File size: 35,833 Bytes
bb9e7a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11808
- loss:Infonce
base_model: BAAI/bge-m3
widget:
- source_sentence: Who are some notable individuals named Roger Mason
sentences:
- "Rav Kook's writings are extensive, and he is considered one of the most celebrated\
\ and influential rabbis of the 20th century. Some rabbis recommend that students\
\ of his begin studying his writings with Ein Ayah. References\n\nExternal links\n\
\ Ayin Ayah (full text), Hebrew Wikisource\n * Ayn Aya Classes in English\n\n\
Talmud\nAggadic Midrashim\nAbraham Isaac Kook\nHebrew-language religious books"
- 'Roger Mason may refer to:
Roger Mason (baseball) (born 1958), American baseball player
Roger Mason (geologist) (born 1941), discoverer of Ediacaran fossils
Roger Mason Jr. (born 1980), American basketball player
Roger Mason (musician), Australian keyboardist
L. Roger Mason, Jr., former assistant director of National Intelligence for Systems
and Resource Analyses'
- 'Timetabled passenger services on both lines had ceased by the end of February
1959. Shipping
The Bourne-Morton Canal or Bourne Old Eau connected the town to the sea in Roman
times. Until the mid-19th century, the present Bourne Eau was capable of carrying
commercial boat traffic from the Wash coast and Spalding. This resulted from the
investment following the Bourne Navigation Act of 1780. Passage became impossible
once the junction of the Eau and the River Glen was converted from gates to a
sluice in 1860. Media
Local news and television programmes are provided by BBC Yorkshire and Lincolnshire
and ITV Yorkshire. Television signals are received from the Belmont TV transmitter,
the Waltham TV transmitter can also be received which broadcast BBC East Midlands
and ITV Central programmes. Local radio stations are BBC Radio Lincolnshire, Greatest
Hits Radio Lincolnshire and Lincs FM. The town''s local newspapers are Bourne
Local and Stamford Mercury. Sport
Bourne Town Football Club plays football in the United Counties Football League,
whilst Bourne Cricket Club plays in the Lincolnshire ECB Premier League. These
teams play their home games at the Abbey Lawn, a recreation ground privately owned
by the Bourne United Charities. Motor sports
The racing-car marques English Racing Automobiles (ERA) and British Racing Motors
(BRM) were both founded in Bourne by Raymond Mays, an international racing driver
and designer who lived in Bourne. The former ERA and BRM workshops in Spalding
Road are adjacent to Eastgate House, the Mays'' family home in the town''s Eastgate.
Landmarks
There are currently 71 listed buildings in the parish of Bourne, the most important
being Bourne Abbey and the Parish Church of St Peter and St Paul (1138), which
is the only one scheduled Grade I. Notable people
Bourne is reputedly the birthplace of Hereward the Wake (in about 1035), although
the 12th-century source of this information, De Gestis Herwardi Saxonis, refers
only to his father as being "of Bourne" and to the father''s house and retainers
there. Robert Mannyng (1264–1340) is credited with putting the speech of the ordinary
people of his time into recognisable form. He is better known as Robert de Brunne
because of his long period of residence as a canon at Bourne Abbey. There he completed
his life''s work of popularising religious and historical material in a Middle
English dialect that was easily understood at that time. William Cecil (1520–1598)
became the first Lord Burghley after serving Queen Elizabeth I. He was born at
a house in the centre of Bourne that is now the Burghley Arms. Dr William Dodd
(1729–1777), was an Anglican clergyman, man of letters and forger. He was prosecuted,
sentenced to death and publicly hanged at Tyburn in 1777. Charles Frederick Worth
(1825–1895), son of a solicitor, lived at Wake House in North Street. He moved
to Paris and became a renowned designer of women''s fashion and the founder of
haute couture. The French government awarded him the Légion d''honneur. Sir George
White (1840-1912), MP for North West Norfolk, a seat he held for twelve years
until he died in 1912. He was knighted for public service in 1907.'
- source_sentence: What football team does the Japanese player play for
sentences:
- After the meeting, Box summons up the courage to ask Lorraine (Sue Holderness)
on the date. The act ends with Robert's coat getting on fire because of the cigarette,
with "Smoke Gets in Your Eyes" on the background.
- is a Japanese football player. He plays for Honda Lock.
