File size: 37,819 Bytes
454badc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 |
---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:872
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
- source_sentence: 'amendements to PIPA came into force on 05 Auguest 2020. 2 Some
parts of PIPA also apply to online service providers. 3 The latest amendment to
PIPA has introduced the concept of ‘pseudonymised data’ for the feasibility of
data economy. 4 Under the PIPA, all data handlers must appoint a chief privacy
officer. 5 Cookies, IP information, etc. are also regulated by the PIPA as personal
information. 6 Breach of a corrective order issued by the PIPC can lead to an
administrative fine of not more than KRW 30 million. ### Forrester Names Securiti
a Leader in the Privacy Management Wave Q4, 2021 Read the Report ### Securiti
named a Leader in the IDC MarketScape for Data Privacy Compliance Software Read
the Report At'
sentences:
- What recognition did Securiti receive in the field of data privacy?
- How does the Office of the Privacy Commissioner educate agencies and organisations
in breach of the law?
- What is the concept of 'pseudonymised data' introduced by the latest amendment
to PIPA?
- source_sentence: '18th, 2020, and it has been in effect since then. ## Influence
of GDPR It is well known that the LGPD was drafted and based on the GDPR, so much
so that some people call it Brazil’s GDPR. The LGPD contains 65 articles that
provide individuals with data subject rights, impose obligations upon organizations
for lawful processing of personal data, require notification of data breaches
to the supervisory authority and affected data subjects, create a national supervisory
authority to interpret and enforce the law, regulate international transfer of
data, define lawful consent collection guidelines and impose heavy penalties on
violators similar to the GDPR. ## Essence of the LGPD Law LGPD provides: 9 data
subject rights requests exercisable by individual data subjects; 10 legal bases
for lawful processing; Obligatory and transparent disclosure requirements for
organizations to contain within their privacy policy; Consent collection and management
requirements for organizations;'
sentences:
- What are the penalties for misusing personal data and obstructing investigations
under the PDPA and its amendments?
- Which data privacy regulation, similar to the GDPR, had a significant impact in
the US after the promulgation of the GDPR in the EU?
- What are the requirements for consent collection and management under the LGPD
law?
- source_sentence: 'to the Privacy Act of 2020. ## Obligations for Organisations Under
the Privacy Act 2020 Under the Privacy Act’s jurisdiction, all organizations have
specific responsibilities or obligations towards their users. The most important
of these obligations include the following: ### 1\. Lawful Purpose Requirements
While data processing has become immensely important for nearly all businesses,
the Privacy Act ensures that such data processing can only occur if the organization
collecting the data has a lawful purpose for the collection and that collection
of the information is necessary for that purpose. It is also expected that the
information will be collected directly from the individual concerned. When collecting
personal information, organizations are required to ensure the individual is aware
of: The fact that the information is being collected; The purpose for which it
is being collected; The intended recipients of the information; The details of
the organization that will be collecting and holding the information; Any laws
that authorize or'
sentences:
- What are the obligations of organizations towards users under the Privacy Act
of 2020, including lawful purpose and consent requirements?
- What is the role of the Spanish Data Protection Agency in enforcing data protection
legislation in Spain and how does it ensure its effectiveness in enforcing the
law across the country?
- What is the purpose of Kuwait's Data Privacy Protection Regulation (DPPR)?
- source_sentence: '## Right of Access to Personal Data: What To Know The wealth of
data available to organizations globally has brought tremendous improvements in
their ability to target and cater to their customers'' needs. Organizations...
View More September 13, 2023 ## Kuwait''s DPPR Kuwait didn’t have any data protection
law until the Communication and Information Technology Regulatory Authority (CITRA)
introduced the Data Privacy Protection Regulation (DPPR). The... ## Take a Product
Tour See how easy it is to manage privacy compliance with robotic automation.
Watch a demo At Securiti, our mission is to enable enterprises to safely harness
the incredible power of data and the cloud by controlling the complex security,
privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap
#### Newsletter #### Company About Us , Personal Data: What To Know The wealth
of data available to organizations globally has brought tremendous improvements
in their ability to target and cater to their customers'' needs. Organizations...
View More September 13, 2023 ## Kuwait''s DPPR Kuwait didn’t have any data protection
law until the Communication and Information Technology Regulatory Authority (CITRA)
introduced the Data Privacy Protection Regulation (DPPR). The... ## Take a Product
Tour See how easy it is to manage privacy compliance with robotic automation.
Watch a demo At Securiti, our mission is to enable enterprises to safely harness
the incredible power of data and the cloud by controlling the complex security,
privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap
#### Newsletter #### Company About Us Careers Contact Us'
sentences:
- What is the definition of personal data according to the PDPO?
- What are the requirements for organizations to notify the regulatory authority
in case of a data breach according to the PDPL and accompanying Regulations?
