File size: 61,044 Bytes
873cb8e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 |
---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:900
- 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: 'ographics, analyst reports and more.
Knowledge Center
Learn about the data privacy, security and governance landscape.
Securiti Education
Courses and Certifications for data privacy, security and governance professionals.
Company
About Us
Learn all about Securiti, our mission and history
Partner Program
Join our Partner Program
Contact Us
Contact us to learn more or schedule a demo
News Coverage
Read about Securiti in the news
Press Releases
Find our latest press releases
Careers
Join the talented Securiti team
Knowledge Center » Data Privacy Automation
# What is Irish Data Protection Act of 2018
By Securiti Research Team
Published February 2, 2021 / Updated September 28, 2023
The Irish Data Protection Act, 2018 (Irish DPA) implements the General Data Protection
Regulation (GDPR) and transposes the European Union Law Enforcement Directive
in Ireland. Since it incorporates most of the provisions from the GDPR and the
Law Enforcement Directive with limited additions and deletions as per the national
law, it is considered to be the principal data protection legislation in Ireland.
Table of contents
Rights of Data Subjects
Responsibilities of data controllers
Irish DPC Cookie Consent Guidelines
Automating Compliance
### Rights of Data Subjects
The Irish DPA provides the same rights to data subjects with respect to their
personal data as that of the GDPR. These rights give data subjects control over
their data and may be processed under particular conditions and limitations.
## Right to be informed
Data subjects have the right to be informed of when and how their data is being
used and collected. This refers to the obligation of the data controller to inform
and notify any relevant details to the data subjects for any important action
taken on their data.
## Right to access
On a request of the data subject, an organization must provide data subject access
to his/her personal data and information about the ways personal data has been
or may have been used, disclosed, or processed by the organization.
## Right to restriction of processing
This right applies when the accuracy of data is contested by the data subject
and when processing is unlawful and the data subject opposes the deletion of the
data. Data subjects need to be informed before any such restriction is lifted.
## Right to data port'
sentences:
- What is the PDPA Act in Malaysia and how does it regulate the processing of personal
data?
- What is the purpose of the European Union GDPR?
- What does the Irish Data Protection Act of 2018 implement in relation to the Law
Enforcement Directive?
- source_sentence: '
Learn all about Securiti, our mission and history
Partner Program
Join our Partner Program
Contact Us
Contact us to learn more or schedule a demo
News Coverage
Read about Securiti in the news
Press Releases
Find our latest press releases
Careers
Join the talented Securiti team
Blog » Data Consent Automation
# Irish Guidance on Consent & Cookies – Grace Period ends on 5 October
By Securiti Research Team
Published October 1, 2020 / Updated October 6, 2023
On 6 April, the Data Protection Commission of Ireland (DPC) released a substantive
Guidance Note on cookies (Guidance) and provided organizations a grace period
of six months to ensure compliance. After the end of the six- month window, which
is 5 October 2020, the Irish DPC may act to enforce the Guidance and can hold
organizations liable for failing to obtain valid consent before the processing
of cookies.
This Guidance was issued based on the report released by the DPC on the findings
of a “cookie sweep survey”. The survey was conducted on around 38 organizations
operating within the territory of Ireland and around 35 of those companies were
found to be significantly lacking in cookie compliance requirements. The DPC noticed
the following non-compliance practices of organizations, among others:
Dropping of non
essential cookies on landing pages without obtaining user’s consent,
The lifespans of most cookies that are dropped are not proportionate to the purposes
of the cookies,
Inadequate cookie banners,
Frequent use of pre
checked boxes for the processing of non
essential cookies,
A lack of stand
alone cookie policies,
Failure to fulfill the requirements of a valid consent as per the General Data
Protection Regulation (GDPR) and the Irish e
Privacy Regulations.
Based on its identification of the above non-compliance areas, the Irish DPC released
the comprehensive Guidance for organizations. The Guidance explains the purposes
of cookies as well as it adheres to the requirements of the GDPR, e-Privacy Directive,
and the Guidelines on Consent of the European Data Protection Board, released
on 4 May 2020 that declared cookie walls invalid.
_Read EDPB’s Updated Guidelines on Consent_
The Guidance also complements the landmark decision by the Court of Justice of
the'
sentences:
- What are the requirements for valid consent under the GDPR and Irish e-Privacy
Regulations according to the Irish DPC's Guidance on cookies?
