SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-long

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: Snowflake/snowflake-arctic-embed-m-long
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (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)

Training Details

Training Dataset

csv

• Dataset: csv
• Size: 29,547 training samples
• Columns: Question and positive
• Approximate statistics based on the first 1000 samples:

  	Question	positive
  type	string	string
  details	min: 18 tokens mean: 34.27 tokens max: 76 tokens	min: 13 tokens mean: 112.44 tokens max: 768 tokens

• Samples:

  Question	positive
  Regarding the assessment of the nature, ownership, and control structure of a customer, could you clarify the level of detail and due diligence expected from a Relevant Person to ensure adherence to regulatory standards?	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]
  What specific factors should be included in our risk assessment methodology to ensure it aligns with the expectations outlined in Chapter 6 and Chapter 7 of the AML Rulebook?	The RBA should not be seen as a "tick-box" approach to AML/TFS. Instead a Relevant Person is required to assess relevant money laundering risks and adopt a proportionate response to such risks, however, even where a customer is assessed through the RBA as being low-risk a minimum of simplified CDD must be undertaken in relation to that customer.
  In the event of an investigation by the ADGM, what are the specific obligations and limitations regarding confidentiality for the entity under investigation, and what kind of disclosures are permissible under sections 197 and 198 of FSMR?	We will not normally make public the fact that we are investigating a matter. We also expect that the person who is the subject of an investigation will treat the matter as confidential. However, subject to the restrictions on disclosure of confidential information in sections 197 and 198 of FSMR, this does not stop the person under investigation from seeking professional advice or making their own enquiries into the matter, giving their auditors appropriate details of the matter or making notifications required by law.

• Loss: MultipleNegativesRankingLoss with these parameters:
  json

  1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 4
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_ratio: 0.1
• batch_sampler: no_duplicates

All Hyperparameters

Training Logs

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

MultipleNegativesRankingLoss