README.md

metadata

base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
  - en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1872
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      The Secretary of Health and Human.pathname_key_services may issue an
      Emergency Use Authorization (EUA) to authorize unapproved medical
      products, or unapproved uses of approved medical products, to be
      manufactured, marketed, and sold in the context of an actual or potential
      emergency designated by the government.
    sentences:
      - >-
        What was the aggregate intrinsic value of exercised stock options as of
        December 30, 2023?
      - >-
        What are some of the regulations related to data breach impact analysis
        and response?
      - >-
        What does the Emergency Use Authorization (EUA) by the U.S. Secretary of
        Health and Human Services allow?
  - source_sentence: >-
      the Virginia Consumer Data Protection Act protect consumers? The Virginia
      Consumer Data Protection Act protects consumers by prohibiting deceptive
      and unfair trade practices, giving consumers the right to sue for damages,
      and providing a mechanism for enforcement against businesses engaging in
      such practices. ## Join Our Newsletter Get all the latest information, law
      updates and more delivered to your inbox ### Share Copy 54 ### More
      Stories that May Interest You View More September 21, 2023 ## Navigating
      Generative AI Privacy Challenges & Safeguarding Tips Introduction The
      emergence of Generative AI has ushered in a new era of innovation in the
      ever-evolving technological landscape that pushes the boundaries of...
      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
    sentences:
      - >-
        What is Securiti's mission and history regarding Italy's GDPR
        implementation and compliance?
      - Which states have enacted data privacy laws like the VCDPA?
      - >-
        How does the Virginia Consumer Data Protection Act protect consumers and
        how is this protection enforced?
  - source_sentence: >-
      Data Flow Intelligence & Governance Prevent sensitive data sprawl through
      real-time streaming platforms Learn more Data Consent Automation First
      Party Consent | Third Party & Cookie Consent Learn more Data Security
      Posture Management Secure sensitive data in hybrid multicloud and SaaS
      environments Learn more Data Breach Impact Analysis & Response Analyze
      impact of a data breach and coordinate response per global regulatory
      obligations Learn more Data Catalog Automatically catalog datasets and
      enable users to find, understand, trust and access data Learn more Data
      Lineage Track changes and transformations of data throughout its lifecycle
      Data Controls Orchestrator View Data Command Center View Sensitive Data
      Intelligence View Asset Discovery Data Discovery & Classification
      Sensitive Data Catalog People Data Graph Learn more Privacy ,  Sensitive
      Data Intelligence Discover & Classify Structured and Unstructured Data |
      People Data Graph Learn more Data Flow Intelligence & Governance Prevent
      sensitive data sprawl through real-time streaming platforms Learn more
      Data Consent Automation First Party Consent | Third Party & Cookie Consent
      Learn more Data Security Posture Management Secure sensitive data in
      hybrid multicloud and SaaS environments Learn more Data Breach Impact
      Analysis & Response Analyze impact of a data breach and coordinate
      response per global regulatory obligations Learn more Data Catalog
      Automatically catalog datasets and enable users to find, understand, trust
      and access data Learn more Data Lineage Track changes and transformations
      of data throughout its lifecycle Data Controls Orchestrator View Data
      Command Center View Sensitive Data Intelligence View
    sentences:
      - >-
        Why is it important to manage security of sensitive data in hybrid
        multicloud and SaaS environments, prevent data sprawl, and analyze the
        impact of data breaches?
      - >-
        What right does the consumer have regarding their personal data in terms
        of deletion?
      - What is the legal basis for the LGPD in Brazil?
  - source_sentence: >-
      its lifecycle Data Controls Orchestrator View Data Command Center View
      Sensitive Data Intelligence View Asset Discovery Data Discovery &
      Classification Sensitive Data Catalog People Data Graph Learn more Privacy
      Automate compliance with global privacy regulations Data Mapping
      Automation View Data Subject Request Automation View People Data Graph
      View Assessment Automation View Cookie Consent View Universal Consent View
      Vendor Risk Assessment View Breach Management View Privacy Policy
      Management View Privacy Center View Learn more Security Identify data risk
      and enable protection & control Data Security Posture Management View Data
      Access Intelligence & Governance View Data Risk Management View
    sentences:
      - >-
        What is ANPD's primary goal regarding LGPD and its rights and
        regulations?
      - >-
        What options are there for joining the Securiti team and expanding
        knowledge in data privacy, security, and governance?
      - >-
        How does the Data Controls Orchestrator help automate compliance with
        global privacy regulations?
  - source_sentence: >-
      remediate the incident, promptly notify relevant individuals, and report
      such data security incidents to the regulatory department(s). Thus, you
      should have a robust security breach response mechanism in place. ## 7\.
      Cross border data transfer and data localization requirements: Under DSL,
      Critical Information Infrastructure Operators are required to store the
      important data in the territory of China and cross-border transfer is
