metadata

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: 0.36082474226804123
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5463917525773195
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5773195876288659
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6907216494845361
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.36082474226804123
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18213058419243983
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11546391752577319
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0690721649484536
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.36082474226804123
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5463917525773195
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5773195876288659
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6907216494845361
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5180083093560761
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.46394207167403045
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.47681473846718614
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.36082474226804123
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5360824742268041
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5773195876288659
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7010309278350515
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.36082474226804123
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.17869415807560135
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11546391752577319
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07010309278350516
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.36082474226804123
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5360824742268041
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5773195876288659
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7010309278350515
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5187124999739344
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4620520373097693
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4737872459927759
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.32989690721649484
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4948453608247423
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5773195876288659
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6804123711340206
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.32989690721649484
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1649484536082474
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11546391752577319
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06804123711340206
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.32989690721649484
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4948453608247423
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5773195876288659
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6804123711340206
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4929368061598079
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.43412698412698414
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.44657071536051934
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.3402061855670103
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5051546391752577
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5670103092783505
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6907216494845361
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.3402061855670103
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1683848797250859
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1134020618556701
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0690721649484536
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3402061855670103
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5051546391752577
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5670103092783505
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6907216494845361
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5032662355781912
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4449517263950254
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4553038204145196
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.32989690721649484
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4948453608247423
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5567010309278351
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6597938144329897
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.32989690721649484
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1649484536082474
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11134020618556702
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06597938144329896
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.32989690721649484
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4948453608247423
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.5567010309278351
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.6597938144329897
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.481245330711533
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.42577319587628865
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.43965778950983864
            name: Cosine Map@100

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("MugheesAwan11/bge-base-securiti-dataset-1-v12")
# 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]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3608
cosine_accuracy@3	0.5464
cosine_accuracy@5	0.5773
cosine_accuracy@10	0.6907
cosine_precision@1	0.3608
cosine_precision@3	0.1821
cosine_precision@5	0.1155
cosine_precision@10	0.0691
cosine_recall@1	0.3608
cosine_recall@3	0.5464
cosine_recall@5	0.5773
cosine_recall@10	0.6907
cosine_ndcg@10	0.518
cosine_mrr@10	0.4639
cosine_map@100	0.4768

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3608
cosine_accuracy@3	0.5361
cosine_accuracy@5	0.5773
cosine_accuracy@10	0.701
cosine_precision@1	0.3608
cosine_precision@3	0.1787
cosine_precision@5	0.1155
cosine_precision@10	0.0701
cosine_recall@1	0.3608
cosine_recall@3	0.5361
cosine_recall@5	0.5773
cosine_recall@10	0.701
cosine_ndcg@10	0.5187
cosine_mrr@10	0.4621
cosine_map@100	0.4738

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3299
cosine_accuracy@3	0.4948
cosine_accuracy@5	0.5773
cosine_accuracy@10	0.6804
cosine_precision@1	0.3299
cosine_precision@3	0.1649
cosine_precision@5	0.1155
cosine_precision@10	0.068
cosine_recall@1	0.3299
cosine_recall@3	0.4948
cosine_recall@5	0.5773
cosine_recall@10	0.6804
cosine_ndcg@10	0.4929
cosine_mrr@10	0.4341
cosine_map@100	0.4466

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3402
cosine_accuracy@3	0.5052
cosine_accuracy@5	0.567
cosine_accuracy@10	0.6907
cosine_precision@1	0.3402
cosine_precision@3	0.1684
cosine_precision@5	0.1134
cosine_precision@10	0.0691
cosine_recall@1	0.3402
cosine_recall@3	0.5052
cosine_recall@5	0.567
cosine_recall@10	0.6907
cosine_ndcg@10	0.5033
cosine_mrr@10	0.445
cosine_map@100	0.4553

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3299
cosine_accuracy@3	0.4948
cosine_accuracy@5	0.5567
cosine_accuracy@10	0.6598
cosine_precision@1	0.3299
cosine_precision@3	0.1649
cosine_precision@5	0.1113
cosine_precision@10	0.066
cosine_recall@1	0.3299
cosine_recall@3	0.4948
cosine_recall@5	0.5567
cosine_recall@10	0.6598
cosine_ndcg@10	0.4812
cosine_mrr@10	0.4258
cosine_map@100	0.4397

Training Details

Training Dataset

Unnamed Dataset

Size: 872 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 89 tokens
mean: 229.38 tokens
max: 414 tokens

min: 9 tokens
mean: 21.92 tokens
max: 102 tokens

	positive	anchor
type	string	string
details	min: 89 tokens mean: 229.38 tokens max: 414 tokens	min: 9 tokens mean: 21.92 tokens max: 102 tokens

Samples:

positive	anchor
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	`What information must the controller provide regarding their identity and contact details?`
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	`What are the key rights provided to data subjects under Ghana's Data Protection Act 2012?`
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	`What is the role of obtaining consent in Thailand's PDPA?`

Loss: MatryoshkaLoss with these parameters:

{
    "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: 10
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

Click to expand

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: 10
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

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	6.8967	-	-	-	-	-
0.7143	20	6.1128	-	-	-	-	-
1.0	28	-	0.4344	0.4387	0.4857	0.3831	0.4515
1.0714	30	4.4294	-	-	-	-	-
1.4286	40	3.2369	-	-	-	-	-
1.7857	50	3.2624	-	-	-	-	-
2.0	56	-	0.4345	0.4456	0.4752	0.3885	0.4672
2.1429	60	2.1973	-	-	-	-	-
2.5	70	1.815	-	-	-	-	-
2.8571	80	1.8725	-	-	-	-	-
3.0	84	-	0.4636	0.4469	0.4781	0.4012	0.4765
3.2143	90	1.2027	-	-	-	-	-
3.5714	100	1.3053	-	-	-	-	-
3.9286	110	1.1	-	-	-	-	-
4.0	112	-	0.4417	0.4282	0.4721	0.4154	0.4671
4.2857	120	0.8088	-	-	-	-	-
4.6429	130	0.8744	-	-	-	-	-
5.0	140	0.8075	0.4435	0.4443	0.4725	0.4116	0.4720
5.3571	150	0.5131	-	-	-	-	-
5.7143	160	0.6387	-	-	-	-	-
6.0	168	-	0.4495	0.4375	0.4768	0.4363	0.4794
6.0714	170	0.5041	-	-	-	-	-
6.4286	180	0.4053	-	-	-	-	-
6.7857	190	0.5665	-	-	-	-	-
7.0	196	-	0.4549	0.4504	0.4721	0.4382	0.4792
7.1429	200	0.3854	-	-	-	-	-
7.5	210	0.3085	-	-	-	-	-
7.8571	220	0.461	-	-	-	-	-
8.0	224	-	0.4570	0.4465	0.4722	0.4399	0.4785
8.2143	230	0.2521	-	-	-	-	-
8.5714	240	0.3944	-	-	-	-	-
8.9286	250	0.3524	-	-	-	-	-
9.0	252	-	0.4533	0.4457	0.4736	0.4394	0.4764
9.2857	260	0.2825	-	-	-	-	-
9.6429	270	0.3919	-	-	-	-	-
10.0	280	0.4004	0.4553	0.4466	0.4738	0.4397	0.4768

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

@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

@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

@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}
}