |
+
| 0.2099 | 36000 | 0.0001 | - | - | - | - |
|
| 523 |
+
| 0.2158 | 37000 | 0.0001 | - | - | - | - |
|
| 524 |
+
| 0.2216 | 38000 | 0.0001 | - | - | - | - |
|
| 525 |
+
| 0.2274 | 39000 | 0.0001 | - | - | - | - |
|
| 526 |
+
| 0.2333 | 40000 | 0.0001 | 0.0001 | -0.0098 | 0.6272 | - |
|
| 527 |
+
| 0.2391 | 41000 | 0.0001 | - | - | - | - |
|
| 528 |
+
| 0.2449 | 42000 | 0.0001 | - | - | - | - |
|
| 529 |
+
| 0.2507 | 43000 | 0.0001 | - | - | - | - |
|
| 530 |
+
| 0.2566 | 44000 | 0.0001 | - | - | - | - |
|
| 531 |
+
| 0.2624 | 45000 | 0.0001 | 0.0001 | -0.0093 | 0.6378 | - |
|
| 532 |
+
| 0.2682 | 46000 | 0.0001 | - | - | - | - |
|
| 533 |
+
| 0.2741 | 47000 | 0.0001 | - | - | - | - |
|
| 534 |
+
| 0.2799 | 48000 | 0.0001 | - | - | - | - |
|
| 535 |
+
| 0.2857 | 49000 | 0.0001 | - | - | - | - |
|
| 536 |
+
| 0.2916 | 50000 | 0.0001 | 0.0001 | -0.0089 | 0.6325 | - |
|
| 537 |
+
| 0.2974 | 51000 | 0.0001 | - | - | - | - |
|
| 538 |
+
| 0.3032 | 52000 | 0.0001 | - | - | - | - |
|
| 539 |
+
| 0.3091 | 53000 | 0.0001 | - | - | - | - |
|
| 540 |
+
| 0.3149 | 54000 | 0.0001 | - | - | - | - |
|
| 541 |
+
| 0.3207 | 55000 | 0.0001 | 0.0001 | -0.0087 | 0.6328 | - |
|
| 542 |
+
| 0.3266 | 56000 | 0.0001 | - | - | - | - |
|
| 543 |
+
| 0.3324 | 57000 | 0.0001 | - | - | - | - |
|
| 544 |
+
| 0.3382 | 58000 | 0.0001 | - | - | - | - |
|
| 545 |
+
| 0.3441 | 59000 | 0.0001 | - | - | - | - |
|
| 546 |
+
| 0.3499 | 60000 | 0.0001 | 0.0001 | -0.0085 | 0.6357 | - |
|
| 547 |
+
| 0.3557 | 61000 | 0.0001 | - | - | - | - |
|
| 548 |
+
| 0.3615 | 62000 | 0.0001 | - | - | - | - |
|
| 549 |
+
| 0.3674 | 63000 | 0.0001 | - | - | - | - |
|
| 550 |
+
| 0.3732 | 64000 | 0.0001 | - | - | - | - |
|
| 551 |
+
| 0.3790 | 65000 | 0.0001 | 0.0001 | -0.0083 | 0.6366 | - |
|
| 552 |
+
| 0.3849 | 66000 | 0.0001 | - | - | - | - |
|
| 553 |
+
| 0.3907 | 67000 | 0.0001 | - | - | - | - |
|
| 554 |
+
| 0.3965 | 68000 | 0.0001 | - | - | - | - |
|
| 555 |
+
| 0.4024 | 69000 | 0.0001 | - | - | - | - |
|
| 556 |
+
| 0.4082 | 70000 | 0.0001 | 0.0001 | -0.0080 | 0.6325 | - |
|
| 557 |
+
| 0.4140 | 71000 | 0.0001 | - | - | - | - |
|
| 558 |
+
| 0.4199 | 72000 | 0.0001 | - | - | - | - |
|
| 559 |
+
| 0.4257 | 73000 | 0.0001 | - | - | - | - |
|
| 560 |
+
| 0.4315 | 74000 | 0.0001 | - | - | - | - |
|
| 561 |
+
| 0.4374 | 75000 | 0.0001 | 0.0001 | -0.0078 | 0.6351 | - |
|
| 562 |
+
| 0.4432 | 76000 | 0.0001 | - | - | - | - |
|
| 563 |
+
| 0.4490 | 77000 | 0.0001 | - | - | - | - |
|
| 564 |
+
| 0.4548 | 78000 | 0.0001 | - | - | - | - |
