sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:7872 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:7872
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'personal information within 45 days. If personal information was
    sold, organizations must also identify and inform the consumer of the sources
    of information, its collection purpose, and the categories of third parties to
    whom the data was sold to. As per the CCPA, the following information must be
    provided in an access request: The categories of personal information the business
    has collected about the consumer in the preceding 12 months. For each category
    identified, the categories of third parties to whom it disclosed that particular
    category of personal information. The categories of sources from which the personal
    information was collected. The business or commercial purpose for which it collected
    or sold the personal information. The categories of third parties with whom the
    business shares consumers’ Personal Information. The right to access is one of
    the toughest articles for businesses to comply with because organizations need
    to track the location of every consumer’s personal information in all on-premises
    and multicloud data systems.'
  sentences:
  - What are the UCPA requirements for organizations regarding personal data handling,
    including pseudonymous and sensitive data, and data transfer to third parties
    in certain circumstances?
  - What are the benefits of implementing CCPA for businesses in terms of reducing
    costs, liabilities, and human effort while ensuring effortless compliance?
  - What information must organizations provide regarding the categories of third
    parties in relation to personal information under the CCPA?
- source_sentence: 'on businesses that meet these criteria, regardless of their physical
    presence in Colorado. Colorado is a one-party consent state for recording conversations.
    This means that as long as one participant in the conversation consents to the
    recording, it is generally legal. However, it''s important to understand and adhere
    to the specific legal requirements and limitations. ## Join Our Newsletter Get
    all the latest information, law updates and more delivered to your inbox ### Share
    Copy 41 ### 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 15,
    2023 ## Right of Access to Personal Data: What To Know The wealth of data available'
  sentences:
  - What solutions does Oracle offer for data security and governance?
  - What are the legal requirements for recording conversations in Colorado, considering
    consent laws and data protection regulations?
  - What are the key components of the NVIDIA computing platform?
- source_sentence: 'such personal data have been collected or where such collected
    personal data are beyond the extent required, discriminatory, unfair or illegal.
    ### Right to Erasure Data subjects can request omission or erasure of the personal
    data upon cessation of the purpose for which the processing has been conducted,
    or where all justifications for maintaining such personal data by the organization
    cease to exist. ## Facts related to Qatar DPL 1 The DPL incorporates concepts
    familiar from other international privacy frameworks to protect a consumer''s
    personal data. 2 Under the DPL, a data controller is responsible for identifying
    all parties who process personal data on its behalf. 3 In Qatar, the Compliance
    and Data Protection department (the “CDP”)at MoTC is responsible for the enforcement
    of the DPL. . 4 The MoTC can also impose fines of up to QAR 5 million (US$1.4
    million)'
  sentences:
  - What is Securiti's mission regarding data protection laws and regulations?
  - What is the role of the Nominating and Corporate Governance Committee at NVIDIA?
  - What is the right to erasure and how does it apply to personal data in Qatar under
    the DPL?
- source_sentence: '. It allows you to identify gaps in compliance and address the
    risks. Seamlessly expand assessment capabilities across your vendor ecosystem
    to maintain compliance against LPPD requirements. ## Map data flows Track data
    flows in your organizations by having a centralized catalogue of internal data
    process flows as well as flows for data transfer to service providers and other
    third parties. ## Manage vendor risk Articles: 8, 9, 12 Track, manage and monitor
    privacy and security readiness for all your service providers from a single interface.
    Collaborate instantly with vendors, automate data requests, and manage all vendor
    contracts and compliance documents. ## Breach Response Notification Article: 12(5),
    Data Protection Board Decision 2019/10 Automates compliance actions and breach
    notifications to concerned stakeholders in relation to security incidents by leveraging
    a knowledge database on security incident diagnosis and response. ## Key data
    subject rights encoded within LPPD Access: Data subjects have the right to access,
    , and privacy impact assessment system, you can gauge your organization''s posture
    against Qatar DPL requirements, identify the gaps, and address the risks. Seamlessly
    being able to expand assessment capabilities across your vendor ecosystem to maintain
    compliance against Qatar DPL requirements. ## Map data flows Articles: 23, 24,
    25 Track data flows in your organizations, trace this data, catalog, transfer,
    and document business process flows internally and to service providers or third
    parties. ## Manage vendor risk Articles: 15, 12 Keep track of privacy and security
    readiness for all your service providers from a single interface. Collaborate
    instantly with vendors, automate data requests and deletions, and manage all vendor
    contracts and compliance documents. ## Breach Response Notification Articles:
    11(5), 14 Automates compliance actions and breach notifications to concerned stakeholders
    in relation to security incidents by leveraging a knowledge database on security
    incident diagnosis and response.'
  sentences:
  - What is the purpose of a centralized catalogue in managing data flows, vendor
    risk, and compliance with LPPD and Qatar DPL requirements?
