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bge-base-securiti-dataset-1-v18

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1872
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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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:1872
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The Secretary of Health and Human.pathname_key_services may issue
an Emergency Use Authorization (EUA) to authorize unapproved medical products,
or unapproved uses of approved medical products, to be manufactured, marketed,
and sold in the context of an actual or potential emergency designated by the
government.
sentences:
- What was the aggregate intrinsic value of exercised stock options as of December
30, 2023?
- What are some of the regulations related to data breach impact analysis and response?
- What does the Emergency Use Authorization (EUA) by the U.S. Secretary of Health
and Human Services allow?
- source_sentence: 'the Virginia Consumer Data Protection Act protect consumers? The
Virginia Consumer Data Protection Act protects consumers by prohibiting deceptive
and unfair trade practices, giving consumers the right to sue for damages, and
providing a mechanism for enforcement against businesses engaging in such practices.
## Join Our Newsletter Get all the latest information, law updates and more delivered
to your inbox ### Share Copy 54 ### More Stories that May Interest You View More
September 21, 2023 ## Navigating Generative AI Privacy Challenges & Safeguarding
Tips Introduction The emergence of Generative AI has ushered in a new era of innovation
in the ever-evolving technological landscape that pushes the boundaries of...
View More September 13, 2023 ## Kuwait''s DPPR Kuwait didn’t have any data protection
law until the Communication and Information Technology Regulatory Authority (CITRA)
introduced the Data Privacy Protection Regulation'
sentences:
- What is Securiti's mission and history regarding Italy's GDPR implementation and
compliance?
- Which states have enacted data privacy laws like the VCDPA?
- How does the Virginia Consumer Data Protection Act protect consumers and how is
this protection enforced?
- source_sentence: Data Flow Intelligence & Governance Prevent sensitive data sprawl
through real-time streaming platforms Learn more Data Consent Automation First
Party Consent | Third Party & Cookie Consent Learn more Data Security Posture
Management Secure sensitive data in hybrid multicloud and SaaS environments Learn
more Data Breach Impact Analysis & Response Analyze impact of a data breach and
coordinate response per global regulatory obligations Learn more Data Catalog
Automatically catalog datasets and enable users to find, understand, trust and
access data Learn more Data Lineage Track changes and transformations of data
throughout its lifecycle Data Controls Orchestrator View Data Command Center View
Sensitive Data Intelligence View Asset Discovery Data Discovery & Classification
Sensitive Data Catalog People Data Graph Learn more Privacy , Sensitive Data
Intelligence Discover & Classify Structured and Unstructured Data | People Data
Graph Learn more Data Flow Intelligence & Governance Prevent sensitive data sprawl
through real-time streaming platforms Learn more Data Consent Automation First
Party Consent | Third Party & Cookie Consent Learn more Data Security Posture
Management Secure sensitive data in hybrid multicloud and SaaS environments Learn
more Data Breach Impact Analysis & Response Analyze impact of a data breach and
coordinate response per global regulatory obligations Learn more Data Catalog
Automatically catalog datasets and enable users to find, understand, trust and
access data Learn more Data Lineage Track changes and transformations of data
throughout its lifecycle Data Controls Orchestrator View Data Command Center View
Sensitive Data Intelligence View
sentences:
- Why is it important to manage security of sensitive data in hybrid multicloud
and SaaS environments, prevent data sprawl, and analyze the impact of data breaches?
- What right does the consumer have regarding their personal data in terms of deletion?
- What is the legal basis for the LGPD in Brazil?
- source_sentence: its lifecycle Data Controls Orchestrator View Data Command Center
View Sensitive Data Intelligence View Asset Discovery Data Discovery & Classification
Sensitive Data Catalog People Data Graph Learn more Privacy Automate compliance
with global privacy regulations Data Mapping Automation View Data Subject Request
Automation View People Data Graph View Assessment Automation View Cookie Consent
View Universal Consent View Vendor Risk Assessment View Breach Management View
Privacy Policy Management View Privacy Center View Learn more Security Identify
data risk and enable protection & control Data Security Posture Management View
Data Access Intelligence & Governance View Data Risk Management View
sentences:
- What is ANPD's primary goal regarding LGPD and its rights and regulations?
- What options are there for joining the Securiti team and expanding knowledge in
data privacy, security, and governance?
- How does the Data Controls Orchestrator help automate compliance with global privacy
regulations?