      regulated by the CSL. CIIOs need to conduct a security assessment in
      accordance with the measures jointly defined by CAC and the relevant
      departments under the State Council for the cross-border transfer of
      important data for business necessity. For non Critical Information
      Infrastructure operators, the important data cross-border transfer will be
      regulated by the measures announced by the Cyberspace Administration of
      China (CAC) and other authorities. However, those “measures” have still
      not yet been released. DSL also intends to establish a data national
      security review and export control system to restrict the cross-border
      transmission of data
    sentences:
      - >-
        What are the requirements for storing important data in the territory of
        China under DSL?
      - >-
        How does behavioral targeting relate to the processing of personal data
        under Bahrain PDPL?
      - >-
        What is the margin of error generally estimated for worldwide Monthly
        Active People (MAP)?
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: 0.27835051546391754
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5463917525773195
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6494845360824743
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7835051546391752
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.27835051546391754
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18213058419243983
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.12989690721649483
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07835051546391751
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.27835051546391754
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5463917525773195
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6494845360824743
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7835051546391752
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5204365648204007
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4373834069710358
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.44377152224424676
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.28865979381443296
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5463917525773195
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6597938144329897
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7731958762886598
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.28865979381443296
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18213058419243983
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1319587628865979
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07731958762886597
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.28865979381443296
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5463917525773195
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6597938144329897
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7731958762886598
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5234913842554121
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4444403534609721
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.45150068207403454
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.26804123711340205
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4845360824742268
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6494845360824743
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7628865979381443
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26804123711340205
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16151202749140892
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.12989690721649483
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07628865979381441
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26804123711340205
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.4845360824742268
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.6494845360824743
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7628865979381443
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4964329019488686
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4132302405498282
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.41983416368750226
            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-v18")
# Run inference
sentences = [
    'remediate the incident, promptly notify relevant individuals, and report such data security incidents to the regulatory department(s). Thus, you should have a robust security breach response mechanism in place. ## 7\\. Cross border data transfer and data localization requirements: Under DSL, Critical Information Infrastructure Operators are required to store the important data in the territory of China and cross-border transfer is regulated by the CSL. CIIOs need to conduct a security assessment in accordance with the measures jointly defined by CAC and the relevant departments under the State Council for the cross-border transfer of important data for business necessity. For non Critical Information Infrastructure operators, the important data cross-border transfer will be regulated by the measures announced by the Cyberspace Administration of China (CAC) and other authorities. However, those “measures” have still not yet been released. DSL also intends to establish a data national security review and export control system to restrict the cross-border transmission of data',
    'What are the requirements for storing important data in the territory of China under DSL?',
    'What is the margin of error generally estimated for worldwide Monthly Active People (MAP)?',
]
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	0.2784
cosine_accuracy@3	0.5464
cosine_accuracy@5	0.6495
cosine_accuracy@10	0.7835
cosine_precision@1	0.2784
cosine_precision@3	0.1821
cosine_precision@5	0.1299
cosine_precision@10	0.0784
cosine_recall@1	0.2784
cosine_recall@3	0.5464
cosine_recall@5	0.6495
cosine_recall@10	0.7835
cosine_ndcg@10	0.5204
cosine_mrr@10	0.4374
cosine_map@100	0.4438

