File size: 48,664 Bytes
695f6b8 4fed24b 695f6b8 0dc1635 695f6b8 4ad2652 695f6b8 4ad2652 695f6b8 4ad2652 695f6b8 4ad2652 695f6b8 4ad2652 695f6b8 4ad2652 695f6b8 054ca62 0dc1635 054ca62 5bebfbf 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 5bebfbf 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 5bebfbf 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 0dc1635 054ca62 695f6b8 0dc1635 695f6b8 0dc1635 695f6b8 0dc1635 695f6b8 0dc1635 695f6b8 0dc1635 695f6b8 054ca62 695f6b8 1644761 4ad2652 695f6b8 1644761 0dc1635 695f6b8 1644761 695f6b8 0dc1635 695f6b8 054ca62 10087e5 0dc1635 054ca62 0dc1635 054ca62 695f6b8 4fed24b 054ca62 0dc1635 054ca62 4ad2652 054ca62 78f1db4 054ca62 99e7ada 054ca62 695f6b8 4ad2652 0dc1635 695f6b8 4ad2652 695f6b8 78f1db4 695f6b8 054ca62 0dc1635 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 0dc1635 695f6b8 054ca62 0dc1635 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 054ca62 695f6b8 0dc1635 4834bb0 695f6b8 054ca62 695f6b8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:90000
- loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-small
widget:
- source_sentence: who is the publisher of the norton anthology american literature
sentences:
- W. W. Norton & Company W. W. Norton & Company is an American publishing company
based in New York City. It has been owned wholly by its employees since the early
1960s. The company is known for its "Norton Anthologies" (particularly The Norton
Anthology of English Literature) and its texts in the Norton Critical Editions
series, the latter of which are frequently assigned in university literature courses.
- New Orleans La Nouvelle-Orléans (New Orleans) was founded in Spring of 1718 (7
May has become the traditional date to mark the anniversary, but the actual day
is unknown[25]) by the French Mississippi Company, under the direction of Jean-Baptiste
Le Moyne de Bienville, on land inhabited by the Chitimacha. It was named for Philippe
II, Duke of Orléans, who was Regent of the Kingdom of France at the time. His
title came from the French city of Orléans.
- I Really Like You The music video was directed by Peter Glanz. Jepsen filmed part
of the song's music video on 16 February 2015, in front of the Mondrian Hotel
in Manhattan alongside Tom Hanks, Justin Bieber and a troupe of dancers. Also
making cameo appearances in the video are Rudy Mancuso and Andrew B. Bachelor
(A.K.A. King Bach), well-known users of the short-form video sharing application
Vine. The video was released on 6 March 2015.[15] CBC Music's Nicolle Weeks described
it as "a more affable version" of the music video for The Verve's "Bitter Sweet
Symphony" (1997).[16] The music video has been rated as one of 10 Best Music Videos
of 2015 (So Far) by the readers of Billboard.[17]
- source_sentence: how many members on the house of representatives
sentences:
- 'United States House of Representatives The composition and powers of the House
are established by Article One of the United States Constitution. The House is
composed of Representatives who sit in congressional districts that are allocated
to each of the 50 states on a basis of population as measured by the U.S. Census,
with each district entitled to one representative. Since its inception in 1789,
all Representatives have been directly elected. The total number of voting representatives
is fixed by law at 435.[1] As of the 2010 Census, the largest delegation is that
of California, with fifty-three representatives. Seven states have the smallest
delegation possible, a single representative: Alaska, Delaware, Montana, North
Dakota, South Dakota, Vermont, and Wyoming.[2]'
- Ain't No Mountain High Enough "Ain't No Mountain High Enough" is an R&B/soul song
written by Nickolas Ashford & Valerie Simpson in 1966 for the Tamla label, a division
of Motown. The composition was first successful as a 1967 hit single recorded
by Marvin Gaye and Tammi Terrell, becoming a hit again in 1970 when recorded by
former Supremes frontwoman Diana Ross. The song became Ross' first solo number-one
hit on the Billboard Hot 100 chart and was nominated for a Grammy Award.
- Synthetic element In chemistry, a synthetic element is a chemical element that
does not occur naturally on Earth, and can only be created artificially. So far,
24 synthetic elements have been created (those with atomic numbers 95–118).
All are unstable, decaying with half-lives ranging from 15.6 million years to
a few hundred microseconds.