- What are the CPPA's duties in enforcing CCPA and CPRA?
- What legislative measures has Spain taken to protect citizens' personal information
and data, and how does it compare to Saudi Arabia's data protection law?
- source_sentence: ' are:
Regulation No. 20 of 2016 concerning Protection of Personal Data in Electronic
Systems (MoCI Reg);
Amended Law No. 11 of 2008 on Electronic Information and Transaction (EIT Law);
Government Regulation No. 71 of 2019 on the Implementation of the Electronic System
and Transaction (GR 71).
There are also sectoral regulations that regulate the personal data in a specific
sector e.g, banking sector, health sector, etc.
## Indonesia’s Incoming Personal Data Protection (PDP) Law
Following delays due to COVID-19, Indonesia is now geared to pass its first Personal
Data Protection Act (PDP Law). On January 24, 2020, the bill’s final draft was
submitted to the Indonesian House of Representatives. The PDP law will address
the much-needed reforms to the country’s data privacy protection rules. The law
is built on the European Union’s General Data Protection Regulation (GDPR).
In essence, Indonesia will soon follow the same data subject rights and personal
data processing regulations set by the European Union in their GDPR.
The PDP Law will have 72 articles across 15 chapters. These articles and chapters
will extensively cover data ownership rights, prohibitions on data use, along
with the collection, storage, processing, and transfer of personal data of Indonesian
users.
With Indonesia being an active part of the global economy and attracting millions
of tourists annually, businesses should quickly align their business operations
to comply with the upcoming PDP law.
## Who Needs to Comply with the PDP Law
The PDP law will impact local businesses in Indonesia and will also have an impact
on companies across the globe that deal with Indonesian consumers. . The PDP law
will apply to any registered company dealing with Indonesian residents, irrespective
of where they are registered.
Whether an entity is public or private, local or international, the PDP Law will
automatically apply to them if they deal with the personal data of Indonesian
residents. The new PDP Law is expected to apply to all sectors, bringing forward
comprehensive provisions on personal data protection, both electronically and
non-electronically.
### Material Scope of the PDP Law
The PDP law will regulate sensitive personal data as well as other personal data
that may endanger or harm the privacy of the data subject.
### Territorial Scope of the Law
The PDP Bill applies to companies both within and outside of the territory of
Indonesia where'
sentences:
- What does Securiti offer businesses in terms of automating privacy and security
processes, and why is it important for businesses to embrace robotic automation
for compliance?
- What companies will be impacted by Indonesia's Personal Data Protection Law?
- What is the purpose of the Data Command Center in relation to the company's products
and solutions?
- source_sentence: 'Contact us to learn more or schedule a demo
News Coverage
Read about Securiti in the news
Press Releases
Find our latest press releases
Careers
Join the talented Securiti team
Knowledge Center » Data Privacy Automation
# What is China’s Data Security Law?
By Securiti Research Team
Published August 9, 2021 / Updated October 2, 2023
In China, the following are three main laws that cover the data privacy and data
security regime:
The Cybersecurity Law of the People’s Republic of China (the “CSL”), implemented
on June 1, 2017.
The Personal Information Protection Law of the People’s Republic of China (the
“PIPL”), effective from November 1, 2021.
The Data Security Law (the “DSL”), will be implemented from September 1, 2021.
The focus of this article is on the DSL that was promulgated to standardize data
processing activities, ensure data security, promote data development and utilization,
and protect the legitimate rights and interests of individuals and organizations.
Table of contents
Scope of Application and Extraterritorial Effect of DSL
Penalties for Non
Compliance
How Securiti Can Help
## Scope of Application and Extraterritorial Effect of DSL
The DSL applies to and regulates data processing activities by organizations and
individuals, and security supervision of such activities within the territory
of China. The DSL also regulates data processing activities conducted outside
of China that harm China’s national security or the public interest, or the legal
interests of citizens and organizations in China. It would be right to state that
DSL has extensive and extra-territorial application. It imposes a number of obligations
on organizations and individuals even those that are not based in China regarding
data categorization and classification, data risk controls and risk assessments,
cross-border data transfers, and data export controls.
The DSL applies to data recorded in electronic and other forms including digital
and cyber information, and information recorded in other forms such as paper records.
Data processing activities regulated by DSL include, without limitation, the collection,
storage, use, processing, transmission, provision, or disclosure of data.