- As followers on Twitter and FB probably well know I’ve been up to more than a
spot of preserving of late. It’s my latest addiction, as if I need any more of
those. My Dad’s the King of Jams, Chutneys and Pickles and I have a feeling he’s
passed his enthusiastic genes for it on to me!. Which is great, but time consuming.
Many an evening has been spent peeling, dicing, de-stoning, chopping, stirring,
testing, sterilising and jarring. And then obviously the tasting. And all the
crackers, bread and cheese to go with it!. I rarely get to bed much before midnight
on my chutneying nights. And to be honest my cupboards are now fit to bursting
with so many goodies, but at least I have christmas presents totally nailed this
year. My Dad’s been making Hedgerow Chutney for years, and it happens to be everyone’s
favourite of all his chutney recipes (and he makes quite a number!). Each autumn
he takes a long walk around the field at the back of his house in Herefordshire
picking all the freebie hedgerow goodies he can find and transforms them into
this marvellously fruitful chutney. There’s always plenty of damsons, bullaces,
sloes, blackberries and a few elderberries. Plus pears or apples for smoothing
and bulking out. We don’t have quite the same fruit in our hedgerows in France
but I thought I’d make my own French version picking the fruit from our garden
and nearby tracks and lanes, managing to find plenty of figs, greengages, plums,
pears, blackberries and sloes just before the season finished a couple of weeks
ago. We’ve elderberries here too but they were way past their best by the time
I got into full chutney mode. There’s no escaping how time consuming and labourious
chutney making can be, especially when using so much fruit that needs hefty preparatory
work. I realise now why it’s a hobby generally taken up by retired folk. But the
results are so worth it, if you can spare it set aside a whole evening in the
kitchen and wile away the hours getting lost in music or the radio or even catching
up on a few programmes on You Tube.
- source_sentence: What is the purpose of Business Intelligence
sentences:
- 'College career
Proctor played as a defensive lineman for the North Carolina Central Eagles from
2008 to 2012. He was redshirted in 2008.'
- The purpose of Business Intelligence is the transformation of raw data into meaningful
information which can be used to make better business decisions. Business Intelligence
grew out of Decision Support systems and is all about collecting data from disparate
sources, conforming and integrating that data into central repositories which
support reporting and analysis activities.
- You have to show the police courtesy, they are only human. No one even WANTS for
the judicial system to work. They are too lazy.
- source_sentence: How does the speaker feel about Battle Symphony
sentences:
- It's a symptomless prearranged fact that when you afford your babe a infant work
you motivate the status system, bolster the infant's stressed system, eat up colic,
and harden your in bondage next to your kid. Now, how satisfying is that
- Piquet passed Laffite to become the race's fifth different leader. Senna reached
second just 1.7 seconds behind Piquet by passing Laffite, who then pitted for
tires. With the two of them in front on their own, and Piquet leading by up to
3.5 seconds, Senna was content for the time being to follow his countryman. After
eight laps in the lead, Piquet pitted for tires. Senna regained first place and
then also pitted. Piquet's 18.4 second stop was even slower than teammate Mansell's
had been, but when he returned to the track, the two-time champion got the bit
between his teeth. Running second behind Senna, Piquet set the fastest lap of
the race on lap 41, but with a pit stop ten seconds quicker than Piquet's, Senna
was able to retain the lead. On the very next lap, the 42nd, Piquet pushed a bit
too much, and crashed hard at the left-hand corner before the last chicane. He
ended up in the tire barrier, unhurt, but with his car in a very precarious position.
The crane, present for just that reason, was unable to move the car. Arnoux, now
16.6 seconds behind in second, took a second a lap off Senna's lead for five laps
while a yellow was displayed in the corner where Piquet had crashed. As soon as
the yellow flag was gone, Arnoux went wide and hit Piquet's abandoned Williams!
The Frenchman decided that his car was not damaged, and attempted to rejoin the
field, but did so right in front of Thierry Boutsen's Arrows-BMW, sidelining both
cars. Very uncharacteristic of a street race, these three – Piquet, Arnoux and
Boutsen – were the only drivers all afternoon to retire due to accidents.
- Like Battle Symphony, it's not bad. It's just extremely boring.