- source_sentence: what is the meaning of mbbs and md
sentences:
- Adductor longus muscle Its main actions is to adduct and laterally rotate the
thigh; it can also produce some degree of flexion/anteversion.[1]
- Category 6 cable When used for 10/100/1000BASE-T, the maximum allowed length of
a Cat 6 cable is up to 100 meters (328 ft). This consists of 90 meters (295 ft)
of solid "horizontal" cabling between the patch panel and the wall jack, plus
5 meters (16 ft) of stranded patch cable between each jack and the attached device.[7]
For 10GBASE-T, an unshielded Cat 6 cable should not exceed 55 meters.[8]
- Doctor of Medicine Historically, Australian medical schools have followed the
British tradition by conferring the degrees of Bachelor of Medicine and Bachelor
of Surgery (MBBS) to its graduates whilst reserving the title of Doctor of Medicine
(MD) for their research training degree, analogous to the PhD, or for their honorary
doctorates. Although the majority of Australian MBBS degrees have been graduate
programs since the 1990s, under the previous Australian Qualifications Framework
(AQF) they remained categorized as Level 7 Bachelor's degrees together with other
undergraduate programs.
- source_sentence: what holds the bone ends of an amphiarthrodial joint together
sentences:
- Pubic symphysis The pubic symphysis is a nonsynovial amphiarthrodial joint. The
name comes from the Greek word "symphysis", meaning "growing together". The width
of the pubic symphysis at the front is 3–5 mm greater than its width at the back.
This joint is connected by fibrocartilage and may contain a fluid-filled cavity;
the center is avascular, possibly due to the nature of the compressive forces
passing through this joint, which may lead to harmful vascular disease.[2] The
ends of both pubic bones are covered by a thin layer of hyaline cartilage attached
to the fibrocartilage. The fibrocartilaginous disk is reinforced by a series of
ligaments. These ligaments cling to the fibrocartilaginous disk to the point that
fibers intermix with it.
- 'John 3:16 In Exodus 4:22, the Israelites as a people are called "my firstborn
son" by God using the singular form. In John, the focus shifts to the person of
Jesus as representative of that title. The verse is part of the New Testament
narrative in the third chapter of John in the discussion at Jerusalem between
Jesus and Nicodemus, who is called a "ruler of the Jews". (v.1) After speaking
of the necessity of a man being born again before he could "see the kingdom of
God", (v.3) Jesus spoke also of "heavenly things" (v.11-13) and of salvation (v.14-17)
and the condemnation (v.18,19) of those that do not believe in Jesus. "14 And
as Moses lifted up the serpent in the wilderness, even so must the Son of man
be lifted up: 15 That whosoever believeth in him should not perish, but have eternal
life." (John 3:14-15) Note that verse 15 is nearly identical to the latter part
of John 3:16.'
- Tony Hadley Anthony Patrick Hadley (born 2 June 1960) is an English singer-songwriter,
occasional stage actor and radio presenter. He rose to fame in the 1980s as the
lead singer of the new wave band Spandau Ballet before launching a solo career
following the group's split in 1990. Hadley is recognisable for his suave image,[1]
as well as his powerful blue-eyed soul voice, which has been described by AllMusic
as a "dramatic warble".[2] He has also been described as a "top crooner" by the
BBC.[3]
- source_sentence: what are the 5 liberties of the first amendment
sentences:
- First Amendment to the United States Constitution The First Amendment (Amendment
I) to the United States Constitution prohibits the making of any law respecting
an establishment of religion, ensuring that there is no prohibition on the free
exercise of religion, abridging the freedom of speech, infringing on the freedom
of the press, interfering with the right to peaceably assemble, or prohibiting
the petitioning for a governmental redress of grievances. It was adopted on December
15, 1791, as one of the ten amendments that constitute the Bill of Rights.
- Poor People's Campaign The SCLC announced the campaign on December 4, 1967. King
delivered a speech which identified "a kind of social insanity which could lead
to national ruin."[23] In January 1968, the SCLC created and distributed an "Economic
Fact Sheet" with statistics explaining why the campaign was necessary.[24] King
avoided providing specific details about the campaign and attempted to redirect
media attention to the values at stake.[25] The Poor People’s Campaign held firm
to the movement’s commitment to non-violence. “We are custodians of the philosophy
of non-violence,” said King at a press conference. “And it has worked”.[9] King
originally wanted the Poor People's Campaign to start in Quitman County, Mississippi
because of the intense and visible economic disparity there.[26]
- 'Jake and the Never Land Pirates Jake and the Never Land Pirates (also known as
Captain Jake and the Never Land Pirates in the fourth season and associated merchandise[1])
is an Annie Award-winning musical and interactive American children''s animated
television series shown on Disney Junior. It is based on Disney''s Peter Pan franchise,
which in turn is based on the famous book and play by British author J. M. Barrie.
