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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:1496
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: We are currently involved in, and may in the future be involved
in, legal proceedings, claims, and government investigations in the ordinary course
of business. These include proceedings, claims, and investigations relating to,
among other things, regulatory matters, commercial matters, intellectual property,
competition, tax, employment, pricing, discrimination, consumer rights, personal
injury, and property rights.
sentences:
- What factors does the regulatory authority consider when ensuring data protection
in cross border transfers in Zimbabwe?
- How does Securiti enable enterprises to safely use data and the cloud while managing
security, privacy, and compliance risks?
- What types of legal issues is the company currently involved in?
- source_sentence: The Company’s minority market share in the global smartphone, personal
computer and tablet markets can make developers less inclined to develop or upgrade
software for the Company’s products and more inclined to devote their resources
to developing and upgrading software for competitors’ products with larger market
share. When developers focus their efforts on these competing platforms, the availability
and quality of applications for the Company’s devices can suffer.
sentences:
- What is the role of obtaining consent in Thailand's PDPA?
- Why might developers be less inclined to develop or upgrade software for the Company's
products?
- What caused the increase in energy generation and storage segment revenue in 2023?
- source_sentence: '** : EMEA (Europe, the Middle East and Africa) The Irish DPA implements
the GDPR into the national law by incorporating most of the provisions of the
GDPR with limited additions and deletions. It contains several provisions restricting
data subjects’ rights that they generally have under the GDPR, for example, where
restrictions are necessary for the enforcement of civil law claims. Resources*
: Irish DPA Overview Irish Cookie Guidance ### Japan #### Japan’s Act on the Protection
of Personal Information (APPI) **Effective Date (Amended APPI)** : April 01, 2022
**Region** : APAC (Asia-Pacific) Japan’s APPI regulates personal related information
and applies to any Personal Information Controller (the “PIC''''), that is a person
or entity providing personal related information for use in business in Japan.
The APPI also applies to the foreign'
sentences:
- What are the requirements for CIIOs and personal information processors in the
state cybersecurity department regarding cross-border data transfers and certifications?
- How does the Irish DPA implement the GDPR into national law?
- What is the current status of the Personal Data Protection Act in El Salvador
compared to Monaco and Venezuela?
- source_sentence: View Salesforce View Workday View GCP View Azure View Oracle View
US California CCPA View US California CPRA View European Union GDPR View Thailand’s
PDPA View China PIPL View Canada PIPEDA View Brazil's LGPD View \+ More View Privacy
View Security View Governance View Marketing View Resources Blog View Collateral
View Knowledge Center View Securiti Education View Company About Us View Partner
Program View Contact Us View News Coverage
sentences:
- What is the role of ANPD in ensuring LGPD compliance and protecting data subject
rights, including those related to health professionals?
- According to the Spanish data protection law, who is required to hire a DPO if
they possess certain information in the event of a data breach?
- What is GCP and how does it relate to privacy, security, governance, marketing,
and resources?
- source_sentence: 'vital interests of the data subject; Complying with an obligation
prescribed in PDPL, not being a contractual obligation, or complying with an order
from a competent court, the Public Prosecution, the investigation Judge, or the
Military Prosecution; or Preparing or pursuing a legal claim or defense. vs Articles:
44 50, Recitals: 101, 112 GDPR states that personal data shall be transferred
to a third country or international organization with an adequate protection level
as determined by the EU Commission. Suppose there is no decision on an adequate
protection level. In that case, a transfer is only permitted when the data controller
or data processor provides appropriate safeguards that ensure data subject rights.
Appropriate safeguards include: BCRs with specific requirements (e.g., a legal
basis for processing, a retention period, and complaint procedures) Standard data
protection clauses adopted by the EU Commission, level of protection. If there
is no adequate level of protection, then data controllers in Turkey and abroad
shall commit, in writing, to provide an adequate level of protection abroad, as
well as agree on the fact that the transfer is permitted by the Board of KVKK.
vs Articles 44 50 Recitals 101, 112 GDPR states that personal data shall be transferred
to a third country or international organization with an adequate protection level
as determined by the EU Commission. Suppose there is no decision on an adequate
protection level. In that case, a transfer is only permitted when the data controller
or data processor provides appropriate safeguards that ensure data subject'' rights.
Appropriate safeguards include: BCRs with specific requirements (e.g., a legal
basis for processing, a retention period, and complaint procedures); standard
data protection clauses adopted by the EU Commission or by a supervisory authority;
an approved code'
sentences:
- What is the right to be informed in relation to personal data?
- In what situations can a controller process personal data to protect vital interests?
- What obligations in PDPL must data controllers or processors meet to protect personal
data transferred to a third country or international organization?