- Why did CITRA introduce Kuwait's DPPR?
- source_sentence: View Salesforce View Workday View GCP View Azure View Oracle View
Learn more Regulations Automate compliance with global privacy regulations. US
California CCPA View US California CPRA View European Union GDPR View Thailand’s
PDPA View China PIPL View Canada PIPEDA View Brazil's LGPD View \+ More View Learn
more Roles Identify data risk and enable protection & control. Privacy View Security
View Governance View Marketing View Resources Blog Read through our articles written
by industry experts Collateral Product broch
sentences:
- What resources are available for learning more about GCP?
- What are the penalties for unauthorized personal data transfer, including maximum
fines for data fiduciaries in various scenarios?
- What are the key provisions of South Korea's data privacy law?
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.32989690721649484
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5670103092783505
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6391752577319587
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7216494845360825
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32989690721649484
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18900343642611683
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12783505154639174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07216494845360824
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32989690721649484
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5670103092783505
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6391752577319587
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7216494845360825
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.518805689291338
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4544509900180003
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4661116752052667
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.3402061855670103
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5773195876288659
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6391752577319587
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.711340206185567
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3402061855670103
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1924398625429553
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12783505154639174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0711340206185567
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3402061855670103
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5773195876288659
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6391752577319587
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.711340206185567
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5235302122076325
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.46329569628538714
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4750840411397005
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.27835051546391754
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5154639175257731
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5979381443298969
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7010309278350515
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.27835051546391754
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17182130584192437
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11958762886597937
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07010309278350514
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.27835051546391754
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5154639175257731
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5979381443298969
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7010309278350515
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4836619509866766
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4146457208312879
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42661551290292493
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.31958762886597936
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4948453608247423
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5979381443298969
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6804123711340206
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.31958762886597936
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16494845360824742
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11958762886597937
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06804123711340206
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.31958762886597936
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4948453608247423
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5979381443298969
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6804123711340206
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48488869988900546
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.42372361315660284
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4348164067654526
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.25773195876288657
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4742268041237113
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5670103092783505
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6494845360824743
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.25773195876288657
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15807560137457044
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1134020618556701
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06494845360824741
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.25773195876288657
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4742268041237113
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5670103092783505
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6494845360824743
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4465366767058729
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.382228767795778
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.39411615598959504
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(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("MugheesAwan11/bge-base-securiti-dataset-1-v13")
# Run inference
sentences = [
"View Salesforce View Workday View GCP View Azure View Oracle View Learn more Regulations Automate compliance with global privacy regulations. US California CCPA View US California CPRA View European Union GDPR View Thailand’s PDPA View China PIPL View Canada PIPEDA View Brazil's LGPD View \\+ More View Learn more Roles Identify data risk and enable protection & control. Privacy View Security View Governance View Marketing View Resources Blog Read through our articles written by industry experts Collateral Product broch",
'What resources are available for learning more about GCP?',