Organizations and individuals need to understand and fulfill the following requirements
of the DSL in order to avoid unnecessary compliance risks and penalties:
##'
sentences:
- What can organizations do to manage vendor risk in relation to data protection
and compliance, considering Kenya's Data Protection Act 2019?
- What are the time limits for organizations to respond to a request under the NZ
Privacy Act 2020, and what are the requirements for transferring personal information
outside NZ?
- How does the Data Security Law in China contribute to data standardization, security
protection, and development?
- source_sentence: "\n\nOblige with Data Localization Requirements:\n\nOblige with\
\ Product Safety and Certifications Requirements:\n\nFulfill Content Monitoring\
\ Requirements:\n\nChina’s Cybersecurity Law (the “CSL”), which went into effect\
\ on June 1st, 2017, applies to the construction, operation, maintenance, and\
\ use of information networks, and the supervision and administration of cybersecurity\
\ in China. The CSL provides guidelines on cybersecurity requirements for safeguarding\
\ Chinese cyberspace. The law protects the legal interests and rights of organizations\
\ as well as individuals in China. It also promotes the secure development of\
\ technology and the digitization of the economy in China. Following entities\
\ come under the application scope of the CSL:\n\n**Network Operators:\n\n** It\
\ refers to the owners and administrators of networks and network service providers,\
\ and could be interpreted to include any companies providing services, or running\
\ their business through a computer network in China.\n\n**Critical Information\
\ Infrastructure Operators (CIIOs):\n\n** It refers to operators of critical information\
\ infrastructure in important industries and sectors (such as information service,\
\ public service, and e\n\ngovernment) and other information infrastructure that,\
\ if leaked, may severely threaten the national security, national economy, people’s\
\ livelihood, and public interests.\n\n**Network Products and Services Providers:\n\
\n** Organizations that provide information through networks or provide services\
\ to obtain information, including users, network services providers which provide\
\ network tools, devices, media, etc.\n\nCompliance with the CSL is not straightforward\
\ since CSL has several ambiguities and complicated obligations for network operators\
\ and CIIOs. Additional laws and guidelines will also be considered concerning\
\ the CSL compliance, including guidelines concerning the security assessment\
\ of cross- border transfers of personal information and important data, Data\
\ Security Law (DSL), and recently promulgated Personal Information Protection\
\ Law (PIPL).\n\nWe have prepared the following compliance checklist for the covered\
\ entities to ensure compliance with the CSL. Please note that this is not an\
\ exhaustive compliance list. For a detailed overview of the CSL, please refer\
\ to our article on What is China’s Cybersecurity Law?\n\n## 1\\. Fulfill Network\
\ Operations Security Requirements:\n\n## A. Requirements for network operators:\n\
\nNetwork operators must adopt the following security measures to prevent network\
\ interference, damage, or unauthorized access, and prevent network data from\
\ leakage, theft, or alteration:\n\nEstablish internal, \n## 5\\. Oblige with\
\ Product Safety and Certifications Requirements:\n\n## A. Requirements for Network\
\ Products and Services Providers:\n\nCybersecurity product manufacturers, security\
\ service suppliers, and other organizations that provide services through networks\
\ should oblige with the following requirements:\n\nNetwork products and services\
\ providers must not set up malicious programs.\n\nUpon discovering a security\
\ flaw, vulnerability, or another risk in their product or service, they must\
\ take remedial action immediately, inform users and report the issue to the relevant\
\ departments.\n\nNetwork product and service providers are required to conduct\
\ security maintenance for their products and services.\n\n## B. Requirements\
\ for CIIOs:\n\nCIIOs must, when procuring network products and services that\
\ may impact national security, submit the products and services to CAC and the\
\ State Council departments for a review for national security purposes. Critical\
\ network equipment and special cybersecurity products can only be sold or provided\
\ after being certified by a qualified establishment, and are in compliance with\
\ national standards.\n\n## 6\\. Fulfill Content Monitoring Requirements:\n\n\
According to Article 47 of the CSL, network operators are required to monitor\
\ the information released by their users for information that is “prohibited\
\ from being published or transmitted by laws or administrative regulations. If\
\ such information is discovered, network operators must cease the transmission\
\ of information, remove the information, keep records, and report any unlawful\
\ content to relevant authorities. Securiti helps organizations automate their\
\ privacy management operations using artificial intelligence and robotic automation.\
\ Request a demo and start your CSL compliance process today.\n\n## Join Our Newsletter\n\
\nGet all the latest information, law updates and more delivered to your inbox\n\
\n### Share\n\nCopy\n\n55\n\n### More Stories that May Interest You\n\nView More\n\
\nSeptember 11, 2023\n\n## Securiti named a Leader in the IDC MarketScape for\
\ Data Privacy Compliance Software\n\nSecuriti has just been recognized as a Leader\
\ in the “IDC MarketScape: Worldwide Data Privacy Compliance Software 2023 Vendor\
\ Assessment” report. This makes us...\n\nView More\n\nMay 10, 2023\n\n## Privacy\n\
\nby\n\nDesign and Privacy\n\nby\n\nDefault\n\nPrivacy-by-design and privacy-by-default\
\ are two cornerstone concepts of data protection regulatory frameworks. Thus,\
\ compliance thereof is an essential legal prerequisite for any entity which...\n\
\nView More\n\nApril 5,"
sentences:
- What are the 13 IPPs of New Zealand's Privacy Act 2020 that apply to all organizations,
including those outside of New Zealand, offering goods/services to individuals
in New Zealand or collecting information about individuals in New Zealand?