  - What are the security requirements for data handlers according to Spain's Data
    Protection Law?
  - What are some key rights granted to data subjects under Bahrain PDPL?
- source_sentence: 'office of the ​​Federal Commissioner for Data Protection and Freedom
    of Information, with its headquarters in the city of Bonn. It is led by a Federal
    Commissioner, elected via a vote by the German Bundestag. Eligibility criteria
    include being at least 35 years old, appropriate qualifications in the field of
    data protection law gained through relevant professional experience. The Commissioner''s
    term is for five years, which can be extended once. The Commissioner has the responsibility
    to act as the primary office responsible for enforcing the Federal Data Protection
    Act within Germany. Some of the office''s key responsibilities include: Advising
    the Bundestag, the Bundesrat, and the Federal Government on administrative and
    legislative measures related to data protection within the country; To oversee
    and implement both the GDPR and Federal Data Protection Act within Germany; To
    promote awareness within the public related to the risks, rules, safeguards, and
    rights concerning the processing of personal data; To handle all,  within Germany.
    It supplements and aligns with the requirements of the EU GDPR. Yes, Germany is
    covered by GDPR (General Data Protection Regulation). GDPR is a regulation that
    applies uniformly across all EU member states, including Germany. The Federal
    Data Protection Act established the office of the ​​Federal Commissioner for Data
    Protection and Freedom of Information, with its headquarters in the city of Bonn.
    It is led by a Federal Commissioner, elected via a vote by the German Bundestag.
    Germany''s interpretation is the Bundesdatenschutzgesetz (BDSG), the German Federal
    Data Protection Act. It mirrors the GDPR in all key areas while giving local German
    regulatory authorities the power to enforce it more efficiently nationally. ##
    Join Our Newsletter Get all the latest information, law updates and more delivered
    to your inbox ### Share Copy 14 ### More Stories that May Interest You View More'
  sentences:
  - What is the collection and use of personal information by businesses?
  - How does Data Mapping Automation optimize data governance and enable data security
    and protection?
  - What are the main responsibilities of the Federal Commissioner for Data Protection
    and Freedom of Information in enforcing data protection laws in Germany, including
    the GDPR and the Federal Data Protection Act?
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: 0.6804123711340206
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9072164948453608
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9484536082474226
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9690721649484536
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6804123711340206
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.302405498281787
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18969072164948453
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09690721649484535
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6804123711340206
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9072164948453608
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9484536082474226
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9690721649484536
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8365594057778603
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7924807723776797
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7937238734919148
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.6907216494845361
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8762886597938144
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9278350515463918
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9690721649484536
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6907216494845361
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2920962199312715
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.18556701030927836
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09690721649484535
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6907216494845361
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8762886597938144
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9278350515463918
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9690721649484536
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8329963353635171
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7889011618393064
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7896128390908116
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.6907216494845361
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8556701030927835
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8969072164948454
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9278350515463918
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.6907216494845361
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2852233676975945
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.17938144329896905
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09278350515463918
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.6907216494845361
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8556701030927835
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8969072164948454
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9278350515463918
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8131932947524921
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7759286532482411
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7795112701355719
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.5979381443298969
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.7731958762886598
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8247422680412371
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8865979381443299
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5979381443298969
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.25773195876288657
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16494845360824742
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08865979381443297