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.2887
cosine_accuracy@3	0.5464
cosine_accuracy@5	0.6598
cosine_accuracy@10	0.7732
cosine_precision@1	0.2887
cosine_precision@3	0.1821
cosine_precision@5	0.132
cosine_precision@10	0.0773
cosine_recall@1	0.2887
cosine_recall@3	0.5464
cosine_recall@5	0.6598
cosine_recall@10	0.7732
cosine_ndcg@10	0.5235
cosine_mrr@10	0.4444
cosine_map@100	0.4515

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.268
cosine_accuracy@3	0.4845
cosine_accuracy@5	0.6495
cosine_accuracy@10	0.7629
cosine_precision@1	0.268
cosine_precision@3	0.1615
cosine_precision@5	0.1299
cosine_precision@10	0.0763
cosine_recall@1	0.268
cosine_recall@3	0.4845
cosine_recall@5	0.6495
cosine_recall@10	0.7629
cosine_ndcg@10	0.4964
cosine_mrr@10	0.4132
cosine_map@100	0.4198

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,872 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 4 tokens mean: 207.32 tokens max: 414 tokens	min: 2 tokens mean: 21.79 tokens max: 102 tokens

• Samples:

  positive	anchor
  Automation PrivacyCenter.Cloud	Data Mapping
  the Tietosuojalaki. ### Greece #### Greece Effective Date : August 28, 2019 Region : EMEA (Europe, Middle East, Africa) Greek Law 4624/2019 was enacted to implement the GDPR and Directive (EU) 2016/680. The Hellenic Data Protection Agency (Αρχή προστασίας δεδομένων προσωπικού χαρακτήρα) is primarily responsible for overseeing the enforcement and implementation of Law 4624/2019 as well as the ePrivacy Directive within Greece. ### Iceland #### Iceland Effective Date : July 15, 2018 Region : EMEA (Europe, Middle East, Africa) ​​Act 90/2018 on Data Protection and Processing	What is the role of the Hellenic Data Protection Agency in overseeing the enforcement and implementation of Greek Law 4624/2019 and the ePrivacy Directive in Greece?
  EU. GDPR also applies to organizations located outside the EU (those that do not have an establishment in the EU) if they offer goods or services to, or monitor the behavior of, data subjects located in the EU, irrespective of their nationality or the company’s location. ## Data Subject Rights PDPL provides individuals rights relating to their personal data, which they can exercise. Under PDPL, the data controller should ensure the identity verification of the data subject before processing his/her data subject request. Also, the data controller must not charge for data subjects for making the data subject requests. The data subject may file a complaint to the Authority against the data controller, where the data subject does not accept the data controller’s decision regarding the request, or if the prescribed period has expired without the data subject’s receipt of any notice regarding his request. GDPR also ensures data subject rights where the data subjects can request the controller or, whatever their nationality or place of residence, concerning the processing of their personal data.” Regarding extraterritorial scope, GDPR applies to organizations that are not established in the EU, but instead monitor individuals’ behavior, as long as their behavior occurs in the EU. GDPR also applies to organizations located outside the EU (those that do not have an establishment in the EU) if they offer goods or services to, or monitor the behavior of, data subjects located in the EU, irrespective of their nationality or the company’s location. ## Rights Both regulations give individuals rights relating to their personal data, which they can exercise. Under LPPD, the data controller must process data subject’ requests and take all necessary administrative and technical measures within 30 days. LPPD does not provide a period extension. There is no fee for the data subject’ request to data controllers. However, the data controller may impose a fee, as set by the	What are the data subjects' rights under GDPR regarding behavior monitoring, and how do they compare to the rights under PDPL?

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256
      ],
      "matryoshka_weights": [
          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: 4
• 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: 4
• 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_256_cosine_map@100	dim_512_cosine_map@100	dim_768_cosine_map@100
0.1695	10	3.9813	-	-	-
0.3390	20	2.6276	-	-	-
0.5085	30	1.7029	-	-	-
0.6780	40	0.641	-	-	-
0.8475	50	0.391	-	-	-
1.0	59	-	0.4761	0.4928	0.4919
0.1695	10	1.362	-	-	-
0.3390	20	0.7574	-	-	-
0.5085	30	0.5287	-	-	-
0.6780	40	0.096	-	-	-
0.8475	50	0.0699	-	-	-
1.0	59	-	0.4483	0.4913	0.4925
1.0169	60	0.25	-	-	-
1.1864	70	1.043	-	-	-
1.3559	80	0.8176	-	-	-
1.5254	90	0.6276	-	-	-
1.6949	100	0.0992	-	-	-
1.8644	110	0.0993	-	-	-
2.0	118	-	0.4469	0.4785	0.4862
0.1695	10	1.0617	-	-	-
0.3390	20	0.7721	-	-	-
0.5085	30	0.6991	-	-	-
0.6780	40	0.095	-	-	-
0.8475	50	0.0695	-	-	-
1.0	59	-	0.4519	0.4786	0.4748
1.0169	60	0.1892	-	-	-
1.1864	70	0.7125	-	-	-
1.3559	80	0.5113	-	-	-
1.5254	90	0.437	-	-	-
1.6949	100	0.0432	-	-	-
1.8644	110	0.0471	-	-	-
2.0	118	-	0.4347	0.4581	0.4516
0.1695	10	0.7237	-	-	-
0.3390	20	0.5054	-	-	-
0.5085	30	0.4194	-	-	-
0.6780	40	0.0437	-	-	-
0.8475	50	0.0388	-	-	-
1.0	59	-	0.4582	0.4692	0.4748
1.0169	60	0.1513	-	-	-
1.1864	70	0.5249	-	-	-
1.3559	80	0.3878	-	-	-
1.5254	90	0.3353	-	-	-
1.6949	100	0.0223	-	-	-
1.8644	110	0.0248	-	-	-
2.0	118	-	0.4251	0.4460	0.4439
2.0339	120	0.1012	-	-	-
2.2034	130	0.3534	-	-	-
2.3729	140	0.2937	-	-	-
2.5424	150	0.1769	-	-	-
2.7119	160	0.0107	-	-	-
2.8814	170	0.0102	-	-	-
3.0	177	-	0.4245	0.4448	0.4488
3.0508	180	0.1054	-	-	-
3.2203	190	0.2246	-	-	-
3.3898	200	0.2323	-	-	-
3.5593	210	0.1045	-	-	-
3.7288	220	0.0082	-	-	-
3.8983	230	0.0123	-	-	-
4.0	236	-	0.4198	0.4515	0.4438

• 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}
}