SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a sentence-transformers model finetuned from 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
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Language: en
- License: apache-2.0
Model Sources
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("MugheesAwan11/bge-base-securiti-dataset-1-v17")
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)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.2887 |
cosine_accuracy@3 |
0.5361 |
cosine_accuracy@5 |
0.6804 |
cosine_accuracy@10 |
0.7835 |
cosine_precision@1 |
0.2887 |
cosine_precision@3 |
0.1787 |
cosine_precision@5 |
0.1361 |
cosine_precision@10 |
0.0784 |
cosine_recall@1 |
0.2887 |
cosine_recall@3 |
0.5361 |
cosine_recall@5 |
0.6804 |
cosine_recall@10 |
0.7835 |
cosine_ndcg@10 |
0.5259 |
cosine_mrr@10 |
0.4444 |
cosine_map@100 |
0.4516 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.299 |
cosine_accuracy@3 |
0.5567 |
cosine_accuracy@5 |
0.701 |
cosine_accuracy@10 |
0.7732 |
cosine_precision@1 |
0.299 |
cosine_precision@3 |
0.1856 |
cosine_precision@5 |
0.1402 |
cosine_precision@10 |
0.0773 |
cosine_recall@1 |
0.299 |
cosine_recall@3 |
0.5567 |
cosine_recall@5 |
0.701 |
cosine_recall@10 |
0.7732 |
cosine_ndcg@10 |
0.5285 |
cosine_mrr@10 |
0.4505 |
cosine_map@100 |
0.4581 |
Information Retrieval
Metric |
Value |
cosine_accuracy@1 |
0.2784 |
cosine_accuracy@3 |
0.5361 |
cosine_accuracy@5 |
0.6701 |
cosine_accuracy@10 |
0.7629 |
cosine_precision@1 |
0.2784 |
cosine_precision@3 |
0.1787 |
cosine_precision@5 |
0.134 |
cosine_precision@10 |
0.0763 |
cosine_recall@1 |
0.2784 |
cosine_recall@3 |
0.5361 |
cosine_recall@5 |
0.6701 |
cosine_recall@10 |
0.7629 |
cosine_ndcg@10 |
0.5088 |
cosine_mrr@10 |
0.4285 |
cosine_map@100 |
0.4347 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 1,872 training samples
- Columns:
positive
and anchor
- Approximate statistics based on the first 1000 samples:
|
positive |
anchor |
type |
string |
string |
details |
- min: 4 tokens
- mean: 207.32 tokens
- max: 414 tokens
|
- min: 2 tokens
- mean: 21.79 tokens
- max: 102 tokens
|
- Samples:
positive |
anchor |
Automation PrivacyCenter.Cloud |
Data Mapping |
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 |
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? |
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 |
What are the data subjects' rights under GDPR regarding behavior monitoring, and how do they compare to the rights under PDPL? |
- Loss:
MatryoshkaLoss
with these parameters:{
"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
: 2
lr_scheduler_type
: cosine
warmup_ratio
: 0.1
bf16
: True
tf32
: True
load_best_model_at_end
: True
optim
: adamw_torch_fused
batch_sampler
: no_duplicates
All Hyperparameters
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
: 2
max_steps
: -1
lr_scheduler_type
: cosine
lr_scheduler_kwargs
: {}
warmup_ratio
: 0.1
warmup_steps
: 0
log_level
: passive
log_level_replica
: warning
log_on_each_node
: True
logging_nan_inf_filter
: True
save_safetensors
: True
save_on_each_node
: False
save_only_model
: False
restore_callback_states_from_checkpoint
: False
no_cuda
: False
use_cpu
: False
use_mps_device
: False
seed
: 42
data_seed
: None
jit_mode_eval
: False
use_ipex
: False
bf16
: True
fp16
: False
fp16_opt_level
: O1
half_precision_backend
: auto
bf16_full_eval
: False
fp16_full_eval
: False
tf32
: True
local_rank
: 0
ddp_backend
: None
tpu_num_cores
: None
tpu_metrics_debug
: False
debug
: []
dataloader_drop_last
: False
dataloader_num_workers
: 0
dataloader_prefetch_factor
: None
past_index
: -1
disable_tqdm
: False
remove_unused_columns
: True
label_names
: None
load_best_model_at_end
: True
ignore_data_skip
: False
fsdp
: []
fsdp_min_num_params
: 0
fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap
: None
accelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed
: None
label_smoothing_factor
: 0.0
optim
: adamw_torch_fused
optim_args
: None
adafactor
: False
group_by_length
: False
length_column_name
: length
ddp_find_unused_parameters
: None
ddp_bucket_cap_mb
: None
ddp_broadcast_buffers
: False
dataloader_pin_memory
: True
dataloader_persistent_workers
: False
skip_memory_metrics
: True
use_legacy_prediction_loop
: False
push_to_hub
: False
resume_from_checkpoint
: None
hub_model_id
: None
hub_strategy
: every_save
hub_private_repo
: False
hub_always_push
: False
gradient_checkpointing
: False
gradient_checkpointing_kwargs
: None
include_inputs_for_metrics
: False
eval_do_concat_batches
: True
fp16_backend
: auto
push_to_hub_model_id
: None
push_to_hub_organization
: None
mp_parameters
:
auto_find_batch_size
: False
full_determinism
: False
torchdynamo
: None
ray_scope
: last
ddp_timeout
: 1800
torch_compile
: False
torch_compile_backend
: None
torch_compile_mode
: None
dispatch_batches
: None
split_batches
: None
include_tokens_per_second
: False
include_num_input_tokens_seen
: False
neftune_noise_alpha
: None
optim_target_modules
: None
batch_eval_metrics
: False
batch_sampler
: no_duplicates
multi_dataset_batch_sampler
: proportional
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 |
- 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
@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
@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
@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}
}