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5979381443298969
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.7731958762886598
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8247422680412371
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8865979381443299
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.7462462760759706
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7009818360333826
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7046889034714706
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.5154639175257731
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6907216494845361
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.711340206185567
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7731958762886598
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5154639175257731
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.23024054982817868
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1422680412371134
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07731958762886597
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5154639175257731
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6907216494845361
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.711340206185567
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.7731958762886598
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6470540335294329
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6063696612665687
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6136636919376227
      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-v14")
# Run inference
sentences = [
    "office of the \u200b\u200bFederal Commissioner for Data Protection and Freedom of Information, with its headquarters in the city of Bonn. It is led by a Federal Commissioner, elected via a vote by the German Bundestag. Eligibility criteria include being at least 35 years old, appropriate qualifications in the field of data protection law gained through relevant professional experience. The Commissioner's term is for five years, which can be extended once. The Commissioner has the responsibility to act as the primary office responsible for enforcing the Federal Data Protection Act within Germany. Some of the office's key responsibilities include: Advising the Bundestag, the Bundesrat, and the Federal Government on administrative and legislative measures related to data protection within the country; To oversee and implement both the GDPR and Federal Data Protection Act within Germany; To promote awareness within the public related to the risks, rules, safeguards, and rights concerning the processing of personal data; To handle all,  within Germany. It supplements and aligns with the requirements of the EU GDPR. Yes, Germany is covered by GDPR (General Data Protection Regulation). GDPR is a regulation that applies uniformly across all EU member states, including Germany. The Federal Data Protection Act established the office of the \u200b\u200bFederal Commissioner for Data Protection and Freedom of Information, with its headquarters in the city of Bonn. It is led by a Federal Commissioner, elected via a vote by the German Bundestag. Germany's interpretation is the Bundesdatenschutzgesetz (BDSG), the German Federal Data Protection Act. It mirrors the GDPR in all key areas while giving local German regulatory authorities the power to enforce it more efficiently nationally. ## Join Our Newsletter Get all the latest information, law updates and more delivered to your inbox ### Share Copy 14 ### More Stories that May Interest You View More",
    'What are the main responsibilities of the Federal Commissioner for Data Protection and Freedom of Information in enforcing data protection laws in Germany, including the GDPR and the Federal Data Protection Act?',
    'What is the collection and use of personal information by businesses?',
]
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   | 0.6804     |
| cosine_accuracy@3   | 0.9072     |
| cosine_accuracy@5   | 0.9485     |
| cosine_accuracy@10  | 0.9691     |
| cosine_precision@1  | 0.6804     |
| cosine_precision@3  | 0.3024     |
| cosine_precision@5  | 0.1897     |
| cosine_precision@10 | 0.0969     |
| cosine_recall@1     | 0.6804     |
| cosine_recall@3     | 0.9072     |
| cosine_recall@5     | 0.9485     |
| cosine_recall@10    | 0.9691     |
| cosine_ndcg@10      | 0.8366     |
| cosine_mrr@10       | 0.7925     |
| **cosine_map@100**  | **0.7937** |

#### 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   | 0.6907     |
| cosine_accuracy@3   | 0.8763     |
| cosine_accuracy@5   | 0.9278     |
| cosine_accuracy@10  | 0.9691     |
| cosine_precision@1  | 0.6907     |
| cosine_precision@3  | 0.2921     |
| cosine_precision@5  | 0.1856     |
| cosine_precision@10 | 0.0969     |
| cosine_recall@1     | 0.6907     |
| cosine_recall@3     | 0.8763     |
| cosine_recall@5     | 0.9278     |
| cosine_recall@10    | 0.9691     |
| cosine_ndcg@10      | 0.833      |
| cosine_mrr@10       | 0.7889     |
| **cosine_map@100**  | **0.7896** |

#### 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   | 0.6907     |
| cosine_accuracy@3   | 0.8557     |
| cosine_accuracy@5   | 0.8969     |
| cosine_accuracy@10  | 0.9278     |
| cosine_precision@1  | 0.6907     |
| cosine_precision@3  | 0.2852     |
| cosine_precision@5  | 0.1794     |
| cosine_precision@10 | 0.0928     |
| cosine_recall@1     | 0.6907     |
| cosine_recall@3     | 0.8557     |
| cosine_recall@5     | 0.8969     |
| cosine_recall@10    | 0.9278     |
| cosine_ndcg@10      | 0.8132     |
| cosine_mrr@10       | 0.7759     |
| **cosine_map@100**  | **0.7795** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.5979     |
| cosine_accuracy@3   | 0.7732     |
| cosine_accuracy@5   | 0.8247     |
| cosine_accuracy@10  | 0.8866     |
| cosine_precision@1  | 0.5979     |
| cosine_precision@3  | 0.2577     |
| cosine_precision@5  | 0.1649     |
| cosine_precision@10 | 0.0887     |
| cosine_recall@1     | 0.5979     |
| cosine_recall@3     | 0.7732     |
| cosine_recall@5     | 0.8247     |
| cosine_recall@10    | 0.8866     |
| cosine_ndcg@10      | 0.7462     |
| cosine_mrr@10       | 0.701      |
| **cosine_map@100**  | **0.7047** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.5155     |
| cosine_accuracy@3   | 0.6907     |
| cosine_accuracy@5   | 0.7113     |