- What security measures must network operators adopt to fulfill content monitoring
requirements under China's Cybersecurity Law, and what obligations do network
products and services providers and CIIOs have in relation to product safety and
certifications?
- How does the PDPA in Malaysia protect personal data in commercial transactions
and who does it apply to?
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: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.27
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.45
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.67
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06699999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.27
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.45
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.67
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.33828063637415534
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.23589682539682535
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24406326043435023
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.06
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.25
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.39
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.66
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08333333333333331
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06599999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.25
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.39
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.66
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31695711820935435
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2123928571428571
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22150012925090945
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.05
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.24
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.38
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.08
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07600000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.38
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2931065726305541
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.19853174603174606
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.21132630111968292
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.05
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.36
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.55
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09333333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05499999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.28
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.55
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.278284909333787
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.19384126984126987
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20776022518923803
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.04
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.3
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.53
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.04
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.06000000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05299999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.04
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.53
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.25162020276083924
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.16670634920634916
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.177182977653562
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-v7")
# Run inference
sentences = [
'\n\nOblige with Data Localization Requirements:\n\nOblige with Product Safety and Certifications Requirements:\n\nFulfill Content Monitoring Requirements:\n\nChina’s Cybersecurity Law (the “CSL”), which went into effect on June 1st, 2017, applies to the construction, operation, maintenance, and use of information networks, and the supervision and administration of cybersecurity in China. The CSL provides guidelines on cybersecurity requirements for safeguarding Chinese cyberspace. The law protects the legal interests and rights of organizations as well as individuals in China. It also promotes the secure development of technology and the digitization of the economy in China. Following entities come under the application scope of the CSL:\n\n**Network Operators:\n\n** It refers to the owners and administrators of networks and network service providers, and could be interpreted to include any companies providing services, or running their business through a computer network in China.\n\n**Critical Information Infrastructure Operators (CIIOs):\n\n** It refers to operators of critical information infrastructure in important industries and sectors (such as information service, public service, and e\n\ngovernment) and other information infrastructure that, if leaked, may severely threaten the national security, national economy, people’s livelihood, and public interests.