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---
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:882
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Data Discovery & Classification Sensitive Data Catalog Sensitive
Data Catalog People Data Graph People Data Graph 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 Data Security
Posture Management View Data Access Intelligence & Governance View Data Risk Management
View Data Breach Analysis View Data Catalog View Data Lineage View Data Quality
View Asset and Data Discovery View Data Access Intelligence & Governance View
Data Privacy Automation View
sentences:
- How does coordinating a response in managing a data breach and meeting global
regulatory obligations help automate compliance with global privacy regulations?
- What law replaced Law No. 1682/2001 in Paraguay's data protection regulations
and what are the restrictions on publicizing sensitive data under it?
- What are the different components or tools related to Data Discovery & Classification?
- source_sentence: 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 Data Breach Analysis View Learn more Governance
Optimize Data Governance with granular insights into your data Data Catalog View
Data Lineage View Data Quality View Data Controls Orchestrator View Solutions
Technologies Covering you everywhere with 1000+ integrations across data systems.
Snowflake View AW, 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 Data Breach Analysis
View Learn more Governance Optimize Data Governance with granular insights into
your data Data Catalog View Data Lineage View Data Quality View Data Controls
Orchestrator View Solutions Technologies Covering you everywhere with 1000+ integrations
across data systems. Snowflake View AW
sentences:
- What can the data principal do if the data fiduciary disagrees with their request
for correction, completion, update, or erasure, and how does cross-border data
transfer factor in?
- What is the purpose of the Vendor Risk Assessment for data security and governance?
- How can privacy automation help in complying with global privacy regulations?
- source_sentence: 'of 2021 is the British Virgin Island’s main data protection law
on par with the EU and UK standards. Learn more ### Jamaica The Data Protection
Act No. 7 of 2020 is Jamaica’s data protection regulation, enforced by the Office
of the Information Commissioner. Learn more ### Ukraine The Law on Personal Data
Protection is Ukraine’s main data protection law, making it one of the few such
regulations that precede the GDPR in Europe. Learn more ### Uzbekistan Uzbekistan
has several regulations that govern different aspects of data protection within
the country. Learn more about : Law on Personal Data Bill to Improve the Legal
Framework for Personal Data Draft Law on Advertising Law on Cybersecurity (No.
RK 764) ### Monaco Act No. 1.165 on the Protection of Personal Data regulates
personal data protection-related matters in the Principality of Monaco'
sentences:
- What are the conditions for parental consent under PIPL and the requirements for
privacy notices?
- What does the Knowledge Center provide information on in relation to security?
- Which European country has a data protection law that predates the GDPR and is
enforced by the Information Commissioner's Office?
- source_sentence: Data Lineage View Data Quality View Asset and Data Discovery View
Data Access Intelligence & Governance View Data Privacy Automation View Sensitive
Data Intelligence View Data Flow Intelligence & Governance View Data Consent Automation
View Data Security Posture Management View Data Breach Impact Analysis & Response
View Data Catalog View Data Lineage View Solutions Technologies Regulations Roles
Back Snowflake View AWS View Microsoft 365 View Salesforce View Workday View GCP
View Azure View Oracle View US California CCPA View US California CPRA View
sentences:
- What is the role of data privacy automation in ensuring data protection and compliance?
- What risks does data security and the cloud help control for enterprises to safely
harness their power?
- What is the term for the right to delete personal data upon request, also known
as 'the right to be forgotten', and what are the other data protection rights
under GDPR?
- source_sentence: Consent of an individual is valid if it is reasonable to expect
that an individual to whom the organization’s activities are directed would understand
the nature, purpose, and consequences of the collection, use, or disclosure of
the personal information to which they are consenting. The information must be
provided in manageable and easily accessible ways to data subjects and data subjects
must be allowed to withdraw consent. If there is a use or disclosure a data subject
would not reasonably expect to be occurring, such as certain sharing of information
with a third party or the tracking of location, express consent would likely be
required. However, the data subject’s consent may not be required for certain
data processing activities such as when the collection is “clearly” in the interests
of the individual and consent cannot be obtained in a timely way, data is being
collected in the course of employment, journalistic, is already publicly available,
information is being collected for the detection and prevention of fraud or for
sentences:
- How should information be provided to data subjects in manageable and easily accessible
ways?