- source_sentence: When did he migrate to New South Wales
sentences:
- 'predict ministry in a sales and special floor being Job to the vulnerability
diver. team: This research will work last for either, often, and also obtaining
spreadsheets in the funny wedding power of the usability time. Physical Demands:
The exclusive transitions was temporarily need perfect of those that must share
developed by an position to badly do the animal objectives of this source. necessary
terabytes may pay acted to increase streets with hearts to address the professional
items. solely, the job will distract, Coordinate and be inbox security fun interdisciplinary
operations that might read in back of 20 updates The service will properly be
to like the detection throughout the use: logging, including, killing, teaching,
leading, preparing, operating, and using.'
- "Shizuka Shirakawa, Scholar of Chinese-language literature. Horin Fukuoji, Nihonga\
\ painter. 2005\n Mitsuko Mori. Actress. Makoto Saitō (1921–2008). Political scientist,\
\ specializing in American diplomatic and political history. Ryuzan Aoki, Ceramic\
\ artist. Toshio Sawada, Civil engineer. Shigeaki Hinohara, Doctor. 2006\n Yoshiaki\
\ Arata. A pioneer of nuclear fusion research. Jakuchō Setouchi. Writer/Buddhist\
\ nun. Hidekazu Yoshida. Music critic. Chusaku Oyama, Nihonga painter. Miyohei\
\ Shinohara, Economist. 2007\n Akira Mikazuki. Former justice minister and professor\
\ emeritus. Shinya Nakamura. Sculptor. Kōji Nakanishi. Organic chemist. Tokindo\
\ Okada, Developmental biologist. Shigeyama Sensaku, Kyogen performer. 2008\n\
\ Hironoshin Furuhashi (1928–2009). Sportsman and sports bureaucrat. Kiyoshi Itō.\
\ A mathematician whose work is now called Itō calculus. Donald Keene."
- He attended Derby Grammar School and Beaufort House in London, and migrated to
New South Wales in 1883. He settled in Newcastle, where he worked as a shipping
agent, eventually partnering with his brothers in a firm. On 6 May 1893 he married
Gertrude Mary Saddington, with whom he had five children.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on BAAI/bge-m3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Jrinky/model3")
# Run inference
sentences = [
'When did he migrate to New South Wales',
'He attended Derby Grammar School and Beaufort House in London, and migrated to New South Wales in 1883. He settled in Newcastle, where he worked as a shipping agent, eventually partnering with his brothers in a firm. On 6 May 1893 he married Gertrude Mary Saddington, with whom he had five children.',
'Shizuka Shirakawa, Scholar of Chinese-language literature. Horin Fukuoji, Nihonga painter. 2005\n Mitsuko Mori. Actress. Makoto Saitō (1921–2008). Political scientist, specializing in American diplomatic and political history. Ryuzan Aoki, Ceramic artist. Toshio Sawada, Civil engineer. Shigeaki Hinohara, Doctor. 2006\n Yoshiaki Arata. A pioneer of nuclear fusion research. Jakuchō Setouchi. Writer/Buddhist nun. Hidekazu Yoshida. Music critic. Chusaku Oyama, Nihonga painter. Miyohei Shinohara, Economist. 2007\n Akira Mikazuki. Former justice minister and professor emeritus. Shinya Nakamura. Sculptor. Kōji Nakanishi. Organic chemist. Tokindo Okada, Developmental biologist. Shigeyama Sensaku, Kyogen performer. 2008\n Hironoshin Furuhashi (1928–2009). Sportsman and sports bureaucrat. Kiyoshi Itō. A mathematician whose work is now called Itō calculus. Donald Keene.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 11,808 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.85 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 186.46 tokens</li><li>max: 1024 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What type of tournament structure was used in this freestyle wrestling competition</code> | <code>This freestyle wrestling competition consisted of a single-elimination tournament, with a repechage used to determine the winners of two bronze medals. Results<br>Legend<br>F — Won by fall<br><br>Final<br><br>Top half<br><br>Bottom half<br><br>Repechage<br><br>References<br>Official website<br><br>Women's freestyle 58 kg<br>World</code> |