\n\n**Network Products and Services Providers:\n\n** Organizations that provide information through networks or provide services to obtain information, including users, network services providers which provide network tools, devices, media, etc.\n\nCompliance with the CSL is not straightforward since CSL has several ambiguities and complicated obligations for network operators and CIIOs. Additional laws and guidelines will also be considered concerning the CSL compliance, including guidelines concerning the security assessment of cross- border transfers of personal information and important data, Data Security Law (DSL), and recently promulgated Personal Information Protection Law (PIPL).\n\nWe have prepared the following compliance checklist for the covered entities to ensure compliance with the CSL. Please note that this is not an exhaustive compliance list. For a detailed overview of the CSL, please refer to our article on What is China’s Cybersecurity Law?\n\n## 1\\. Fulfill Network Operations Security Requirements:\n\n## A. Requirements for network operators:\n\nNetwork operators must adopt the following security measures to prevent network interference, damage, or unauthorized access, and prevent network data from leakage, theft, or alteration:\n\nEstablish internal, \n## 5\\. Oblige with Product Safety and Certifications Requirements:\n\n## A. Requirements for Network Products and Services Providers:\n\nCybersecurity product manufacturers, security service suppliers, and other organizations that provide services through networks should oblige with the following requirements:\n\nNetwork products and services providers must not set up malicious programs.\n\nUpon discovering a security flaw, vulnerability, or another risk in their product or service, they must take remedial action immediately, inform users and report the issue to the relevant departments.\n\nNetwork product and service providers are required to conduct security maintenance for their products and services.\n\n## B. Requirements for CIIOs:\n\nCIIOs must, when procuring network products and services that may impact national security, submit the products and services to CAC and the State Council departments for a review for national security purposes. Critical network equipment and special cybersecurity products can only be sold or provided after being certified by a qualified establishment, and are in compliance with national standards.\n\n## 6\\. Fulfill Content Monitoring Requirements:\n\nAccording to Article 47 of the CSL, network operators are required to monitor the information released by their users for information that is “prohibited from being published or transmitted by laws or administrative regulations. If such information is discovered, network operators must cease the transmission of information, remove the information, keep records, and report any unlawful content to relevant authorities. Securiti helps organizations automate their privacy management operations using artificial intelligence and robotic automation. Request a demo and start your CSL compliance process today.\n\n## Join Our Newsletter\n\nGet all the latest information, law updates and more delivered to your inbox\n\n### Share\n\nCopy\n\n55\n\n### More Stories that May Interest You\n\nView More\n\nSeptember 11, 2023\n\n## Securiti named a Leader in the IDC MarketScape for Data Privacy Compliance Software\n\nSecuriti has just been recognized as a Leader in the “IDC MarketScape: Worldwide Data Privacy Compliance Software 2023 Vendor Assessment” report. This makes us...\n\nView More\n\nMay 10, 2023\n\n## Privacy\n\nby\n\nDesign and Privacy\n\nby\n\nDefault\n\nPrivacy-by-design and privacy-by-default are two cornerstone concepts of data protection regulatory frameworks. Thus, compliance thereof is an essential legal prerequisite for any entity which...\n\nView More\n\nApril 5,',
"What security measures must network operators adopt to fulfill content monitoring requirements under China's Cybersecurity Law, and what obligations do network products and services providers and CIIOs have in relation to product safety and certifications?",
'How does the PDPA in Malaysia protect personal data in commercial transactions and who does it apply to?',
]
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 | 0.08 |
| cosine_accuracy@3 | 0.27 |
| cosine_accuracy@5 | 0.45 |
| cosine_accuracy@10 | 0.67 |
| cosine_precision@1 | 0.08 |
| cosine_precision@3 | 0.09 |
| cosine_precision@5 | 0.09 |
| cosine_precision@10 | 0.067 |
| cosine_recall@1 | 0.08 |
| cosine_recall@3 | 0.27 |
| cosine_recall@5 | 0.45 |
| cosine_recall@10 | 0.67 |
| cosine_ndcg@10 | 0.3383 |
| cosine_mrr@10 | 0.2359 |
| **cosine_map@100** | **0.2441** |
#### 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 | 0.06 |
| cosine_accuracy@3 | 0.25 |
| cosine_accuracy@5 | 0.39 |
| cosine_accuracy@10 | 0.66 |
| cosine_precision@1 | 0.06 |
| cosine_precision@3 | 0.0833 |
| cosine_precision@5 | 0.078 |
| cosine_precision@10 | 0.066 |
| cosine_recall@1 | 0.06 |
| cosine_recall@3 | 0.25 |
| cosine_recall@5 | 0.39 |
| cosine_recall@10 | 0.66 |
| cosine_ndcg@10 | 0.317 |