| cosine_accuracy@10  | 0.7732     |
| cosine_precision@1  | 0.5155     |
| cosine_precision@3  | 0.2302     |
| cosine_precision@5  | 0.1423     |
| cosine_precision@10 | 0.0773     |
| cosine_recall@1     | 0.5155     |
| cosine_recall@3     | 0.6907     |
| cosine_recall@5     | 0.7113     |
| cosine_recall@10    | 0.7732     |
| cosine_ndcg@10      | 0.6471     |
| cosine_mrr@10       | 0.6064     |
| **cosine_map@100**  | **0.6137** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 7,872 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: 18 tokens</li><li>mean: 206.12 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.62 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>on both in terms of material and territorial scope. ### 1.1 Material Scope The Spanish data protection law affords blanket protection for all data that may have been collected on a data subject. There are only a handful of exceptions that include: Information subject to a pending legal case Information collected concerning the investigation of terrorism or organised crime Information classified as "Confidential" for matters related to Spain's national security ### 1.2 Territorial Scope The Spanish data protection law applies to all data handlers that are: Carrying out data collection activities in Spain Not established in Spain but carrying out data collection activities on Spanish territory Not established within the European Union but carrying out data collection activities on Spanish residents unless for data transit purposes only ## 2\. Obligations for Organizations Under Spanish Data Protection Law The Spanish data protection law and GDPR lay out specific obligations for all data handlers. These obligations ensure, . ### 2.3 Privacy Policy Requirements Spain's data protection law requires all data handlers to inform the data subject of the following in their privacy policy: The purpose of collecting the data and the recipients of the information The obligatory or voluntary nature of the reply to the questions put to them The consequences of obtaining the data or of refusing to provide them The possibility of exercising rights of access, rectification, erasure, portability, and objection The identity and address of the controller or their local Spanish representative ### 2.4 Security Requirements Article 9 of Spain's Data Protection Law is direct and explicit in stating the responsibility of the data handler is to take adequate measures to ensure the protection of any data collected. It mandates all data handlers to adopt technical and organisational measures necessary to ensure the security of the personal data and prevent their alteration, loss, and unauthorised processing or access. Additionally, collection of any</code> | <code>What are the requirements for organizations under the Spanish data protection law regarding privacy policies and security measures?</code> |
  | <code>before the point of collection of their personal information. ## Right to Erasure The right to erasure gives consumers the right to request deleting all their data stored by the organization. Organizations are supposed to comply within 45 days and must deliver a report to the consumer confirming the deletion of their information. ## Right to Opt-in for Minors Personal information containing minors' personal information cannot be sold by a business unless the minor (age of 13 to 16 years) or the Parent/Guardian (if the minor is aged below 13 years) opt-ins to allow this sale. Businesses can be held liable for the sale of minors' personal information if they either knew or wilfully disregarded the consumer's status as a minor and the minor or Parent/Guardian had not willingly opted in. ## Right to Continued Protection Even when consumers choose to allow a business to collect and sell their personal information, businesses' must sign written</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | <code>What are the conditions under which businesses can sell minors' personal information?</code>                                               |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

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

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

</details>

### Training Logs
| Epoch      | Step    | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0407     | 10      | 7.3941        | -                      | -                      | -                      | -                     | -                      |
| 0.0813     | 20      | 6.0968        | -                      | -                      | -                      | -                     | -                      |
| 0.1220     | 30      | 4.9439        | -                      | -                      | -                      | -                     | -                      |
| 0.1626     | 40      | 3.8622        | -                      | -                      | -                      | -                     | -                      |
| 0.2033     | 50      | 3.0938        | -                      | -                      | -                      | -                     | -                      |
| 0.2439     | 60      | 1.8775        | -                      | -                      | -                      | -                     | -                      |
| 0.2846     | 70      | 2.3808        | -                      | -                      | -                      | -                     | -                      |
| 0.3252     | 80      | 4.0718        | -                      | -                      | -                      | -                     | -                      |
| 0.3659     | 90      | 2.2182        | -                      | -                      | -                      | -                     | -                      |
| 0.4065     | 100     | 1.914         | -                      | -                      | -                      | -                     | -                      |
| 0.4472     | 110     | 1.5123        | -                      | -                      | -                      | -                     | -                      |
| 0.4878     | 120     | 1.7047        | -                      | -                      | -                      | -                     | -                      |