|
| 565 |
+
| 0.4607 | 79000 | 0.0001 | - | - | - | - |
|
| 566 |
+
| 0.4665 | 80000 | 0.0001 | 0.0001 | -0.0077 | 0.6323 | - |
|
| 567 |
+
| 0.4723 | 81000 | 0.0001 | - | - | - | - |
|
| 568 |
+
| 0.4782 | 82000 | 0.0001 | - | - | - | - |
|
| 569 |
+
| 0.4840 | 83000 | 0.0001 | - | - | - | - |
|
| 570 |
+
| 0.4898 | 84000 | 0.0001 | - | - | - | - |
|
| 571 |
+
| 0.4957 | 85000 | 0.0001 | 0.0001 | -0.0076 | 0.6316 | - |
|
| 572 |
+
| 0.5015 | 86000 | 0.0001 | - | - | - | - |
|
| 573 |
+
| 0.5073 | 87000 | 0.0001 | - | - | - | - |
|
| 574 |
+
| 0.5132 | 88000 | 0.0001 | - | - | - | - |
|
| 575 |
+
| 0.5190 | 89000 | 0.0001 | - | - | - | - |
|
| 576 |
+
| 0.5248 | 90000 | 0.0001 | 0.0001 | -0.0074 | 0.6306 | - |
|
| 577 |
+
| 0.5307 | 91000 | 0.0001 | - | - | - | - |
|
| 578 |
+
| 0.5365 | 92000 | 0.0001 | - | - | - | - |
|
| 579 |
+
| 0.5423 | 93000 | 0.0001 | - | - | - | - |
|
| 580 |
+
| 0.5481 | 94000 | 0.0001 | - | - | - | - |
|
| 581 |
+
| 0.5540 | 95000 | 0.0001 | 0.0001 | -0.0073 | 0.6305 | - |
|
| 582 |
+
| 0.5598 | 96000 | 0.0001 | - | - | - | - |
|
| 583 |
+
| 0.5656 | 97000 | 0.0001 | - | - | - | - |
|
| 584 |
+
| 0.5715 | 98000 | 0.0001 | - | - | - | - |
|
| 585 |
+
| 0.5773 | 99000 | 0.0001 | - | - | - | - |
|
| 586 |
+
| 0.5831 | 100000 | 0.0001 | 0.0001 | -0.0072 | 0.6333 | - |
|
| 587 |
+
| 0.5890 | 101000 | 0.0001 | - | - | - | - |
|
| 588 |
+
| 0.5948 | 102000 | 0.0001 | - | - | - | - |
|
| 589 |
+
| 0.6006 | 103000 | 0.0001 | - | - | - | - |
|
| 590 |
+
| 0.6065 | 104000 | 0.0001 | - | - | - | - |
|
| 591 |
+
| 0.6123 | 105000 | 0.0001 | 0.0001 | -0.0071 | 0.6351 | - |
|
| 592 |
+
| 0.6181 | 106000 | 0.0001 | - | - | - | - |
|
| 593 |
+
| 0.6240 | 107000 | 0.0001 | - | - | - | - |
|
| 594 |
+
| 0.6298 | 108000 | 0.0001 | - | - | - | - |
|
| 595 |
+
| 0.6356 | 109000 | 0.0001 | - | - | - | - |
|
| 596 |
+
| 0.6415 | 110000 | 0.0001 | 0.0001 | -0.0070 | 0.6330 | - |
|
| 597 |
+
| 0.6473 | 111000 | 0.0001 | - | - | - | - |
|
| 598 |
+
| 0.6531 | 112000 | 0.0001 | - | - | - | - |
|
| 599 |
+
| 0.6589 | 113000 | 0.0001 | - | - | - | - |
|
| 600 |
+
| 0.6648 | 114000 | 0.0001 | - | - | - | - |
|
| 601 |
+
| 0.6706 | 115000 | 0.0001 | 0.0001 | -0.0070 | 0.6336 | - |
|
| 602 |
+
| 0.6764 | 116000 | 0.0001 | - | - | - | - |
|
| 603 |
+
| 0.6823 | 117000 | 0.0001 | - | - | - | - |
|
| 604 |
+
| 0.6881 | 118000 | 0.0001 | - | - | - | - |
|
| 605 |
+
| 0.6939 | 119000 | 0.0001 | - | - | - | - |
|
| 606 |
+
| 0.6998 | 120000 | 0.0001 | 0.0001 | -0.0069 | 0.6305 | - |
|
| 607 |