| cosine_mrr@10 | 0.2124 |
| **cosine_map@100** | **0.2215** |
#### 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 | 0.05 |
| cosine_accuracy@3 | 0.24 |
| cosine_accuracy@5 | 0.38 |
| cosine_accuracy@10 | 0.6 |
| cosine_precision@1 | 0.05 |
| cosine_precision@3 | 0.08 |
| cosine_precision@5 | 0.076 |
| cosine_precision@10 | 0.06 |
| cosine_recall@1 | 0.05 |
| cosine_recall@3 | 0.24 |
| cosine_recall@5 | 0.38 |
| cosine_recall@10 | 0.6 |
| cosine_ndcg@10 | 0.2931 |
| cosine_mrr@10 | 0.1985 |
| **cosine_map@100** | **0.2113** |
#### 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 | 0.05 |
| cosine_accuracy@3 | 0.28 |
| cosine_accuracy@5 | 0.36 |
| cosine_accuracy@10 | 0.55 |
| cosine_precision@1 | 0.05 |
| cosine_precision@3 | 0.0933 |
| cosine_precision@5 | 0.072 |
| cosine_precision@10 | 0.055 |
| cosine_recall@1 | 0.05 |
| cosine_recall@3 | 0.28 |
| cosine_recall@5 | 0.36 |
| cosine_recall@10 | 0.55 |
| cosine_ndcg@10 | 0.2783 |
| cosine_mrr@10 | 0.1938 |
| **cosine_map@100** | **0.2078** |
#### 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 | 0.04 |
| cosine_accuracy@3 | 0.21 |
| cosine_accuracy@5 | 0.3 |
| cosine_accuracy@10 | 0.53 |
| cosine_precision@1 | 0.04 |
| cosine_precision@3 | 0.07 |
| cosine_precision@5 | 0.06 |
| cosine_precision@10 | 0.053 |
| cosine_recall@1 | 0.04 |
| cosine_recall@3 | 0.21 |
| cosine_recall@5 | 0.3 |
| cosine_recall@10 | 0.53 |
| cosine_ndcg@10 | 0.2516 |
| cosine_mrr@10 | 0.1667 |
| **cosine_map@100** | **0.1772** |
<!--
## 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: 900 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: 159 tokens</li><li>mean: 446.78 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 22.04 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------|
| <code> issues related to the organization's privacy officers, exemption from consent requirements, biometric information registration, and breach reports. The next two stages will come into effect in September 2023 and September 2024, respectively.<br><br>### Hong Kong<br><br>#### Hong Kong Personal Data (Privacy) Ordinance (PDPO)<br><br>**Effective Date** : Since 1995 **Region** : APAC (Asia-Pacific)<br><br>The PDPO is the primary legislation in Hong Kong which was enacted to protect the privacy of individuals’ personal data, and regulate the collection, holding, processing, disclosure, or use of personal data by the organizations. The PDPO applies to private and public sector organizations that process, use, hold, or collect personal data. It covers any organization that deals with the collection and processing of personal data irrespective of the location of processing, provided that the personal data is controlled by the data user based in Hong Kong.<br><br>Resources*<br><br>:<br><br>Hong Kong PDPO Overview<br><br>### Ireland<br><br>#### Irish Data Protection Act (DPA)<br><br>**Effective Date** : May 24, 2018 **Region** : EMEA (Europe, the Middle East and Africa)<br><br>The Irish DPA implements the GDPR into the national law by incorporating most of the provisions of the GDPR with limited additions and deletions. It contains several provisions restricting data subjects’ rights that they generally have under the GDPR, for example, where restrictions are necessary for the enforcement of civil law claims.<br><br>Resources*<br><br>:<br><br>Irish DPA Overview<br><br>Irish Cookie Guidance<br><br>### Japan<br><br>#### Japan’s Act on the Protection of Personal Information (APPI)<br><br>**Effective Date (Amended APPI)** : April 01, 2022 **Region** : APAC (Asia-Pacific)<br><br>Japan’s APPI regulates personal related information and applies to any Personal Information Controller (the “PIC''), that is a person or entity providing personal related information for use in business in Japan. The APPI also applies to the foreign PICs which handle personal information of data subjects (“principals”) in Japan for the purpose of supplying goods or services to those persons.The act ensures the individual’s rights to privacy and also the legal use of personal data for economic development.<br><br>Resources*<br><br>:<br><br>Japan APPI Overview<br><br>### New Zealand<br><br>#### New Zealand</code> | <code>What are the regulations regarding breach reports in New Zealand?</code> |
| <code> data. Finally, as previously mentioned, consumers can opt-out of the collection of their sensitive personal data.<br><br>**Means to submit DSR request:<br><br>** A consumer may exercise a right by submitting an authenticated request to a controller, by means prescribed by the controller, specifying the right the consumer intends to exercise. In the instance of processing personal data concerning a child, the parent or legal guardian of the child can exercise a right on the child's behalf. In the case of processing personal data concerning a consumer subject to guardianship, conservatorship, or other protective arrangements under Title 75, Chapter 5, Protection of Persons Under Disability and Their Property, the guardian or the conservator of the consumer shall exercise a right on the consumer's behalf.