- What are the obligations and requirements for businesses under China's Personal
Information Protection Law?
- Which state, following California, Virginia, and Colorado, recently passed privacy
legislation like the VCDPA?
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: 0.4020618556701031
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5567010309278351
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6804123711340206
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7525773195876289
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4020618556701031
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1855670103092783
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1360824742268041
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07525773195876287
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4020618556701031
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5567010309278351
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6804123711340206
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7525773195876289
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5649836192344125
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5059687448862709
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5167362215588647
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.3917525773195876
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5876288659793815
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6288659793814433
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7525773195876289
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3917525773195876
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19587628865979378
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12577319587628866
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07525773195876287
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3917525773195876
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5876288659793815
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6288659793814433
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7525773195876289
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5625195371806965
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5031173294059894
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5141611082081141
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.38144329896907214
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5773195876288659
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6391752577319587
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.711340206185567
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38144329896907214
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1924398625429553
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12783505154639174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07113402061855668
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.38144329896907214
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5773195876288659
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6391752577319587
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.711340206185567
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5460935382949205
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.49311078383243345
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5067772343986099
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-v19")
# Run inference
sentences = [
'Consent of an individual is valid if it is reasonable to expect that an individual to whom the organization’s activities are directed would understand the nature, purpose, and consequences of the collection, use, or disclosure of the personal information to which they are consenting. The information must be provided in manageable and easily accessible ways to data subjects and data subjects must be allowed to withdraw consent. If there is a use or disclosure a data subject would not reasonably expect to be occurring, such as certain sharing of information with a third party or the tracking of location, express consent would likely be required. However, the data subject’s consent may not be required for certain data processing activities such as when the collection is “clearly” in the interests of the individual and consent cannot be obtained in a timely way, data is being collected in the course of employment, journalistic, is already publicly available, information is being collected for the detection and prevention of fraud or for',
'How should information be provided to data subjects in manageable and easily accessible ways?',
'Which state, following California, Virginia, and Colorado, recently passed privacy legislation like the VCDPA?',
]
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 | 0.4021 |