"What are the key provisions of South Korea's data privacy law?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3299 |
| cosine_accuracy@3 | 0.567 |
| cosine_accuracy@5 | 0.6392 |
| cosine_accuracy@10 | 0.7216 |
| cosine_precision@1 | 0.3299 |
| cosine_precision@3 | 0.189 |
| cosine_precision@5 | 0.1278 |
| cosine_precision@10 | 0.0722 |
| cosine_recall@1 | 0.3299 |
| cosine_recall@3 | 0.567 |
| cosine_recall@5 | 0.6392 |
| cosine_recall@10 | 0.7216 |
| cosine_ndcg@10 | 0.5188 |
| cosine_mrr@10 | 0.4545 |
| **cosine_map@100** | **0.4661** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3402 |
| cosine_accuracy@3 | 0.5773 |
| cosine_accuracy@5 | 0.6392 |
| cosine_accuracy@10 | 0.7113 |
| cosine_precision@1 | 0.3402 |
| cosine_precision@3 | 0.1924 |
| cosine_precision@5 | 0.1278 |
| cosine_precision@10 | 0.0711 |
| cosine_recall@1 | 0.3402 |
| cosine_recall@3 | 0.5773 |
| cosine_recall@5 | 0.6392 |
| cosine_recall@10 | 0.7113 |
| cosine_ndcg@10 | 0.5235 |
| cosine_mrr@10 | 0.4633 |
| **cosine_map@100** | **0.4751** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2784 |
| cosine_accuracy@3 | 0.5155 |
| cosine_accuracy@5 | 0.5979 |
| cosine_accuracy@10 | 0.701 |
| cosine_precision@1 | 0.2784 |
| cosine_precision@3 | 0.1718 |
| cosine_precision@5 | 0.1196 |
| cosine_precision@10 | 0.0701 |
| cosine_recall@1 | 0.2784 |
| cosine_recall@3 | 0.5155 |
| cosine_recall@5 | 0.5979 |
| cosine_recall@10 | 0.701 |
| cosine_ndcg@10 | 0.4837 |
| cosine_mrr@10 | 0.4146 |
| **cosine_map@100** | **0.4266** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3196 |
| cosine_accuracy@3 | 0.4948 |
| cosine_accuracy@5 | 0.5979 |
| cosine_accuracy@10 | 0.6804 |
| cosine_precision@1 | 0.3196 |
| cosine_precision@3 | 0.1649 |
| cosine_precision@5 | 0.1196 |
| cosine_precision@10 | 0.068 |
| cosine_recall@1 | 0.3196 |
| cosine_recall@3 | 0.4948 |
| cosine_recall@5 | 0.5979 |
| cosine_recall@10 | 0.6804 |
| cosine_ndcg@10 | 0.4849 |
| cosine_mrr@10 | 0.4237 |
| **cosine_map@100** | **0.4348** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2577 |
| cosine_accuracy@3 | 0.4742 |
| cosine_accuracy@5 | 0.567 |
| cosine_accuracy@10 | 0.6495 |
| cosine_precision@1 | 0.2577 |
| cosine_precision@3 | 0.1581 |
| cosine_precision@5 | 0.1134 |
| cosine_precision@10 | 0.0649 |
| cosine_recall@1 | 0.2577 |
| cosine_recall@3 | 0.4742 |
| cosine_recall@5 | 0.567 |
| cosine_recall@10 | 0.6495 |
| cosine_ndcg@10 | 0.4465 |
| cosine_mrr@10 | 0.3822 |
| **cosine_map@100** | **0.3941** |
<!--
## 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: 872 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 89 tokens</li><li>mean: 229.38 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.92 tokens</li><li>max: 102 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
| <code>controller should inform the data subject in every situation where his or her personal data is processed. The LPPD provides a general requirement to provide information on the collection methods but does not explicitly refer to automated decision-making or profiling. vs Articles: 5 14, Recitals: 58 63 This right requires the controller to provide the following information to the data subject when requested. This should be given in a concise, transparent, intelligible, and easily accessible form, using plain language: The identity and contact details of the controller, controller’s representative, and DPO, where applicable The purpose and the legal basis of the processing The categories of personal data concerned The recipients of the personal data The appropriate or suitable safeguards and the means to obtain a copy of them or where they have been made available The controller must provide information necessary to ensure fair and transparent processing whether or not the personal</code> | <code>What information must the controller provide regarding their identity and contact details?</code> |
| <code>and deletions, and manage all vendor contracts and compliance documents. ## Key Rights Under Ghana’s Data Protection Act 2012 **Right to be Informed** : Data subjects have the right to be informed of the processing of their personal data and the purposes for which the data is processed. **Right to Access:** Data subjects have the right to obtain confirmation whether or not the controller holds personal data about them, access their personal data, and obtain descriptions of data recipients. **Right to Rectification** : Under the right to rectification, data subjects can request the correction of their data. **Right to Erasure:** Data subjects have the right to request the erasure and destruction of the data that is no longer needed by the organization. **Right to Object:** The data subject has the right to prevent the data controller from processing personal data if such processing causes or is likely to cause unwarranted damage or distress to the data</code> | <code>What are the key rights provided to data subjects under Ghana's Data Protection Act 2012?</code> |
| <code>aim to protect personal data, they have differences in scope, requirements, and applicability. PDPA applies to Thailand, while GDPR applies to the European Union. The effect of PDPA in Thailand is to regulate how personal data is processed, collected, used, and protected by individuals and organizations in the country. Thailand's PDPA includes provisions related to personal data breach notifications, requiring data controllers to notify the Personal Data Protection Committee (PDPC) of a personal data breach as soon as possible, preferably within 72 hours of becoming aware of it. The principles of PDPA in Thailand include obtaining consent, especially for minors, ensuring data security, issuing timely data breach notifications, designating a data protection officer, conducting data protection impact assessments, maintaining a record of processing activities, and ensuring adequate standards when transferring data across borders. ## Join Our Newsletter Get all the latest information, law updates and more delivered to your inbox ### Share</code> | <code>What is the role of obtaining consent in Thailand's PDPA?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `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`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: 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}
- `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`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3571 | 10 | 6.4098 | - | - | - | - | - |
| 0.7143 | 20 | 4.9339 | - | - | - | - | - |
| 1.0 | 28 | - | 0.4266 | 0.4263 | 0.4703 | 0.3934 | 0.4650 |
| 1.0714 | 30 | 3.7606 | - | - | - | - | - |
| 1.4286 | 40 | 2.5546 | - | - | - | - | - |
| 1.7857 | 50 | 3.1845 | - | - | - | - | - |
| **2.0** | **56** | **-** | **0.4348** | **0.4266** | **0.4751** | **0.3941** | **0.4661** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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.*
--> |