- source_sentence: 'remediate the incident, promptly notify relevant individuals,
and report such data security incidents to the regulatory department(s). Thus,
you should have a robust security breach response mechanism in place. ## 7\. Cross
border data transfer and data localization requirements: Under DSL, Critical Information
Infrastructure Operators are required to store the important data in the territory
of China and cross-border transfer is regulated by the CSL. CIIOs need to conduct
a security assessment in accordance with the measures jointly defined by CAC and
the relevant departments under the State Council for the cross-border transfer
of important data for business necessity. For non Critical Information Infrastructure
operators, the important data cross-border transfer will be regulated by the measures
announced by the Cyberspace Administration of China (CAC) and other authorities.
However, those “measures” have still not yet been released. DSL also intends to
establish a data national security review and export control system to restrict
the cross-border transmission of data'
sentences:
- What are the requirements for storing important data in the territory of China
under DSL?
- How does behavioral targeting relate to the processing of personal data under
Bahrain PDPL?
- What is the margin of error generally estimated for worldwide Monthly Active People
(MAP)?
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.27835051546391754
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5463917525773195
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6494845360824743
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7835051546391752
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.27835051546391754
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18213058419243983
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12989690721649483
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07835051546391751
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.27835051546391754
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5463917525773195
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6494845360824743
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7835051546391752
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5204365648204007
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4373834069710358
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.44377152224424676
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.28865979381443296
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5463917525773195
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6597938144329897
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7731958762886598
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28865979381443296
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18213058419243983
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1319587628865979
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07731958762886597
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.28865979381443296
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5463917525773195
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6597938144329897
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7731958762886598
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5234913842554121
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4444403534609721
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.45150068207403454
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.26804123711340205
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4845360824742268
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6494845360824743
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7628865979381443
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26804123711340205
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16151202749140892
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12989690721649483
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07628865979381441
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.26804123711340205
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4845360824742268
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6494845360824743
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7628865979381443
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4964329019488686
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4132302405498282
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.41983416368750226
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](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-v18")
# Run inference
sentences = [