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: 0.5773195876288659
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6804123711340206
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7938144329896907
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4020618556701031
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1924398625429553
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1360824742268041
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07938144329896907
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4020618556701031
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5773195876288659
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6804123711340206
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7938144329896907
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5821623921468868
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5161471117656685
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5239473985229559
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.41237113402061853
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5670103092783505
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6597938144329897
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7835051546391752
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.41237113402061853
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18900343642611683
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1319587628865979
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07835051546391752
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.41237113402061853
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5670103092783505
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6597938144329897
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7835051546391752
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5830365443881826
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5208312878415973
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5295727941555394
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.4020618556701031
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6185567010309279
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: 0.4020618556701031
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20618556701030924
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: 0.4020618556701031
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6185567010309279
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: 0.576352896876016
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5177957781050565
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.527827441661229
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-v22")
# Run inference
sentences = [
"vital interests of the data subject; Complying with an obligation prescribed in PDPL, not being a contractual obligation, or complying with an order from a competent court, the Public Prosecution, the investigation Judge, or the Military Prosecution; or Preparing or pursuing a legal claim or defense. vs Articles: 44 50, Recitals: 101, 112 GDPR states that personal data shall be transferred to a third country or international organization with an adequate protection level as determined by the EU Commission. Suppose there is no decision on an adequate protection level. In that case, a transfer is only permitted when the data controller or data processor provides appropriate safeguards that ensure data subject rights. Appropriate safeguards include: BCRs with specific requirements (e.g., a legal basis for processing, a retention period, and complaint procedures) Standard data protection clauses adopted by the EU Commission, level of protection. If there is no adequate level of protection, then data controllers in Turkey and abroad shall commit, in writing, to provide an adequate level of protection abroad, as well as agree on the fact that the transfer is permitted by the Board of KVKK. vs Articles 44 50 Recitals 101, 112 GDPR states that personal data shall be transferred to a third country or international organization with an adequate protection level as determined by the EU Commission. Suppose there is no decision on an adequate protection level. In that case, a transfer is only permitted when the data controller or data processor provides appropriate safeguards that ensure data subject' rights. Appropriate safeguards include: BCRs with specific requirements (e.g., a legal basis for processing, a retention period, and complaint procedures); standard data protection clauses adopted by the EU Commission or by a supervisory authority; an approved code",
'What obligations in PDPL must data controllers or processors meet to protect personal data transferred to a third country or international organization?',
'In what situations can a controller process personal data to protect vital interests?',
]
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 | 0.5773 |
| cosine_accuracy@5 | 0.6804 |
| cosine_accuracy@10 | 0.7938 |
| cosine_precision@1 | 0.4021 |
| cosine_precision@3 | 0.1924 |
| cosine_precision@5 | 0.1361 |
| cosine_precision@10 | 0.0794 |
| cosine_recall@1 | 0.4021 |
| cosine_recall@3 | 0.5773 |
| cosine_recall@5 | 0.6804 |
| cosine_recall@10 | 0.7938 |
| cosine_ndcg@10 | 0.5822 |
| cosine_mrr@10 | 0.5161 |
| **cosine_map@100** | **0.5239** |
#### 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 | 0.4124 |
| cosine_accuracy@3 | 0.567 |
| cosine_accuracy@5 | 0.6598 |
| cosine_accuracy@10 | 0.7835 |
| cosine_precision@1 | 0.4124 |
| cosine_precision@3 | 0.189 |
| cosine_precision@5 | 0.132 |
| cosine_precision@10 | 0.0784 |
| cosine_recall@1 | 0.4124 |
| cosine_recall@3 | 0.567 |
| cosine_recall@5 | 0.6598 |
| cosine_recall@10 | 0.7835 |
| cosine_ndcg@10 | 0.583 |
| cosine_mrr@10 | 0.5208 |
| **cosine_map@100** | **0.5296** |
#### 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 | 0.4021 |
| cosine_accuracy@3 | 0.6186 |
| cosine_accuracy@5 | 0.6495 |
| cosine_accuracy@10 | 0.7629 |
| cosine_precision@1 | 0.4021 |
| cosine_precision@3 | 0.2062 |
| cosine_precision@5 | 0.1299 |
| cosine_precision@10 | 0.0763 |
| cosine_recall@1 | 0.4021 |
| cosine_recall@3 | 0.6186 |
| cosine_recall@5 | 0.6495 |
| cosine_recall@10 | 0.7629 |
| cosine_ndcg@10 | 0.5764 |
| cosine_mrr@10 | 0.5178 |
| **cosine_map@100** | **0.5278** |
<!--
## 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: 1,496 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: 67 tokens</li><li>mean: 216.99 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.6 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`: 1
- `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`: 1
- `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 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|
| 0.2128 | 10 | 3.8486 | - | - | - |
| 0.4255 | 20 | 2.3611 | - | - | - |
| 0.6383 | 30 | 2.3209 | - | - | - |
| 0.8511 | 40 | 1.3248 | - | - | - |
| **1.0** | **47** | **-** | **0.5278** | **0.5296** | **0.5239** |
* 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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