| <code>What was the status of Josip Broz Tito under the 1974 Constitution of Yugoslavia regarding his presidency</code> | <code>1 Wednesday, 22 April 1998. 2 (8.30 a.m.). 3 JUDGE CASSESE: Good morning. May I ask the<br>4 Registrar to call out the case number, please. 5 THE REGISTRAR: Case number IT-95-13a-T,<br>6 Prosecutor versus Slavko Dokmanovic. 7 MR. NIEMANN: My name is Niemann. I appear<br>8 with my colleagues, Mr. Williamson, Mr. Waespi and<br>9 Mr. Vos. 10 MR. FILA: My name is Mr. Toma Fila and<br>11 I appear with Ms. Lopicic and Mr. Petrovic in Defence of<br>12 my client, Mr. Slavko Dokmanovic. 13 JUDGE CASSESE: Mr. Dokmanovic, can you<br>14 follow me? Before we call the witness, may I ask you<br>15 whether you agree to this note from the Registrar about<br>16 the two documents which we discussed yesterday -- you<br>17 have probably received the English translation of the<br>18 bibliography of our witness, plus the missing pages of<br>19 the other document, so I think it is agreed that they<br>20 can be admitted into evidence. 21 MR. NIEMANN: Yes. 22 JUDGE CASSESE: Shall we proceed with the<br>24 MR. FILA: Your Honour, before we continue<br>25 wi...</code> |
| <code>How quickly can you get loan approval and funds transferred with Crawfort</code> | <code>Then click on the submit button, and it’s done. Make your dream come true with Crawfort<br>When you all submit the loan form, then the agency takes a few hours to process and for approval of the loan. Not only that, you can get your loan amount in your account within a day after getting approval. Many money lenders all take more time in processing things and to credit the amount as well. So, for all that, a customer suffers more as they can’t get the money immediately. But here all these things are not done, and the staff here always make sure to provide you best and fast services. For all these things, you can get the best loan services from here without any doubt.</code> |
* Loss: <code>selfloss.Infonce</code> with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 1,476 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.61 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 171.81 tokens</li><li>max: 1024 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is Hector Guimard best known for</code> | <code>Hector Guimard (, 10 March 1867 – 20 May 1942) was a French architect and designer, and a prominent figure of the Art Nouveau style. He achieved early fame with his design for the Castel Beranger, the first Art Nouveau apartment building in Paris, which was selected in an 1899 competition as one of the best new building facades in the city. He is best known for the glass and iron edicules or canopies, with ornamental Art Nouveau curves, which he designed to cover the entrances of the first stations of the Paris Metro. Between 1890 and 1930, Guimard designed and built some fifty buildings, in addition to one hundred and forty-one subway entrances for Paris Metro, as well as numerous pieces of furniture and other decorative works. However, in the 1910s Art Nouveau went out of fashion and by the 1960s most of his works had been demolished, and only two of his original Metro edicules were still in place. Guimard's critical reputation revived in the 1960s, in part due to subsequent acquisit...</code> |
| <code>What does Mark Kantrowitz say about the inclusion of loans in financial aid packages</code> | <code>"They don't always understand that part of the financial aid package includes loans," he says. But loans "don't really reduce your costs," explains Mark Kantrowitz, founder of the financial aid website FinAid.org and publisher of Edvisors Network. "They simply spread them out over time. ... A loan is a loan.</code> |
| <code>How can Ayurveda support women's health during menopause</code> | <code>Especially as we journey towards menopause, Ayurveda is there to support us with its millenary wisdom. These are some easy routines to incorporate for the daily care of the vulva and vagina, our most delicate flower. Sesame oil: our best allied against dryness, it cannot be missing in our diet.</code> |
* Loss: <code>selfloss.Infonce</code> with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2
- `per_device_eval_batch_size`: 2
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.2033 | 100 | 0.2694 | 0.0690 |
| 0.4065 | 200 | 0.0822 | 0.0528 |
| 0.6098 | 300 | 0.0689 | 0.0497 |
| 0.8130 | 400 | 0.0644 | 0.0469 |
| 1.0163 | 500 | 0.0643 | 0.0443 |
| 1.2195 | 600 | 0.0378 | 0.0473 |
| 1.4228 | 700 | 0.04 | 0.0479 |
| 1.6260 | 800 | 0.0358 | 0.0461 |
| 1.8293 | 900 | 0.0332 | 0.0507 |
| 2.0325 | 1000 | 0.0283 | 0.0538 |
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.4.0
- Transformers: 4.42.4
- PyTorch: 2.2.0+cu121
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### Infonce
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |