File size: 30,873 Bytes
2f67c47 |
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 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:99840
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: na. es. sui .s. siqs aut̃. It nrͣ hodie ꝙ ea demũ sit ma. ⁊ a.
g̃iadħ de ni ma. ⁊ a. siadħ de nolib ubis ⁊ młrib e Uia ex ꝯcubinis filu nascũt᷑
uales ⁊ te nalib faluꝰ usdeimꝰ ⁊ de mr̃ib eoꝵ .s. qui dicãt᷑ nales. ⁊ ꝙͣtũ
pocut eiꝰ relinqͥ. ł int̾ iuiuoꝵ. ⁊ ĩ ucima nol̃tate quoqͣ.tdari ⁊ postea ꝓseqũ
teꝰ denorẽ ice. dicemꝰ quib ex cai lb̾i uales fi ant sus .i. redigãt᷑ in potatẽ
ꝑentũ. ⁊ de h tͣctatu
sentences:
- na. es. sui .s. siqs aut̃. It nrͣ hodie ꝙ ea demũ sit ma. ⁊ a. g̃iadħ de ni ma.
⁊ a. siadħ de nolib ubis ⁊ młrib e Uia ex ꝯcubinis filu nascũt᷑ uales ⁊ te
nalib faluꝰ usdeimꝰ ⁊ de mr̃ib eoꝵ .s. qui dicãt᷑ nales. ⁊ ꝙͣtũ pocut eiꝰ
relinqͥ. ł int̾ iuiuoꝵ. ⁊ ĩ ucima nol̃tate quoqͣ.tdari ⁊ postea ꝓseqũ teꝰ denorẽ
ice. dicemꝰ quib ex cai lb̾i uales fi ant sus .i. redigãt᷑ in potatẽ ꝑentũ.
⁊ de h tͣctatu
- 'illius excubaret: ibidem ꝓ fide xp̃i aꝑsecutorib tradita est qi cum digna &
eumenia: & eupe Ciuitate falare: passio scõtu graciliani. & felicissime iurg
nis. Quoꝵ ora ꝓxp̃o contusi lapidib. dehinc gladio ꝑcusi optatam martytii suscepert̃
palmam. idus augusti.'
- 'Et nos Poncius Ugo, Dei gracia Impuriarum comes predictus, promitimus vobis Raimundo
Xetmario, nomine dicte domne Marchisie, predictam forciam deffendere ab omni homine
qui a te directum accipere noluerit vel facere. Assigno etiam vobis et dono in
feudum, in esmendam dicti careu, dictos V squillatas milii, annuatim accipiendas
in festo Omnium Sanctorum, in omnibus nostris directis et taschis quas accipimus
in stagno de Cils. Actum est hoc VII kalendas novembris anno Domini MºCCºLXXº
octavo. Sig(+)num Raimundi Xetmarii predicti, qui hoc firmo et laudo. Sig(+)num
Ponci Ugonis, Dei gracia comis Impuriarum predicti, qui hoc firmamus et laudamus.
Testes huius rei sunt: Bernardus de Palaciolo de Villanova, et Berengarius de
Lanciano, et Guilelmus Alferici et Simon de Trilia, milites.'
- source_sentence: co segnito se dicitcis. Quid qͣntis. Qiditeit ti. nabiꝙ s int̾prctatum
magr̃ubhtͥtał. di ca cis docnite r itdete uencrunt ⁊ intertubi ibi manceto ⁊ mancrunt
sbidit illo. Hora aut̃ trar q̃i deciina. Eint auł anoicas tẽ srmo ni petͥ unis
ce ouioui qui aud itrant a ioilt ⁊ situti fucrant guns. Inicrut hu trem suilss
mont̃ ⁊ dicit ci. quutumui inesilq intͥpꝰ fͤ Et arduxi cum ad ihͥm. Intuit uit᷑issctil
ois.
sentences:
- 'Si qua Deos tangit pietas, Astraeaque vivit, Castigatque Reum torto Rhamnusia
plumbo: Et te poena manet Physignathe, nec Rhadamanthi Effugies uncum.'
- Sic absentibus ducibus praedictis et caeteris regni primatibus, reconciliatus
est rex Saxonibus simulatorie, et cum eis ad usque Goslariam pervenit, non multum
tamen confidens in illis. Roudolfus dux et caeteri rebelles reconciliantur regi.
- co segnito se dicitcis. Quid qͣntis. Qiditeit ti. nabiꝙ s int̾prctatum magr̃ubhtͥtał.
di ca cis docnite r itdete uencrunt ⁊ intertubi ibi manceto ⁊ mancrunt sbidit
illo. Hora aut̃ trar q̃i deciina. Eint auł anoicas tẽ srmo ni petͥ unis ce ouioui
qui aud itrant a ioilt ⁊ situti fucrant guns. Inicrut hu trem suilss mont̃ ⁊ dicit
ci. quutumui inesilq intͥpꝰ fͤ Et arduxi cum ad ihͥm. Intuit uit᷑issctil ois.
- source_sentence: 'israhelitieo popło inͦ deserto p̃cessit: ipse in euan geliis deserta
gentiũ uisitauit: Et ꝑ quẽ tunc in srna huic generationi manna defluxit: ipse
xp̃ianȩ genti. corporis sanguinis sui manna in xecclesia subministrat.'
sentences:
- Sed ne quid videretur omissum, aut nostro potuisset dubium cordi remanere, ad
beati Petri sacratissimum corpus districta eum ex abundanti fecimus sacramenta
praebere. Quibus praestitis, magna sumus exsultatione gavisi, quod hujuscemodi
experimento innocentia ejus evidenter enituit. Pro qua re gloria vestra praedictum
virum cum omni charitate suscipiat, et reverentiam ei, qualem sacerdoti decet,
exhibeat, nec quaedam cordibus remaneat de iis quae sunt jam purgata dubietas.
Sed ita suprascripto vos episcopo devotissime oportet in omnibus adhaerere, ut
congrue decenterque Deum in ejus persona cujus minister est videamini honorare.
EPISTOLA XXXIV.
- La char d'une joe de beuf tranchee par lesches et mise en pasté, et puis, quant
elle est cuicte, gecter la saulse d'un halebran dedens. En la haste menue d'un
pourcel n'a aucun appareil a faire fors le laver et embrocher et enveloper de
sa taye, et cuire longuement. Poules farcies coulourees ou dorees.
- 'israhelitieo popło inͦ deserto p̃cessit: ipse in euan geliis deserta gentiũ
uisitauit: Et ꝑ quẽ tunc in srna huic generationi manna defluxit: ipse xp̃ianȩ
genti. corporis sanguinis sui manna in xecclesia subministrat.'
- source_sentence: 'ad renonationem ietłm ꝑueniret. Ee xagesim oita scdo anno regno
rogis chasdeoꝵ subũso. cui ad huc octo anti: ad regnandͥ: restabant. oꝵ etiam
sexta insio damelis ostendit pp̃ cãm : ettinctim. datiꝰ qui medis imꝑabat u septima
uisio danieł ostendit memoratori tegno fucces sit. Cmꝰ pͥmo anno regtui: ut decimauisio
ba ncł ostendit. supputatis unis uidens aꝓpĩ. qͣre tempꝰ reũsionis. que ꝑ leremiã
fũat ꝓtais.'
sentences:
- 'Nos igitur attendentes, quòd ad Religionem conversi, si fuerint in suis locis
laudabiliter conversati, illegitimitatis macula non obstante, juris permissione
licenter possunt ad Ordines promoveri b , discretioni tuæ præsentium auctoritate
committimus, quatenus cum eodem Presbytero, ejus ad hoc suffragantibus meritis,
super quibus tuam intendimus conscientiam onerare, quòd hujusmodi non obstante
defectu in susceptis Ordinibus ministrare, & ad regulares administrationes dicti
Ordinis dumtaxat assumi valeat, auctoritate nostra dispenses, prout secundùm Deum
animæ suæ saluti videris expedire: Ita tamen quòd dictus Conradus nullatenus præficiatur
in eodem Ordine in Ministrum . Datum Romæ apud Sanctam Mariam Majorem sexto Nonas
Maii, Pontificatus nostri Anno Secundo.'
- Nam intellectum proferentis in eo significare uox dicitur, quod ipsum auditori
manifestat, dum consimilem in auditore generat. Unde Priscianus articulatam, id
est significatiuam, uocem esse dicit, quae coartatur cum sensu proferentis, id
est quam ipse proferens intendit proferre ad manifestandum intellectum suum. In
quo quidem uocem articulatam, id est significatiuam, eum accipere dicunt quantum
ad intellectum proferentis quem manifestat, non ad intellectum auditoris, quem
generat.
- 'ad renonationem ietłm ꝑueniret. Ee xagesim oita scdo anno regno rogis chasdeoꝵ
subũso. cui ad huc octo anti: ad regnandͥ: restabant. oꝵ etiam sexta insio damelis
ostendit pp̃ cãm : ettinctim. datiꝰ qui medis imꝑabat u septima uisio danieł
ostendit memoratori tegno fucces sit. Cmꝰ pͥmo anno regtui: ut decimauisio ba
ncł ostendit. supputatis unis uidens aꝓpĩ. qͣre tempꝰ reũsionis. que ꝑ leremiã
fũat ꝓtais.'
