---
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:494
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Program Join our Partner Program Contact Us Contact us to learn
more or schedule a demo News Coverage Read about Securiti in the news Press Releases
Find our latest press releases Careers Join the talented Securiti team Knowledge
Center » Data Privacy Automation # New Zealand''s Privacy Act of 2020 By Securiti
Research Team Published March 7, 2022 / Updated August 11, 2023 New Zealand was
one of the first countries that enacted a law specifically dedicated to its residents''
right to privacy with its Privacy Act of 1993. Whilst the entire definition of
what "privacy" means has undergone a radical shift since then New Zealand’s principles
based legislation has remained relatively fit for purpose. Even with the advent
of social media and the internet adding an entirely new paradigm to that topic.
In recognition of the evolution of privacy, New Zealand updated its'
sentences:
- Where can I find Securiti's latest press releases?
- What are the requirements for data transfer under Spain's data protection law,
including certifications and information for data subjects?
- 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: 'that the third party: has appropriate policies and processes in
place; has trained its staff to ensure information is appropriately safeguarded
at all times; has adequate security measures in place. Simultaneously, the Cross-border
Guidelines also specify that organizations must provide notice to customers that:
their personal information may be sent to another jurisdiction for processing;
while the information is in the other jurisdiction, it may be accessed by the
courts, law enforcement, and national security authorities. ## 10\. Data Subject
Rights PIPEDA bestows the following rights to data subjects: Right to access Right
to accuracy and completeness Right to withdraw consent and submit complaints ##
11\. Penalties for PIPEDA Non-Compliance PIPEDA imposes administrative penalties
for non-compliance, where the amount may vary depending upon the severity and
the kind of violation. According to PIPEDA, : organizations must keep personal
information accurate. 7. **Safeguards** : organizations must protect personal
information against loss or theft. 8. **Openness** : privacy policy and practices
must be understandable and easily available. 9. **Individual access** : data subjects
have a right to access the personal information an organization holds about them.
10. **Resource** : organizations must develop accessible complaint procedures.
## 3\. Obligations for the Data Controller and Data Processor PIPEDA does not
differentiate between data controllers and data processors and provides a similar
set of responsibilities for both controllers and processors. PIPEDA demands all
organizations appoint individuals who will be accountable for ensuring streamlined
compliance of an organization’s data activities in accordance with the provisions
of PIPEDA. ## 4\. Consent Requirements In many circumstances, PIPEDA requires
organizations to obtain the data subject’s consent to use, disclose, and retain
any personal information.'
sentences:
- What are the key provisions of South Korea's data privacy law?
- What are the circumstances in which the data subject must be notified about the
collection of personal data?
- How does PIPEDA ensure staff's compliance with guidelines and obligations regarding
information protection?
- source_sentence: 'The criteria used The purpose of processing This information must
be provided within 15 days from the date of the data subject’s request. vs GDPR
states that, when responding to an access request, a data controller must indicate
the following: The categories of personal data concerned The recipients or categories
of recipients to whom personal data have been disclosed to The retention period
The right to lodge a complaint with the supervisory authority The existence of
data transfers The existence of automated decision making The information must
be provided without undue delay and in any event within one month of the receipt
of the request. LGPD grants the right to data portability through an express request
and subject to commercial and industrial secrecy, pursuant to the regulation of
the controlling agency. This right, however, does not include data that has already
been anonymised by the controller. vs GDPR defines the right to'
sentences:
- What is considered an offense related to obstructing the OPC in an investigation?
- What does LGPD grant the right to in terms of data portability?
- How does automation aid in complying with data privacy regulations like the PDPO?
- source_sentence: 'uriti Research Team Published December 3, 2020 / Updated October
3, 2023 On 1 December 2020, New Zealand’s new Privacy Act 2020 came into effect.
Our experts at Securiti have compiled the following list of compliance actions
to remind organizations of their obligations under New Zealand’s new Privacy Act.
## 1\. Notify privacy breaches within 72 hours Organizations must notify privacy
breach that has caused serious harm to the affected individual or is likely to
do so, to the Privacy Commissioner and the affected individuals as soon as practicable
or within 72 hours after becoming aware of the breach. Where it is not reasonably
practicable to notify the affected individual or each member of a group of affected
individuals, organizations must notify the public in a manner that no individual
is identified. Companies that fail to notify privacy breaches without any reasonable
excuse would be liable on conviction to a fine not exceeding $10,000. ## 2\. Notify
privacy breaches caused by any'
sentences:
- When are controllers and data processors required to appoint a DPO according to
the PDP Law and state regulations in Indonesia?
- What is the time frame for notifying privacy breaches under New Zealand's new
Privacy Act?
- What rights do Colorado residents have over their personal data under the Colorado
Privacy Act?
- source_sentence: Careers View Events Spotlight Talks IDC Names Securiti a Worldwide
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-, Press
Releases View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide
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
sentences:
- What is the purpose of the Data Command Center?
- What are IBM's future prospects and preparedness for new business opportunities?
- What is the US California CCPA?