+
| 0.7056 | 121000 | 0.0001 | - | - | - | - |
|
| 608 |
+
| 0.7114 | 122000 | 0.0001 | - | - | - | - |
|
| 609 |
+
| 0.7173 | 123000 | 0.0001 | - | - | - | - |
|
| 610 |
+
| 0.7231 | 124000 | 0.0001 | - | - | - | - |
|
| 611 |
+
| 0.7289 | 125000 | 0.0001 | 0.0001 | -0.0068 | 0.6362 | - |
|
| 612 |
+
| 0.7348 | 126000 | 0.0001 | - | - | - | - |
|
| 613 |
+
| 0.7406 | 127000 | 0.0001 | - | - | - | - |
|
| 614 |
+
| 0.7464 | 128000 | 0.0001 | - | - | - | - |
|
| 615 |
+
| 0.7522 | 129000 | 0.0001 | - | - | - | - |
|
| 616 |
+
| 0.7581 | 130000 | 0.0001 | 0.0001 | -0.0067 | 0.6340 | - |
|
| 617 |
+
| 0.7639 | 131000 | 0.0001 | - | - | - | - |
|
| 618 |
+
| 0.7697 | 132000 | 0.0001 | - | - | - | - |
|
| 619 |
+
| 0.7756 | 133000 | 0.0001 | - | - | - | - |
|
| 620 |
+
| 0.7814 | 134000 | 0.0001 | - | - | - | - |
|
| 621 |
+
| 0.7872 | 135000 | 0.0001 | 0.0001 | -0.0067 | 0.6365 | - |
|
| 622 |
+
| 0.7931 | 136000 | 0.0001 | - | - | - | - |
|
| 623 |
+
| 0.7989 | 137000 | 0.0001 | - | - | - | - |
|
| 624 |
+
| 0.8047 | 138000 | 0.0001 | - | - | - | - |
|
| 625 |
+
| 0.8106 | 139000 | 0.0001 | - | - | - | - |
|
| 626 |
+
| 0.8164 | 140000 | 0.0001 | 0.0001 | -0.0066 | 0.6339 | - |
|
| 627 |
+
| 0.8222 | 141000 | 0.0001 | - | - | - | - |
|
| 628 |
+
| 0.8281 | 142000 | 0.0001 | - | - | - | - |
|
| 629 |
+
| 0.8339 | 143000 | 0.0001 | - | - | - | - |
|
| 630 |
+
| 0.8397 | 144000 | 0.0001 | - | - | - | - |
|
| 631 |
+
| 0.8456 | 145000 | 0.0001 | 0.0001 | -0.0066 | 0.6352 | - |
|
| 632 |
+
| 0.8514 | 146000 | 0.0001 | - | - | - | - |
|
| 633 |
+
| 0.8572 | 147000 | 0.0001 | - | - | - | - |
|
| 634 |
+
| 0.8630 | 148000 | 0.0001 | - | - | - | - |
|
| 635 |
+
| 0.8689 | 149000 | 0.0001 | - | - | - | - |
|
| 636 |
+
| 0.8747 | 150000 | 0.0001 | 0.0001 | -0.0065 | 0.6357 | - |
|
| 637 |
+
| 0.8805 | 151000 | 0.0001 | - | - | - | - |
|
| 638 |
+
| 0.8864 | 152000 | 0.0001 | - | - | - | - |
|
| 639 |
+
| 0.8922 | 153000 | 0.0001 | - | - | - | - |
|
| 640 |
+
| 0.8980 | 154000 | 0.0001 | - | - | - | - |
|
| 641 |
+
| 0.9039 | 155000 | 0.0001 | 0.0001 | -0.0065 | 0.6336 | - |
|
| 642 |
+
| 0.9097 | 156000 | 0.0001 | - | - | - | - |
|
| 643 |
+
| 0.9155 | 157000 | 0.0001 | - | - | - | - |
|
| 644 |
+
| 0.9214 | 158000 | 0.0001 | - | - | - | - |
|
| 645 |
+
| 0.9272 | 159000 | 0.0001 | - | - | - | - |
|
| 646 |
+
| 0.9330 | 160000 | 0.0001 | 0.0001 | -0.0064 | 0.6334 | - |
|
| 647 |
+
| 0.9389 | 161000 | 0.0001 | - | - | - | - |
|
| 648 |
+
| 0.9447 | 162000 | 0.0001 | - | - | - | - |
|
| 649 |
+
