---
base_model: BAAI/bge-small-en-v1.5
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:29545
- loss:MultipleNegativesSymmetricRankingLoss
widget:
- source_sentence: Could you clarify the process for determining whether an entity
is subject to FATCA and the ADGM Common Reporting Standard Regulations 2017?
sentences:
- If Rule 7.5.3(b) or 7.5.3(c) applies, the Insurance Intermediary must, if requested
by the Retail Client, provide to that Client a list of insurers with whom it deals
or may deal in relation to the relevant Contracts of Insurance.
- 'REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES
IN RELATION TO VIRTUAL ASSETS
International Tax Reporting Obligations
COBS Rule 17.4 requires Authorised Persons to consider and, if applicable, adhere
to their tax reporting obligations including, as applicable, under the Foreign
Account Tax Compliance Act (“FATCA”) and the ADGM Common Reporting Standard Regulations
2017.
'
- "The following lists some of the items that an Authorised Person should consider\
\ including in its internal reporting of Operational Risks:\na.\tthe results of\
\ monitoring activities;\nb.\tassessments of the Operational Risk framework performed\
\ by control functions such as internal audit, compliance, risk management and/or\
\ external audit;\nc.\treports generated by (and/or for) supervisory authorities;\n\
d.\tmaterial breaches of the Authorised Person's risk appetite and tolerance with\
\ respect to Operational Risk;\ne.\tdetails of recent significant internal Operational\
\ Risk events and losses, including near misses or events that resulted in a positive\
\ return; and\nf.\trelevant external events and any potential impact on the Authorised\
\ Person and its Operational Risk framework, including Operational Risk capital."
- source_sentence: Could you provide specific examples of how a Relevant Person should
verify the existence of any secrecy or data protection laws in a third-party's
country of incorporation that might impede access to CDD information?
sentences:
- A Relevant Person should verify whether any secrecy or data protection law exists
in the country of incorporation of the business partner that would prevent access
to relevant data.
- "An Applicant for a Financial Services Permission must pay to the Regulator an\
\ application fee of $10,000 to carry on the Regulated Activity of:\n(a)\tArranging\
\ Credit;\n(b)\tOperating a Multilateral Trading Facility;\n(c)\tOperating an\
\ Organised Trading Facility;\n(d)\tManaging a Collective Investment Fund;\n(e)\t\
Managing a Venture Capital Fund and co-investments;\n(f)\tActing as the Administrator\
\ of a Collective Investment Fund;\n(g)\tActing as Trustee of an Investment Trust;\n\
(h)\tOperating a Credit Rating Agency; or\n(i)\tOperating a Private Financing\
\ Platform."
- A Business Reorganisation Plan may be further amended following its initial implementation
if the Regulator is of the view that changes to the plan are required to achieve
the long-term viability of the Institution.
- source_sentence: What specific criteria must a conventional custodian meet to be
approved by the FSRA as a Digital Security Facility (DSF) for the custody of Digital
Securities?
sentences:
- "Such Rules may prescribe—\n(a)\tthe circumstances in which an Issuer is required\
\ to appoint a sponsor, and a Reporting Entity is required to appoint a compliance\
\ adviser or other expert adviser;\n(b)\tthe requirements applicable to the Issuer\
\ or Reporting Entity, and a person Appointed as a sponsor, compliance adviser\
\ or other expert adviser; and\n(c)\tany other matter necessary to give effect\
\ to such appointments.\n"
- If a Fund Manager is unable to manage a conflict of interest as provided above,
it must dismiss or replace the member as appropriate.
- 'DIGITAL SECURITIES – INTERMEDIARIES
Intermediaries conducting a Regulated Activity in relation to Virtual Assets –
Extension into Digital Securities
Virtual Asset Custodians may apply to the FSRA to be a DSF in order to provide
custody of Digital Securities. Refer to paragraphs 73 to 75 for further information
on the requirements that will apply.
'
- source_sentence: Can you elaborate on the types of records an Authorised Person
must retain related to ESG disclosures and corporate governance practices?
sentences:
- 'REGULATORY REQUIREMENTS - SPOT COMMODITY ACTIVITIES
Default Rules
The FSRA suggests that an Applicant/Authorised Person consider different scenarios/circumstances
where it may need to utilise the powers provided to it under its Default Rules,
and take appropriate action as required. Scenario testing of this kind could
relate to when there is a financial and/or technical ‘default’ in relation to,
for example, delivery failure, storage failure or wider banking arrangements. Due
to the short settlement cycle of Spot Commodity markets, the impact of a ‘default’
may be on a per-transaction basis or structural basis, in limiting the ability
of Members to fulfil their delivery obligations (and therefore the ability of
the MTF to operate on a fair and orderly basis).
'
- Risk control. Authorised Persons should recognise and control the Credit Risk
arising from their new products and services. Well in advance of entering into
business transactions involving new types of products and activities, they should
ensure that they understand the risks fully and have established appropriate Credit
Risk policies, procedures and controls, which should be approved by the Governing
Body or its appropriate delegated committee. A formal risk assessment of new products
and activities should also be performed and documented.
- 'Records: An Authorised Person must make and retain records of matters and dealings,
including Accounting Records and corporate governance practices which are the
subject of requirements and standards under the Regulations and Rules.
'
- source_sentence: How does ADGM ensure that FinTech Participants remain compliant
with evolving regulatory standards, particularly in the context of new and developing
technologies?
sentences:
- 'DIGITAL SECURITIES – INTERMEDIARIES
Conventional Intermediaries – Digital Securities
Intermediaries 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.
'
- "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)\t\
a FinTech Participant."
- Where an individual is appointed under this Rule, the Regulator may exercise any
powers it would otherwise be entitled to exercise as if the individual held Approved
Person status.
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/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](https://huggingface.co/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](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': 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:
```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("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:
(a) the FinTech Proposal promotes FinTech innovation, in terms of the business application and deployment model of the technology.
(b) the FinTech Proposal has the potential to:
i. promote significant growth, efficiency or competition in the financial sector;
ii. promote better risk management solutions and regulatory outcomes for the financial industry; or
iii. improve the choices and welfare of clients.
(c) the FinTech Proposal is at a sufficiently advanced stage of development to mount a live test.
(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.
|
| 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.
|
* Loss: [MultipleNegativesSymmetricRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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
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:
(a) reserving risk;
(b) investment risk (including risks associated with the use of Derivatives);
(c) underwriting risk;
(d) market risk;
(e) liquidity management risk;
(f) credit quality risk;
(g) fraud and other fiduciary risks;
(h) compliance risk;
(i) outsourcing risk; and
(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? |
In establishing its systems and controls to address information security risks, an Authorised Person should have regard to:
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;
b. the risk of loss or theft of customer data;
c. integrity: safeguarding the accuracy and completeness of information and its processing;
d. non repudiation and accountability: ensuring that the Person or system that processed the information cannot deny their actions; and
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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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