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: 0.34845360824742266
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5855670103092784
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6701030927835051
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.756701030927835
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34845360824742266
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1951890034364261
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13402061855670103
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0756701030927835
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.34845360824742266
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5855670103092784
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6701030927835051
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.756701030927835
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5507373799577976
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4849337260677468
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4942402452655515
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.3463917525773196
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5938144329896907
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.668041237113402
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.756701030927835
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3463917525773196
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1979381443298969
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13360824742268038
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07567010309278348
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3463917525773196
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5938144329896907
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.668041237113402
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.756701030927835
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5517739147624575
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.48604565537555244
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4956303541940711
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.3422680412371134
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5670103092783505
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6618556701030928
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7484536082474227
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3422680412371134
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1890034364261168
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.13237113402061854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07484536082474226
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3422680412371134
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5670103092783505
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6618556701030928
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7484536082474227
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5412682955861301
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.475321551300933
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.48455040697749474
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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **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-v20")
# Run inference
sentences = [
'Careers View Events Spotlight Talks IDC Names Securiti a Worldwide 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-, Press Releases View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide 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',
'What is the purpose of the Data Command Center?',
"What are IBM's future prospects and preparedness for new business opportunities?",
]
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]
```
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3485 |
| cosine_accuracy@3 | 0.5856 |
| cosine_accuracy@5 | 0.6701 |
| cosine_accuracy@10 | 0.7567 |
| cosine_precision@1 | 0.3485 |
| cosine_precision@3 | 0.1952 |
| cosine_precision@5 | 0.134 |
| cosine_precision@10 | 0.0757 |
| cosine_recall@1 | 0.3485 |
| cosine_recall@3 | 0.5856 |
| cosine_recall@5 | 0.6701 |
| cosine_recall@10 | 0.7567 |
| cosine_ndcg@10 | 0.5507 |
| cosine_mrr@10 | 0.4849 |
| **cosine_map@100** | **0.4942** |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3464 |
| cosine_accuracy@3 | 0.5938 |
| cosine_accuracy@5 | 0.668 |
| cosine_accuracy@10 | 0.7567 |
| cosine_precision@1 | 0.3464 |
| cosine_precision@3 | 0.1979 |
| cosine_precision@5 | 0.1336 |
| cosine_precision@10 | 0.0757 |
| cosine_recall@1 | 0.3464 |
| cosine_recall@3 | 0.5938 |
| cosine_recall@5 | 0.668 |
| cosine_recall@10 | 0.7567 |
| cosine_ndcg@10 | 0.5518 |
| cosine_mrr@10 | 0.486 |
| **cosine_map@100** | **0.4956** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3423 |
| cosine_accuracy@3 | 0.567 |
| cosine_accuracy@5 | 0.6619 |
| cosine_accuracy@10 | 0.7485 |
| cosine_precision@1 | 0.3423 |
| cosine_precision@3 | 0.189 |
| cosine_precision@5 | 0.1324 |
| cosine_precision@10 | 0.0748 |
| cosine_recall@1 | 0.3423 |
| cosine_recall@3 | 0.567 |
| cosine_recall@5 | 0.6619 |
| cosine_recall@10 | 0.7485 |
| cosine_ndcg@10 | 0.5413 |
| cosine_mrr@10 | 0.4753 |
| **cosine_map@100** | **0.4846** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 494 training samples
* Columns: positive and anchor
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 18 tokens
- mean: 223.56 tokens
- max: 414 tokens
| - min: 10 tokens
- mean: 21.87 tokens
- max: 102 tokens
|
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------|
| ### Denmark #### Denmark **Effective Date** : May 25, 2018 **Region** : EMEA (Europe, Middle East, Africa) Similar to other EU countries, Denmark has enacted a data protection act for the purpose of implementing the GDPR in the country. The Danish Data Protection Act (Act No. 502 of 23 May 2018) was enacted for the protection of natural persons with respect to personal data processing and to regulate the free movement of personal data. The Act replaced the previous Danish Act on Processing of Personal Data (Act no. 429 of 31/05/2000). Under the new Act, the Danish Data Protection Authority (Datatilsynet) oversees all aspects related to the supervision and enforcement of the Data Protection Act and the GDPR within the country as well as representing Denmark in the European Data Protection Board. ### Finland #### Finland **Effective Date** : January 1, 2019 **Region** : EMEA (Europe | What is the role of the Danish Data Protection Authority in Denmark's implementation of the GDPR? |
| CPRA compliance involves adhering to the requirements outlined in the California Privacy Rights Act (CPRA) to protect consumer privacy and data rights. ## Join Our Newsletter Get all the latest information, law updates and more delivered to your inbox ### Share Copy 91 ### More Stories that May Interest You 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 (DPPR). The... View More September 11, 2023 ## Indonesia’s Protection of Personal Data Law: Explained In January 2020, Indonesia joined the burgeoning list of countries with their own data protection regulations. Provisions for data protection had existed within various... View More August 31, 2023 ## | Why is it important to comply with CPRA requirements and how does it protect data rights? |
| 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 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, 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 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 | What is the role of Vendor Assessment in securing and protecting sensitive data in Data Access Intelligence & Governance? |
* Loss: [MatryoshkaLoss](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
Click to expand
- `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