sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1872 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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: 0.28865979381443296
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5360824742268041
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6804123711340206
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7835051546391752
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.28865979381443296
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.17869415807560135
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1360824742268041
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07835051546391751
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.28865979381443296
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5360824742268041
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6804123711340206
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7835051546391752
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5259450080571785
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4444403534609721
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4516380787113637
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.29896907216494845
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5567010309278351
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7010309278350515
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7731958762886598
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.29896907216494845
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1855670103092783
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14020618556701028
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07731958762886595
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.29896907216494845
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5567010309278351
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.7010309278350515
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7731958762886598
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5284665496563921
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4504540991654395
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4581455693989837
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.27835051546391754
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5360824742268041
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6701030927835051
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7628865979381443
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.27835051546391754
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.17869415807560135
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.134020618556701
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07628865979381441
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.27835051546391754
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.5360824742268041
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.6701030927835051
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7628865979381443
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5088292931094907
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.42847733595156284
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4347457352503283
      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-v17")
# 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]
```

<!--
### 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   | 0.2887     |
| cosine_accuracy@3   | 0.5361     |
| cosine_accuracy@5   | 0.6804     |
| cosine_accuracy@10  | 0.7835     |
| cosine_precision@1  | 0.2887     |
| cosine_precision@3  | 0.1787     |
| cosine_precision@5  | 0.1361     |
| cosine_precision@10 | 0.0784     |
| cosine_recall@1     | 0.2887     |
| cosine_recall@3     | 0.5361     |
| cosine_recall@5     | 0.6804     |
| cosine_recall@10    | 0.7835     |
| cosine_ndcg@10      | 0.5259     |
| cosine_mrr@10       | 0.4444     |
| **cosine_map@100**  | **0.4516** |

#### 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   | 0.299      |
| cosine_accuracy@3   | 0.5567     |
| cosine_accuracy@5   | 0.701      |
| cosine_accuracy@10  | 0.7732     |
| cosine_precision@1  | 0.299      |
| cosine_precision@3  | 0.1856     |
| cosine_precision@5  | 0.1402     |
| cosine_precision@10 | 0.0773     |
| cosine_recall@1     | 0.299      |
| cosine_recall@3     | 0.5567     |
| cosine_recall@5     | 0.701      |
| cosine_recall@10    | 0.7732     |
| cosine_ndcg@10      | 0.5285     |
| cosine_mrr@10       | 0.4505     |
| **cosine_map@100**  | **0.4581** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2784     |
| cosine_accuracy@3   | 0.5361     |
| cosine_accuracy@5   | 0.6701     |
| cosine_accuracy@10  | 0.7629     |
| cosine_precision@1  | 0.2784     |
| cosine_precision@3  | 0.1787     |
| cosine_precision@5  | 0.134      |
| cosine_precision@10 | 0.0763     |
| cosine_recall@1     | 0.2784     |
| cosine_recall@3     | 0.5361     |
| cosine_recall@5     | 0.6701     |
| cosine_recall@10    | 0.7629     |
| cosine_ndcg@10      | 0.5088     |
| cosine_mrr@10       | 0.4285     |
| **cosine_map@100**  | **0.4347** |

<!--
## 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.*
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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 1,872 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                            | anchor                                                                             |
  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 207.32 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 21.79 tokens</li><li>max: 102 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | anchor                                                                                                                                                                             |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Automation PrivacyCenter.Cloud | Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | Breach Management | Privacy Notice Learn more 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,  PrivacyCenter.Cloud | Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | Breach Management | Privacy Notice Learn more 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</code>                                                                                                                                                                                                                     | <code>What is the purpose of Third Party & Cookie Consent in data automation and security?</code>                                                                                  |
  | <code>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</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>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?</code> |
  | <code>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</code> | <code>What are the data subjects' rights under GDPR regarding behavior monitoring, and how do they compare to the rights under PDPL?</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
      ],
      "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`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step   | Training Loss | dim_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                 |

* 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}
}
```

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