| cosine_accuracy@3 | 0.5567 |
| cosine_accuracy@5 | 0.6804 |
| cosine_accuracy@10 | 0.7526 |
| cosine_precision@1 | 0.4021 |
| cosine_precision@3 | 0.1856 |
| cosine_precision@5 | 0.1361 |
| cosine_precision@10 | 0.0753 |
| cosine_recall@1 | 0.4021 |
| cosine_recall@3 | 0.5567 |
| cosine_recall@5 | 0.6804 |
| cosine_recall@10 | 0.7526 |
| cosine_ndcg@10 | 0.565 |
| cosine_mrr@10 | 0.506 |
| **cosine_map@100** | **0.5167** |
#### 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 | 0.3918 |
| cosine_accuracy@3 | 0.5876 |
| cosine_accuracy@5 | 0.6289 |
| cosine_accuracy@10 | 0.7526 |
| cosine_precision@1 | 0.3918 |
| cosine_precision@3 | 0.1959 |
| cosine_precision@5 | 0.1258 |
| cosine_precision@10 | 0.0753 |
| cosine_recall@1 | 0.3918 |
| cosine_recall@3 | 0.5876 |
| cosine_recall@5 | 0.6289 |
| cosine_recall@10 | 0.7526 |
| cosine_ndcg@10 | 0.5625 |
| cosine_mrr@10 | 0.5031 |
| **cosine_map@100** | **0.5142** |
#### 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 | 0.3814 |
| cosine_accuracy@3 | 0.5773 |
| cosine_accuracy@5 | 0.6392 |
| cosine_accuracy@10 | 0.7113 |
| cosine_precision@1 | 0.3814 |
| cosine_precision@3 | 0.1924 |
| cosine_precision@5 | 0.1278 |
| cosine_precision@10 | 0.0711 |
| cosine_recall@1 | 0.3814 |
| cosine_recall@3 | 0.5773 |
| cosine_recall@5 | 0.6392 |
| cosine_recall@10 | 0.7113 |
| cosine_ndcg@10 | 0.5461 |
| cosine_mrr@10 | 0.4931 |
| **cosine_map@100** | **0.5068** |
<!--
## 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: 882 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: 227.32 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.98 tokens</li><li>max: 102 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|
| <code>Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset and Data Discovery Discover dark and native data assets Learn more Data Access Intelligence & Governance Identify which users have access to sensitive data and prevent unauthorized access Learn more Data Privacy 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</code> | <code>What is the purpose of the Data Command Center?</code> |
| <code>data subject must be notified of any such extension within one month of receiving the request, along with the reasons for the delay and the possibility of complaining to the supervisory authority. The right to restrict processing applies when the data subject contests data accuracy, the processing is unlawful, and the data subject opposes erasure and requests restriction. The controller must inform data subjects before any such restriction is lifted. Under GDPR, the data subject also has the right to obtain from the controller the rectification of inaccurate personal data and to have incomplete personal data completed. Article: 22 Under PDPL, if a decision is based solely on automated processing of personal data intended to assess the data subject regarding his/her performance at work, financial standing, credit-worthiness, reliability, or conduct, then the data subject has the right to request processing in a manner that is not solely automated. This right shall not apply where the decision is taken in the course of entering into</code> | <code>What is the requirement for notifying the data subject of any extension under GDPR and PDPL?</code> |
| <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> |
* 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`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|
| 0.3571 | 10 | 4.0517 | - | - | - |
| 0.7143 | 20 | 2.5778 | - | - | - |
| 1.0 | 28 | - | 0.5304 | 0.5224 | 0.5234 |
| 1.0714 | 30 | 2.1161 | - | - | - |
| 1.4286 | 40 | 1.5394 | - | - | - |
| 1.7857 | 50 | 1.5162 | - | - | - |
| **2.0** | **56** | **-** | **0.5412** | **0.5382** | **0.5185** |
| 2.1429 | 60 | 1.202 | - | - | - |
| 2.5 | 70 | 1.0456 | - | - | - |
| 2.8571 | 80 | 1.1341 | - | - | - |
| 3.0 | 84 | - | 0.5340 | 0.5554 | 0.5498 |
| 3.2143 | 90 | 0.8724 | - | - | - |
| 3.5714 | 100 | 0.932 | - | - | - |
| 3.9286 | 110 | 0.9548 | - | - | - |
| 4.0 | 112 | - | 0.5296 | 0.5487 | 0.5491 |
| 0.3571 | 10 | 0.9958 | - | - | - |
| 0.7143 | 20 | 0.8264 | - | - | - |
| 1.0 | 28 | - | 0.5155 | 0.5250 | 0.5269 |
| 1.0714 | 30 | 0.7969 | - | - | - |
| 1.4286 | 40 | 0.6244 | - | - | - |
| 1.7857 | 50 | 0.6368 | - | - | - |
| **2.0** | **56** | **-** | **0.5034** | **0.5314** | **0.5233** |
| 2.1429 | 60 | 0.4748 | - | - | - |
| 2.5 | 70 | 0.4037 | - | - | - |
| 2.8571 | 80 | 0.4615 | - | - | - |
| 3.0 | 84 | - | 0.5079 | 0.5145 | 0.5155 |
| 3.2143 | 90 | 0.3148 | - | - | - |
| 3.5714 | 100 | 0.4142 | - | - | - |
| 3.9286 | 110 | 0.366 | - | - | - |
| 4.0 | 112 | - | 0.5068 | 0.5142 | 0.5167 |
* 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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