| 0.5285     | 130     | 2.9509        | -                      | -                      | -                      | -                     | -                      |
| 0.5691     | 140     | 1.0605        | -                      | -                      | -                      | -                     | -                      |
| 0.6098     | 150     | 1.762         | -                      | -                      | -                      | -                     | -                      |
| 0.6504     | 160     | 1.6545        | -                      | -                      | -                      | -                     | -                      |
| 0.6911     | 170     | 3.0971        | -                      | -                      | -                      | -                     | -                      |
| 0.7317     | 180     | 1.3791        | -                      | -                      | -                      | -                     | -                      |
| 0.7724     | 190     | 1.9717        | -                      | -                      | -                      | -                     | -                      |
| 0.8130     | 200     | 5.1309        | -                      | -                      | -                      | -                     | -                      |
| 0.8537     | 210     | 1.4047        | -                      | -                      | -                      | -                     | -                      |
| 0.8943     | 220     | 1.4391        | -                      | -                      | -                      | -                     | -                      |
| 0.9350     | 230     | 3.6443        | -                      | -                      | -                      | -                     | -                      |
| 0.9756     | 240     | 3.721         | -                      | -                      | -                      | -                     | -                      |
| 1.0122     | 249     | -             | 0.6625                 | 0.7330                 | 0.7497                 | 0.5784                | 0.7568                 |
| 1.0041     | 250     | 1.3171        | -                      | -                      | -                      | -                     | -                      |
| 1.0447     | 260     | 5.2603        | -                      | -                      | -                      | -                     | -                      |
| 1.0854     | 270     | 4.0513        | -                      | -                      | -                      | -                     | -                      |
| 1.1260     | 280     | 2.5508        | -                      | -                      | -                      | -                     | -                      |
| 1.1667     | 290     | 1.7385        | -                      | -                      | -                      | -                     | -                      |
| 1.2073     | 300     | 1.1692        | -                      | -                      | -                      | -                     | -                      |
| 1.2480     | 310     | 0.788         | -                      | -                      | -                      | -                     | -                      |
| 1.2886     | 320     | 1.2322        | -                      | -                      | -                      | -                     | -                      |
| 1.3293     | 330     | 3.3735        | -                      | -                      | -                      | -                     | -                      |
| 1.3699     | 340     | 1.2204        | -                      | -                      | -                      | -                     | -                      |
| 1.4106     | 350     | 0.8458        | -                      | -                      | -                      | -                     | -                      |
| 1.4512     | 360     | 0.7586        | -                      | -                      | -                      | -                     | -                      |
| 1.4919     | 370     | 0.8964        | -                      | -                      | -                      | -                     | -                      |
| 1.5325     | 380     | 1.9721        | -                      | -                      | -                      | -                     | -                      |
| 1.5732     | 390     | 0.5605        | -                      | -                      | -                      | -                     | -                      |
| 1.6138     | 400     | 0.9648        | -                      | -                      | -                      | -                     | -                      |
| 1.6545     | 410     | 1.0002        | -                      | -                      | -                      | -                     | -                      |
| 1.6951     | 420     | 2.138         | -                      | -                      | -                      | -                     | -                      |
| 1.7358     | 430     | 0.8221        | -                      | -                      | -                      | -                     | -                      |
| 1.7764     | 440     | 2.124         | -                      | -                      | -                      | -                     | -                      |
| 1.8171     | 450     | 2.7892        | -                      | -                      | -                      | -                     | -                      |
| 1.8577     | 460     | 0.9088        | -                      | -                      | -                      | -                     | -                      |
| 1.8984     | 470     | 0.9254        | -                      | -                      | -                      | -                     | -                      |
| 1.9390     | 480     | 3.1205        | -                      | -                      | -                      | -                     | -                      |
| 1.9797     | 490     | 3.014         | -                      | -                      | -                      | -                     | -                      |
| **1.9878** | **492** | **-**         | **0.7047**             | **0.7795**             | **0.7896**             | **0.6137**            | **0.7937**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->