<br><br>**Time period to fulfill DSR request<br><br>** : A controller shall comply with a consumer's request to exercise a right within 45 days after the day on which a controller had received that particular request. The controller then shall take action on the consumer's request; and inform the consumer of any action taken on the consumer's request.<br><br>**Extension in the time period:<br><br>** An additional 45 days can be granted if it is reasonably necessary to comply with the request, keeping in mind the complexity of the request or the volume of the requests received by the controller. In such cases, the controller is to inform the consumer of the extension and provide reasons for the extension.<br><br>**Charges:<br><br>** Controllers are not allowed to charge a fee for responding to a request under the law apart from certain situations. If the request is a consumer's second or subsequent request within the same 12<br><br>month period, a controller may charge a reasonable fee. A controller may also charge a reasonable fee to cover the administrative costs of complying with a request or refuse to act on a request if:<br><br>the request is excessive, repetitive, technically infeasible as per the law; or<br><br>the controller considers that the primary goal for the submitted request was something other than exercising a right; or<br><br>the request disrupts or imposes an undue burden on the resources of the controller’s business.<br><br>**Appeal against refusal:<br><br>** The data controller may choose to not to take action on a consumer’s DSR request. It must provide the consumer the reasons for which it did not take the action within the 45 days time period of receiving the DSR request. The data controller may also choose to not honor the request</code> | <code>What is the time frame for a controller to fulfill a consumer's request to exercise a right, and what can extend this period?</code> |
| <code> or use of personal data. This is the same as the term 'data controller.'<br><br>## Data Processor<br><br>Data Processor is a person or entity who processes personal data on behalf of another person or entity (a data user) instead of for his/her purpose(s).<br><br>## Consent<br><br>Consent is not a prerequisite for collecting personal data unless the personal data is used for a new purpose or for direct marketing purposes. Where consent is required, consent means to express and voluntary consent.<br><br>## Data Subjects' Rights under the PDPO:<br><br>The PDPO prescribes the following rights for the data subjects;<br><br>DPP 6 provides data subjects with the right to request access to and correction of their personal data. A data user should give reasons when refusing a data subject’s request to access or correction of his/her personal data.<br><br>Data subjects have the right to be informed by data user(s) regarding the holding of their personal data.<br><br>There is no explicit right to erasure available under the PDPO, however, data subjects can request the data user to delete his/her personal data that is no longer necessary for the processing. Also, data users are not allowed to retain personal data longer than necessary.<br><br>Under the PDPO, there is no right to object to processing (including profiling) available, but data subjects may opt<br><br>out from direct marketing activities.<br><br>## **Who needs to comply with the PDPO**?<br><br>The PDPO applies to private and public sector organizations that process, use, hold, or collect personal data. It covers any organization that deals with the collection and processing of personal data irrespective of the location of processing provided that the personal data is controlled by the data user based in Hong Kong.<br><br>The PDPO provides the following exemptions for the processing of personal data in Part VIII;<br><br>specified public or judicial interests<br><br>domestic or recreational purposes, or for<br><br>employment purposes.<br><br>The PDPO does not directly regulate data processors; therefore, they do not directly come under the application scope of the PDPO. However, data users are required to, by contractual or other means, ensure that their data processors meet the applicable requirements of the PDPO.<br><br>## **Organizations' obligations under the PDPO:**<br><br>PDPO does not explicitly state accountability principles and other privacy management related measures; however, the PCPD recommends</code> | <code>What rights do data subjects have under the PDPO regarding the right to object to processing, and what are the limitations?</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
- `gradient_accumulation_steps`: 2
- `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`: 2
- `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 |
|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6897 | 10 | 8.029 | - | - | - | - | - |
| 0.9655 | 14 | - | 0.2004 | 0.2241 | 0.2170 | 0.1726 | 0.2279 |
| 1.3793 | 20 | 5.6389 | - | - | - | - | - |
| **1.931** | **28** | **-** | **0.2078** | **0.2113** | **0.2215** | **0.1772** | **0.2441** |
* 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.*
--> |