It is the first Disney Junior original show following the switch from Playhouse
Disney. It stars Sean Ryan Fox from Henry Danger, Megan Richie, Jadon Sand, David
Arquette, Corey Burton, Jeff Bennett, Loren Hoskins and Dee Bradley Baker. The
title character Jake was previously voiced by Colin Ford, and then later by Cameron
Boyce, while Izzy was voiced for the first three seasons by Madison Pettis and
Cubby was voiced by Jonathan Morgan Heit. The series is created by Disney veteran
Bobs Gannaway, whose works include another Disney Junior series, Mickey Mouse
Clubhouse, and films such as Secret of the Wings, The Pirate Fairy and Planes:
Fire & Rescue. The last episode aired on November 6, 2016.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on thenlper/gte-small
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.38
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.66
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19333333333333336
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.132
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.38
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.58
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.66
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5747352409361379
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5051349206349205
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5163932476955072
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.46
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.64
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.21333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15200000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.44
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.69
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.72
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5958872018988118
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5641904761904761
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5591780429569271
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.42000000000000004
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.61
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.69
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.77
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42000000000000004
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20333333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14200000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.41000000000000003
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.59
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.675
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.76
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5853112214174749
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5346626984126983
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5377856453262171
name: Cosine Map@100
---
# SentenceTransformer based on thenlper/gte-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-small](https://huggingface.co/thenlper/gte-small). It maps sentences & paragraphs to a 384-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:** [thenlper/gte-small](https://huggingface.co/thenlper/gte-small) <!-- at revision 17e1f347d17fe144873b1201da91788898c639cd -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 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/huggingface/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': 128, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 384, '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})
(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("redis/model-a-baseline")
# Run inference
sentences = [
'what are the 5 liberties of the first amendment',
'First Amendment to the United States Constitution The First Amendment (Amendment I) to the United States Constitution prohibits the making of any law respecting an establishment of religion, ensuring that there is no prohibition on the free exercise of religion, abridging the freedom of speech, infringing on the freedom of the press, interfering with the right to peaceably assemble, or prohibiting the petitioning for a governmental redress of grievances. It was adopted on December 15, 1791, as one of the ten amendments that constitute the Bill of Rights.',
"Jake and the Never Land Pirates Jake and the Never Land Pirates (also known as Captain Jake and the Never Land Pirates in the fourth season and associated merchandise[1]) is an Annie Award-winning musical and interactive American children's animated television series shown on Disney Junior. It is based on Disney's Peter Pan franchise, which in turn is based on the famous book and play by British author J. M. Barrie. It is the first Disney Junior original show following the switch from Playhouse Disney. It stars Sean Ryan Fox from Henry Danger, Megan Richie, Jadon Sand, David Arquette, Corey Burton, Jeff Bennett, Loren Hoskins and Dee Bradley Baker. The title character Jake was previously voiced by Colin Ford, and then later by Cameron Boyce, while Izzy was voiced for the first three seasons by Madison Pettis and Cubby was voiced by Jonathan Morgan Heit. The series is created by Disney veteran Bobs Gannaway, whose works include another Disney Junior series, Mickey Mouse Clubhouse, and films such as Secret of the Wings, The Pirate Fairy and Planes: Fire & Rescue. The last episode aired on November 6, 2016.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7343, 0.0079],
