This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5 on the csv dataset. It maps sentences & paragraphs to a 384-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-small-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- csv

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("jebish7/bge_MNSR")
# Run inference
sentences = [
    'How does ADGM ensure that FinTech Participants remain compliant with evolving regulatory standards, particularly in the context of new and developing technologies?',
    'The Guidance is applicable to the following Persons:\n(a)\tan applicant for a Financial Services Permission to carry on the Regulated Activity of Developing Financial Technology Services within the RegLab in or from ADGM; and/or\n(b)\ta FinTech Participant.',
    'DIGITAL SECURITIES – INTERMEDIARIES\nConventional Intermediaries – Digital Securities\nIntermediaries intending to operate solely, in the context of Digital Securities, as a broker or dealer for Clients (including the operation of an OTC broking or dealing desk) are not permitted to structure their broking / dealing service or platform in such a way that would have it be considered as operating a RIE or MTF.  The FSRA would consider features such as allowing for price discovery, displaying a public trading order book (accessible to any member of the public, regardless of whether they are Clients), and allowing trades to automatically be matched using an exchange-type matching engine as characteristic of a RIE or MTF, and not activities acceptable for an Digital Securities intermediary to undertake.\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

csv

Dataset: csv
Size: 29,545 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 19 tokens
mean: 34.47 tokens
max: 70 tokens
min: 23 tokens
mean: 113.88 tokens
max: 512 tokens

Samples:

anchor	positive
`In the case of a cross-border transaction involving jurisdictions with differing sanctions regimes, how should a Relevant Person prioritize and reconcile these requirements?`	`Sanctions. UNSC Sanctions and Sanctions issued or administered by the U.A.E., including Targeted Financial Sanctions, apply in the ADGM. Relevant Persons must comply with Targeted Financial Sanctions. Sanctions compliance is emphasised by specific obligations contained in the AML Rulebook requiring Relevant Persons to establish and maintain effective systems and controls to comply with applicable Sanctions, including in particular Targeted Financial Sanctions, as set out in Chapter ‎11.`
`How does the FSRA monitor and assess the deployment scalability of a FinTech proposal within the UAE and ADGM beyond the RegLab validity period?`	Evaluation Criteria. To qualify for authorisation under the RegLab framework, the applicant must demonstrate how it satisfies the following evaluation criteria:[object Object](a) the FinTech Proposal promotes FinTech innovation, in terms of the business application and deployment model of the technology.[object Object](b) the FinTech Proposal has the potential to:[object Object]i. promote significant growth, efficiency or competition in the financial sector;[object Object]ii. promote better risk management solutions and regulatory outcomes for the financial industry; or[object Object]iii. improve the choices and welfare of clients.[object Object](c) the FinTech Proposal is at a sufficiently advanced stage of development to mount a live test.[object Object](d) the FinTech Proposal can be deployed in the ADGM and the UAE on a broader scale or contribute to the development of ADGM as a financial centre, and, if so, how the applicant intends to do so on completion of the validity period.[object Object][object Object]
`How does the ADGM define "distinct risks" that arise from conducting business entirely in an NFTF manner compared to a mix of face-to-face and NFTF interactions, and what specific risk mitigation strategies should be employed in these scenarios?`	The risk assessment under Rule ‎6.2.1(c) should identify actions to mitigate risks associated with undertaking NFTF business generally, and the use of eKYC specifically. This is because distinct risks are often likely to arise where business is conducted entirely in an NFTF manner, compared to when the business relationship includes a mix of face-to-face and NFTF interactions. The assessment should make reference to risk mitigation measures recommended by the Regulator, a competent authority of the U.A.E., FATF, and other relevant bodies.[object Object][object Object]

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

csv

Dataset: csv
Size: 3,676 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 18 tokens
mean: 34.98 tokens
max: 63 tokens
min: 3 tokens
mean: 114.8 tokens
max: 512 tokens

Samples:

anchor	positive
`How should our firm approach the development and implementation of a risk management system that addresses the full spectrum of risks listed, including technology, compliance, and legal risks?`	Management of particular risks[object Object]Without prejudice to the generality of Rule ‎2.4(1, a Captive Insurer must develop, implement and maintain a risk management system to identify and address risks, including but not limited to:[object Object](a) reserving risk;[object Object](b) investment risk (including risks associated with the use of Derivatives);[object Object](c) underwriting risk;[object Object](d) market risk;[object Object](e) liquidity management risk;[object Object](f) credit quality risk;[object Object](g) fraud and other fiduciary risks;[object Object](h) compliance risk;[object Object](i) outsourcing risk; and[object Object](j) reinsurance risk. Reinsurance risk refers to risks associated with the Captive Insurer's use of reinsurance arrangements as Cedant.
`What measures could an Authorised Person take to ensure non-repudiation and accountability, so that individuals or systems processing information cannot deny their actions?`	[object Object]In establishing its systems and controls to address information security risks, an Authorised Person should have regard to:[object Object]a. confidentiality: information should be accessible only to Persons or systems with appropriate authority, which may require firewalls within a system, as well as entry restrictions;[object Object]b. the risk of loss or theft of customer data;[object Object]c. integrity: safeguarding the accuracy and completeness of information and its processing;[object Object]d. non repudiation and accountability: ensuring that the Person or system that processed the information cannot deny their actions; and[object Object]e. internal security: including premises security, staff vetting; access rights and portable media, staff internet and email access, encryption, safe disposal of customer data, and training and awareness.
`What authority does the Regulator have over the terms and conditions applied to the escrow account holding funds from a Prospectus Offer?`	`The Regulator may, during the Offer Period or such other longer period as specified, impose a requirement that the monies held by a Person making a Prospectus Offer or his agent pursuant to the Prospectus Offer or issuance are held in an escrow account for a specified period and on specified terms.`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 10
warmup_ratio: 0.1
load_best_model_at_end: True
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: 64
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_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: 10
max_steps: -1
lr_scheduler_type: linear
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: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
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
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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss
0.8658	200	1.6059	-
1.2684	293	-	0.4773
1.4632	400	0.8247	-
2.2684	586	-	0.4313
2.0606	600	0.7352	-
2.9264	800	1.0011	-
3.2684	879	-	0.4038
3.5238	1000	0.646	-
4.2684	1172	-	0.3926
4.1212	1200	0.6207	-
4.9870	1400	0.8652	-
5.2684	1465	-	0.3769
5.5844	1600	0.5708	-
6.2684	1758	-	0.3691
6.1818	1800	0.5588	-
7.0476	2000	0.7551	-
7.2684	2051	-	0.3608
7.6450	2200	0.5758	-
8.1212	2310	-	0.3561

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.14
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.4.0
Accelerate: 0.34.2
Datasets: 3.0.1
Tokenizers: 0.20.0

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",
}

	anchor	positive
type	string	string
details	min: 19 tokens mean: 34.47 tokens max: 70 tokens	min: 23 tokens mean: 113.88 tokens max: 512 tokens