- source_sentence: 'cta test̾i sur: u̾bi qd̾ madauit in mil egones. Q uod disposunt
ad abrahã. mũti sui ad p̃saaci Et statuit il acob ĩ p̾ceptũ: ⁊ isrł mn testiñ
etꝰ Dices tibi dabo t̾ram chanaan: fu ncdũ heditatis ur̃e. Dũ e̾e̾nt nũo ocui.
paucissimi ⁊ ĩcole ouis. Et ꝑtͣni eẽt de gnͣte ĩ gentẽ: ⁊ de regno ad ulũ
alterũ. Non reliquit hoĩem'
sentences:
- p̾mioꝵ. p̃s. b̾ildixit finis tuus inte. Et ĩminitas apee cato. Qua xp̃c donatus
e̾ ps. p̾ucinsti eũ i bñ. dicidis ¶Infernans quo dupiex .s. adinacio. ps. laudat᷑ͣ
ptc̃ce indesidus aĩe sue ⁊ ñquis bñdi. et cũ quis sibi tribuit bona que ht̃
atco. Iob. timebat enĩ ne forte peccau̾int fuii eius. ⁊ bñdix̾int deo incordib
suis. Corꝑans ẽ ad carnis delecta tr̃em us. or̃s caro feñ. ⁊ oĩs gła euis qiͣ
d̾r ꝑ ysaiam. ue qͥ niungitis domũ addom̃. ⁊ agr̃ ago copłatis us ad t̾minũ
ioci. Nñquid ħ̾itabitis uos so
- 'cta test̾i sur: u̾bi qd̾ madauit in mil egones. Q uod disposunt ad abrahã. mũti
sui ad p̃saaci Et statuit il acob ĩ p̾ceptũ: ⁊ isrł mn testiñ etꝰ Dices tibi
dabo t̾ram chanaan: fu ncdũ heditatis ur̃e. Dũ e̾e̾nt nũo ocui. paucissimi
⁊ ĩcole ouis. Et ꝑtͣni eẽt de gnͣte ĩ gentẽ: ⁊ de regno ad ulũ alterũ. Non
reliquit hoĩem'
- 'uintia orientalium anglorũque fuerint gesta eccdesi astica pastim & strap̃tis
ul tra diqone pranũ partim re uerentis simi abbatis ̃ relatione conperimus: Atiuero
in prouintia lindissi que sint gesta eo fidemxp̃i queue suc cessia sacerdotalis
eꝓterit uollitteris reueren tissii antisteris ciniberti uł aliorũ fidebũ uirorum
anagubee alidigimus: D auterinnoa danhrsibrorum prouintia eo quo rem pu fueiu
api ꝑceperunt us quea sp̃ sens ꝑduersas regiones: ineccle siitio aecta non uno
quolibet auqtere sed fidelimnumerta umtestiũ nulie csar uel neminis sepoterant
adsertione cagnoui: eceptis his'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 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': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'cta test̾i sur: u̾bi qd̾ madauit in mil egones. Q uod disposunt ad abrahã. mũti sui ad p̃saaci Et statuit il acob ĩ p̾ceptũ: ⁊ isrł mn testiñ etꝰ Dices tibi dabo t̾ram chanaan: fu ncdũ heditatis ur̃e. Dũ e̾e̾nt nũo ocui. paucissimi ⁊ ĩcole ouis. Et ꝑtͣni eẽt de gnͣte ĩ gentẽ: ⁊ de regno ad ulũ alterũ. Non reliquit hoĩem',
'cta test̾i sur: u̾bi qd̾ madauit in mil egones. Q uod disposunt ad abrahã. mũti sui ad p̃saaci Et statuit il acob ĩ p̾ceptũ: ⁊ isrł mn testiñ etꝰ Dices tibi dabo t̾ram chanaan: fu ncdũ heditatis ur̃e. Dũ e̾e̾nt nũo ocui. paucissimi ⁊ ĩcole ouis. Et ꝑtͣni eẽt de gnͣte ĩ gentẽ: ⁊ de regno ad ulũ alterũ. Non reliquit hoĩem',
'p̾mioꝵ. p̃s. b̾ildixit finis tuus inte. Et ĩminitas apee cato. Qua xp̃c donatus e̾ ps. p̾ucinsti eũ i bñ. dicidis ¶Infernans quo\uf1ac dupiex .s. adinacio. ps. laudat᷑ͣ ptc̃ce indesidus aĩe sue ⁊ ñquis bñdi. et cũ quis sibi tribuit bona que ht̃ atco. Iob. timebat enĩ ne forte peccau̾int fuii eius. ⁊ bñdix̾int deo incordib\uf1ac suis. Corꝑans ẽ ad carnis delecta tr̃em us. or̃s caro feñ. ⁊ oĩs gła euis qiͣ d̾r ꝑ ysaiam. ue qͥ niungitis domũ addom̃. ⁊ agr̃ ago copłatis us\uf1ac ad t̾minũ ioci. Nñquid ħ̾itabitis uos so',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.2812],
# [1.0000, 1.0000, 0.2812],
# [0.2812, 0.2812, 1.0000]])
```
<!--