# [0.7343, 1.0000, 0.0383],
# [0.0079, 0.0383, 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoMSMARCO` and `NanoNQ`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNQ |
|:--------------------|:------------|:-----------|
| cosine_accuracy@1 | 0.38 | 0.46 |
| cosine_accuracy@3 | 0.58 | 0.64 |
| cosine_accuracy@5 | 0.66 | 0.72 |
| cosine_accuracy@10 | 0.8 | 0.74 |
| cosine_precision@1 | 0.38 | 0.46 |
| cosine_precision@3 | 0.1933 | 0.2133 |
| cosine_precision@5 | 0.132 | 0.152 |
| cosine_precision@10 | 0.08 | 0.08 |
| cosine_recall@1 | 0.38 | 0.44 |
| cosine_recall@3 | 0.58 | 0.6 |
| cosine_recall@5 | 0.66 | 0.69 |
| cosine_recall@10 | 0.8 | 0.72 |
| **cosine_ndcg@10** | **0.5747** | **0.5959** |
| cosine_mrr@10 | 0.5051 | 0.5642 |
| cosine_map@100 | 0.5164 | 0.5592 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nq"
],
"dataset_id": "lightonai/NanoBEIR-en"
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.42 |
| cosine_accuracy@3 | 0.61 |
| cosine_accuracy@5 | 0.69 |
| cosine_accuracy@10 | 0.77 |
| cosine_precision@1 | 0.42 |
| cosine_precision@3 | 0.2033 |
| cosine_precision@5 | 0.142 |
| cosine_precision@10 | 0.08 |
| cosine_recall@1 | 0.41 |
| cosine_recall@3 | 0.59 |
| cosine_recall@5 | 0.675 |
| cosine_recall@10 | 0.76 |
| **cosine_ndcg@10** | **0.5853** |
| cosine_mrr@10 | 0.5347 |
| cosine_map@100 | 0.5378 |
<!--
## 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: 90,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 11.82 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 106.2 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 104.63 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played in the movie throw momma from the train</code> | <code>Anne Ramsey Angelina (Anne) Ramsey (March 27, 1929[1] – August 11, 1988) was an American stage, television, and film actress. She was best known for portraying Mama Fratelli in The Goonies (1985) and Mrs. Lift, mother of Danny DeVito's protagonist, in Throw Momma from the Train (1987). The latter film saw Ramsey nominated for a Golden Globe Award and the Academy Award for Best Supporting Actress.</code> | <code>Aye Mere Watan Ke Logo "Aye Mere Watan Ke Logo" (Hindi: ऐ मेरे वतन के लोगों; "O' people of my country") is a Hindi patriotic song written by Kavi Pradeep, composed by C. Ramchandra, and performed by Lata Mangeshkar. The song commemorates Indian soldiers who died during the Sino-Indian War in 1962. The song was first performed live by Mangeshkar on 27 January 1963 at the National Stadium in New Delhi in the presence of President Sarvepalli Radhakrishnan and Prime Minister Jawaharlal Nehru, on account of Republic Day (26 January) 1963, which was just two months after the end of the war.</code> |
| <code>when was the wall in san diego built</code> | <code>Mexico–United States barrier In September 2017, the U.S. government announced the start of construction of eight prototype barriers made from concrete and other materials.[51][52] On June 3, 2018 the San Diego section of the US border wall construction began. [53]</code> | <code>The Dance of Dragons At the Wall, Jon Snow (Kit Harington) retreats from Hardhome defeated, accompanied by the surviving wildlings, much to the chagrin of some of the Night's Watch. In the North, Stannis Baratheon (Stephen Dillane) reluctantly allows Melisandre (Carice van Houten) to sacrifice his daughter Shireen (Kerry Ingram) after Ramsay Bolton (Iwan Rheon) sabotages his resources, resulting to his army's damaged morale. In Braavos, Arya Stark (Maisie Williams), detours from her mission given by Jaqen H'ghar (Tom Wlaschiha) to reconnoiter Meryn Trant (Ian Beattie) instead. In Dorne, Jaime Lannister (Nikolaj Coster-Waldau) secures Myrcella Baratheon's (Nell Tiger Free) release from Doran Martell's (Alexander Siddig) court against an indignant Ellaria Sand (Indira Varma). In Meereen, the Sons of the Harpy attack the stadium of Daznak's Pit in an attempt to assassinate Daenerys Targaryen (Emilia Clarke), who is rescued by Jorah Mormont (Iain Glen) and her firstborn dragon, Drogon. Lea...</code> |
| <code>urology is the study of diseases of the</code> | <code>Urology Urology (from Greek οὖρον ouron "urine" and -λογία -logia "study of"), also known as genitourinary surgery, is the branch of medicine that focuses on surgical and medical diseases of the male and female urinary-tract system and the male reproductive organs. Organs under the domain of urology include the kidneys, adrenal glands, ureters, urinary bladder, urethra, and the male reproductive organs (testes, epididymis, vas deferens, seminal vesicles, prostate, and penis).</code> | <code>The Jackie Gleason Show By far the most memorable and popular of Gleason's characters was blowhard Brooklyn bus driver Ralph Kramden, featured originally in a series of Cavalcade skits known as "The Honeymooners", with Pert Kelton as his wife Alice, and Art Carney as his upstairs neighbor Ed Norton. These were so popular that in 1955 Gleason suspended the variety format and filmed The Honeymooners as a regular half-hour sitcom (television's first spin-off), co-starring Carney, Audrey Meadows (who had replaced the blacklisted Kelton after the earlier move to CBS), and Joyce Randolph. Finishing 19th in the ratings, these 39 episodes were subsequently rerun constantly in syndication, often five nights a week, with the cycle repeating every two months for decades. They are probably the most familiar body of work from 1950s television with the exception of I Love Lucy starring Lucille Ball and Desi Arnaz.</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