'remediate the incident, promptly notify relevant individuals, and report such data security incidents to the regulatory department(s). Thus, you should have a robust security breach response mechanism in place. ## 7\\. Cross border data transfer and data localization requirements: Under DSL, Critical Information Infrastructure Operators are required to store the important data in the territory of China and cross-border transfer is regulated by the CSL. CIIOs need to conduct a security assessment in accordance with the measures jointly defined by CAC and the relevant departments under the State Council for the cross-border transfer of important data for business necessity. For non Critical Information Infrastructure operators, the important data cross-border transfer will be regulated by the measures announced by the Cyberspace Administration of China (CAC) and other authorities. However, those “measures” have still not yet been released. DSL also intends to establish a data national security review and export control system to restrict the cross-border transmission of data',
'What are the requirements for storing important data in the territory of China under DSL?',
'What is the margin of error generally estimated for worldwide Monthly Active People (MAP)?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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 | 0.2784 |
| cosine_accuracy@3 | 0.5464 |
| cosine_accuracy@5 | 0.6495 |
| cosine_accuracy@10 | 0.7835 |
| cosine_precision@1 | 0.2784 |
| cosine_precision@3 | 0.1821 |
| cosine_precision@5 | 0.1299 |
| cosine_precision@10 | 0.0784 |
| cosine_recall@1 | 0.2784 |
| cosine_recall@3 | 0.5464 |
| cosine_recall@5 | 0.6495 |
| cosine_recall@10 | 0.7835 |
| cosine_ndcg@10 | 0.5204 |
| cosine_mrr@10 | 0.4374 |
| **cosine_map@100** | **0.4438** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<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 | 0.5464 |
| cosine_accuracy@5 | 0.6598 |
| cosine_accuracy@10 | 0.7732 |
| cosine_precision@1 | 0.2887 |
| cosine_precision@3 | 0.1821 |
| cosine_precision@5 | 0.132 |
| cosine_precision@10 | 0.0773 |
| cosine_recall@1 | 0.2887 |
| cosine_recall@3 | 0.5464 |
| cosine_recall@5 | 0.6598 |
| cosine_recall@10 | 0.7732 |
| cosine_ndcg@10 | 0.5235 |
| cosine_mrr@10 | 0.4444 |
| **cosine_map@100** | **0.4515** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.268 |
| cosine_accuracy@3 | 0.4845 |
| cosine_accuracy@5 | 0.6495 |
| cosine_accuracy@10 | 0.7629 |
| cosine_precision@1 | 0.268 |
| cosine_precision@3 | 0.1615 |
| cosine_precision@5 | 0.1299 |
| cosine_precision@10 | 0.0763 |
| cosine_recall@1 | 0.268 |
| cosine_recall@3 | 0.4845 |
| cosine_recall@5 | 0.6495 |
| cosine_recall@10 | 0.7629 |
| cosine_ndcg@10 | 0.4964 |
| cosine_mrr@10 | 0.4132 |
| **cosine_map@100** | **0.4198** |
<!--
## 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: 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`: 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 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|
| 0.1695 | 10 | 3.9813 | - | - | - |
| 0.3390 | 20 | 2.6276 | - | - | - |
| 0.5085 | 30 | 1.7029 | - | - | - |
| 0.6780 | 40 | 0.641 | - | - | - |
| 0.8475 | 50 | 0.391 | - | - | - |
| **1.0** | **59** | **-** | **0.4761** | **0.4928** | **0.4919** |
| 0.1695 | 10 | 1.362 | - | - | - |
| 0.3390 | 20 | 0.7574 | - | - | - |
| 0.5085 | 30 | 0.5287 | - | - | - |
| 0.6780 | 40 | 0.096 | - | - | - |
| 0.8475 | 50 | 0.0699 | - | - | - |
| **1.0** | **59** | **-** | **0.4483** | **0.4913** | **0.4925** |
| 1.0169 | 60 | 0.25 | - | - | - |
| 1.1864 | 70 | 1.043 | - | - | - |
| 1.3559 | 80 | 0.8176 | - | - | - |
| 1.5254 | 90 | 0.6276 | - | - | - |
| 1.6949 | 100 | 0.0992 | - | - | - |
| 1.8644 | 110 | 0.0993 | - | - | - |
| 2.0 | 118 | - | 0.4469 | 0.4785 | 0.4862 |
| 0.1695 | 10 | 1.0617 | - | - | - |
| 0.3390 | 20 | 0.7721 | - | - | - |
| 0.5085 | 30 | 0.6991 | - | - | - |
| 0.6780 | 40 | 0.095 | - | - | - |
| 0.8475 | 50 | 0.0695 | - | - | - |
| **1.0** | **59** | **-** | **0.4519** | **0.4786** | **0.4748** |
| 1.0169 | 60 | 0.1892 | - | - | - |
| 1.1864 | 70 | 0.7125 | - | - | - |
| 1.3559 | 80 | 0.5113 | - | - | - |
| 1.5254 | 90 | 0.437 | - | - | - |
| 1.6949 | 100 | 0.0432 | - | - | - |
| 1.8644 | 110 | 0.0471 | - | - | - |
| 2.0 | 118 | - | 0.4347 | 0.4581 | 0.4516 |
| 0.1695 | 10 | 0.7237 | - | - | - |
| 0.3390 | 20 | 0.5054 | - | - | - |
| 0.5085 | 30 | 0.4194 | - | - | - |
| 0.6780 | 40 | 0.0437 | - | - | - |
| 0.8475 | 50 | 0.0388 | - | - | - |
| **1.0** | **59** | **-** | **0.4582** | **0.4692** | **0.4748** |
| 1.0169 | 60 | 0.1513 | - | - | - |
| 1.1864 | 70 | 0.5249 | - | - | - |
| 1.3559 | 80 | 0.3878 | - | - | - |
| 1.5254 | 90 | 0.3353 | - | - | - |
| 1.6949 | 100 | 0.0223 | - | - | - |
| 1.8644 | 110 | 0.0248 | - | - | - |
| 2.0 | 118 | - | 0.4251 | 0.4460 | 0.4439 |
| 2.0339 | 120 | 0.1012 | - | - | - |
| 2.2034 | 130 | 0.3534 | - | - | - |
| 2.3729 | 140 | 0.2937 | - | - | - |
| 2.5424 | 150 | 0.1769 | - | - | - |
| 2.7119 | 160 | 0.0107 | - | - | - |
| 2.8814 | 170 | 0.0102 | - | - | - |
| 3.0 | 177 | - | 0.4245 | 0.4448 | 0.4488 |
| 3.0508 | 180 | 0.1054 | - | - | - |
| 3.2203 | 190 | 0.2246 | - | - | - |
| 3.3898 | 200 | 0.2323 | - | - | - |
| 3.5593 | 210 | 0.1045 | - | - | - |
| 3.7288 | 220 | 0.0082 | - | - | - |
| 3.8983 | 230 | 0.0123 | - | - | - |
| 4.0 | 236 | - | 0.4198 | 0.4515 | 0.4438 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```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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