| 0.9505 | 163000 | 0.0001 | - | - | - | - |
|
| 650 |
+
| 0.9563 | 164000 | 0.0001 | - | - | - | - |
|
| 651 |
+
| 0.9622 | 165000 | 0.0001 | 0.0001 | -0.0064 | 0.6337 | - |
|
| 652 |
+
| 0.9680 | 166000 | 0.0001 | - | - | - | - |
|
| 653 |
+
| 0.9738 | 167000 | 0.0001 | - | - | - | - |
|
| 654 |
+
| 0.9797 | 168000 | 0.0001 | - | - | - | - |
|
| 655 |
+
| 0.9855 | 169000 | 0.0001 | - | - | - | - |
|
| 656 |
+
| 0.9913 | 170000 | 0.0001 | 0.0001 | -0.0063 | 0.6347 | - |
|
| 657 |
+
| 0.9972 | 171000 | 0.0001 | - | - | - | - |
|
| 658 |
+
| 1.0 | 171486 | - | - | - | - | 0.5986 |
|
| 659 |
+
|
| 660 |
+
* The bold row denotes the saved checkpoint.
|
| 661 |
+
</details>
|
| 662 |
+
|
| 663 |
+
### Framework Versions
|
| 664 |
+
- Python: 3.10.14
|
| 665 |
+
- Sentence Transformers: 3.0.1
|
| 666 |
+
- Transformers: 4.44.0
|
| 667 |
+
- PyTorch: 2.4.0
|
| 668 |
+
- Accelerate: 0.33.0
|
| 669 |
+
- Datasets: 2.20.0
|
| 670 |
+
- Tokenizers: 0.19.1
|
| 671 |
+
|
| 672 |
+
## Citation
|
| 673 |
+
|
| 674 |
+
### BibTeX
|
| 675 |
+
|
| 676 |
+
#### Sentence Transformers
|
| 677 |
+
```bibtex
|
| 678 |
+
@inproceedings{reimers-2019-sentence-bert,
|
| 679 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
| 680 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 681 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
| 682 |
+
month = "11",
|
| 683 |
+
year = "2019",
|
| 684 |
+
publisher = "Association for Computational Linguistics",
|
| 685 |
+
url = "https://arxiv.org/abs/1908.10084",
|
| 686 |
+
}
|
| 687 |
+
```
|
| 688 |
+
|
| 689 |
+
#### MSELoss
|
| 690 |
+
```bibtex
|
| 691 |
+
@inproceedings{reimers-2020-multilingual-sentence-bert,
|
| 692 |
+
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
|
| 693 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
| 694 |
+
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
|
| 695 |
+
month = "11",
|
| 696 |
+
year = "2020",
|
| 697 |
+
publisher = "Association for Computational Linguistics",
|
| 698 |
+
url = "https://arxiv.org/abs/2004.09813",
|
| 699 |
+
}
|
| 700 |
+
```
|
| 701 |
+
|
| 702 |
+
<!--
|
| 703 |
+
## Glossary
|
| 704 |
+
|
| 705 |
+
*Clearly define terms in order to be accessible across audiences.*
|
| 706 |
+
-->
|
| 707 |
+
|
| 708 |
+
<!--
|
| 709 |
+
## Model Card Authors
|
| 710 |
+
|
| 711 |
+
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| 712 |
+
-->
|
| 713 |
+
|
| 714 |
+
<!--
|
| 715 |
+
## Model Card Contact
|
| 716 |
+
|
| 717 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| 718 |
-->
|