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 10,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 11.76 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 105.95 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 105.69 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what is the political system that the united states follows in regards to elections</code> | <code>Primary election The United States is one of few countries to select candidates through popular vote in a primary election system; most countries rely on party leaders to vote candidates, as was previously the case in the U.S.[9] In modern politics, primary elections have been described as a significant vehicle for taking decision-making from political insiders to the voters, though this is disputed by select political science research.[10] The selection of candidates for federal, state, and local general elections takes place in primary elections organized by the public administration for the general voting public to participate in for the purpose of nominating the respective parties' official candidates; state voters start the electoral process for governors and legislators through the primary process, as well as for many local officials from city councilors to county commissioners.[11] The candidate who moves from the primary to be successful in the general election takes public off...</code> | <code>Bob Gaudio Robert John "Bob" Gaudio (born November 17, 1942) is an American singer, songwriter, musician, and record producer, and the keyboardist/backing vocalist for The Four Seasons.</code> |
| <code>what caused the unusual landscape at the valley of fire</code> | <code>Valley of Fire State Park Complex uplifting and faulting of the region, followed by extensive erosion, have created the present landscape. The rough floor and jagged walls of the park contain brilliant formations of eroded sandstone and sand dunes more than 150 million years old. Other important rock formations include limestones, shales, and conglomerates.[4]</code> | <code>Fundamental Constitutions of Carolina Because the Fundamental Constitutions were drafted during John Locke's service to one of Province of Carolina proprietors, Anthony Ashley Cooper, it is widely alleged that Locke had a major role in the making of the Constitutions. In the view of historian David Armitage[5] and political scientist Vicki Hsueh, the Constitutions were co-authored by Locke and his patron Cooper, known also as 1st Earl of Shaftesbury.[6] However the document was a legal document written for and signed and sealed by the eight Lord proprietors to whom Charles II had granted the colony; Locke was only a paid secretary. He wrote it much as a lawyer writes a will.[4]</code> |
| <code>according to the guinness world records what author has the most published works</code> | <code>Guinness World Records The book itself holds a world record, as the best-selling copyrighted book of all time. As of the 2017 edition, it is now in its 62nd year of publication, published in 100 countries and 23 languages. The international franchise has extended beyond print to include television series and museums. The popularity of the franchise has resulted in Guinness World Records becoming the primary international authority on the cataloguing and verification of a huge number of world records; the organisation employs official record adjudicators authorised to verify the authenticity of the setting and breaking of records.[2]</code> | <code>The Big Chill (film) The Big Chill is a 1983 American comedy-drama film directed by Lawrence Kasdan, starring Tom Berenger, Glenn Close, Jeff Goldblum, William Hurt, Kevin Kline, Mary Kay Place, Meg Tilly, and JoBeth Williams. The plot focuses on a group of baby boomers who attended the University of Michigan, reuniting after 15 years when their friend Alex commits suicide. Kevin Costner was cast as Alex, but all scenes showing his face were cut. It was filmed in Beaufort, South Carolina.[2]</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
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 8e-05
- `weight_decay`: 0.005
- `max_steps`: 1125
- `warmup_ratio`: 0.1
- `fp16`: True
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 1
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `ddp_find_unused_parameters`: False
- `push_to_hub`: True
- `hub_model_id`: redis/model-a-baseline
- `eval_on_start`: True
#### 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`: 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`: 8e-05
- `weight_decay`: 0.005
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 1125
- `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
- `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`: True
- `dataloader_num_workers`: 1
- `dataloader_prefetch_factor`: 1
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `ddp_find_unused_parameters`: False
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: redis/model-a-baseline
- `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`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:|
| 0 | 0 | - | 2.1869 | 0.6259 | 0.6583 | 0.6421 |
| 0.3556 | 250 | 0.3907 | 0.0761 | 0.5880 | 0.6146 | 0.6013 |
| 0.7112 | 500 | 0.0814 | 0.0680 | 0.5666 | 0.6170 | 0.5918 |
| 1.0669 | 750 | 0.0714 | 0.0634 | 0.5580 | 0.5846 | 0.5713 |
| 1.4225 | 1000 | 0.0406 | 0.0614 | 0.5747 | 0.5959 | 0.5853 |
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.2.0
- Transformers: 4.57.3
- PyTorch: 2.9.1+cu128
- Accelerate: 1.12.0
- Datasets: 2.21.0
- Tokenizers: 0.22.1
## 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",
}
```
#### 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.*
--> |