### 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: 99,840 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 85.65 tokens</li><li>max: 473 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 85.65 tokens</li><li>max: 473 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Per totum namque mundum est mundus; et mundum persequitur mundus, coinquinatus mundum, perditus redemptum, damnatus salvatum.</code> | <code>Per totum namque mundum est mundus; et mundum persequitur mundus, coinquinatus mundum, perditus redemptum, damnatus salvatum.</code> |
| <code>motꝰ siait supͣ sepe dixmꝰ gꝰ anteon nem generanonem est motus ge eti am aute generaitionem primi mobilis est mo tus go etiam motus est. inte p̾mum mo tum᷑ ꝙ est impossibile go fint hec caisa ꝙ motus non eet̾ sꝑ momĩ p̾tito iprẽ ꝙ primum mobile oportet᷑ prius generari mẽe et postea moneri qr absq dubio se queret᷑ ꝙ quedam mutatio eet̃ anteil</code> | <code>motꝰ siait supͣ sepe dixmꝰ gꝰ anteon nem generanonem est motus ge eti am aute generaitionem primi mobilis est mo tus go etiam motus est. inte p̾mum mo tum᷑ ꝙ est impossibile go fint hec caisa ꝙ motus non eet̾ sꝑ momĩ p̾tito iprẽ ꝙ primum mobile oportet᷑ prius generari mẽe et postea moneri qr absq dubio se queret᷑ ꝙ quedam mutatio eet̃ anteil</code> |
| <code>Dictum est, id quod in nomine confuse significaretur, in definitione quae fit enumeratione partium, aperiri atque explicari. Quod fieri non potest, nisi per quarumdam partium nuncupationem; nihil enim dum explicatur oratione, totum simul dici potest. Quae cum ita sint, cumque omnis hujusmodi definitio quaedam sit partium distributio, quatuor his modis fieri potest. Aut enim substantiales partes explicantur, aut proprietatis partes dicuntur, aut quasi totius membra enumerantur, aut tanquam species dividuntur. Substantiales partes explicantur, cum ex genere ac differentiis definitio constituitur. Genus enim quod singulariter praedicatur, speciei totum est. Id genus sumptum in definitione, pars quaedam fit. Non enim solum speciem complet, nisi adjiciantur etiam differentiae, in quibus eadem ratio quae in genere est. Nam cum ipsae singulariter dictae totam speciem claudant, in definitione sumptae, partes speciei fiunt, quia non solum speciem quidem esse designant, sed etiam genus.</code> | <code>Dictum est, id quod in nomine confuse significaretur, in definitione quae fit enumeratione partium, aperiri atque explicari. Quod fieri non potest, nisi per quarumdam partium nuncupationem; nihil enim dum explicatur oratione, totum simul dici potest. Quae cum ita sint, cumque omnis hujusmodi definitio quaedam sit partium distributio, quatuor his modis fieri potest. Aut enim substantiales partes explicantur, aut proprietatis partes dicuntur, aut quasi totius membra enumerantur, aut tanquam species dividuntur. Substantiales partes explicantur, cum ex genere ac differentiis definitio constituitur. Genus enim quod singulariter praedicatur, speciei totum est. Id genus sumptum in definitione, pars quaedam fit. Non enim solum speciem complet, nisi adjiciantur etiam differentiae, in quibus eadem ratio quae in genere est. Nam cum ipsae singulariter dictae totam speciem claudant, in definitione sumptae, partes speciei fiunt, quia non solum speciem quidem esse designant, sed etiam genus.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.6410 | 500 | 0.1311 |
### Framework Versions
- Python: 3.12.11
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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",
}
```
#### MultipleNegativesRankingLoss
```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.*
--> |