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jebish7
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mpnet-base-all-obliqa_NMR_3

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:29547
loss:MultipleNegativesRankingLoss
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SentenceTransformer based on jebish7/mpnet-base-all-obliqa_NMR

This is a sentence-transformers model finetuned from jebish7/mpnet-base-all-obliqa_NMR on the csv dataset. 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: jebish7/mpnet-base-all-obliqa_NMR
  • Maximum Sequence Length: 384 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • csv

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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

# Download from the 🤗 Hub
model = SentenceTransformer("jebish7/mpnet-base-all-obliqa_NMR_3")
# Run inference
sentences = [
    'Can the ADGM provide examples of legal risks associated with securitisation that Authorised Persons should particularly be aware of and manage?',
    "This Chapter includes the detailed Rules and associated guidance in respect of a firm's obligation to manage effectively its Exposures to Operational Risk. Operational Risk refers to the risk of incurring losses due to the failure of systems, processes, and personnel to perform expected tasks. Operational Risk losses also include losses arising out of legal risk. This Chapter aims to ensure that an Authorised Person has a robust Operational Risk management framework commensurate with the nature, scale and complexity of its operations and that it holds sufficient regulatory capital against Operational Risk Exposures.",
    'When employing an eKYC System to assist with CDD, a Relevant Person should:\na.\tensure that it has a thorough understanding of the eKYC System itself and the risks of eKYC, including those outlined by relevant guidance from FATF and other international standard setting bodies;\nb.\tcomply with all the Rules of the Regulator relevant to eKYC including, but not limited to, applicable requirements regarding the business risk assessment, as per Rule \u200e6.1, and outsourcing, as per Rule \u200e9.3;\nc.\tcombine eKYC with transaction monitoring, anti-fraud and cyber-security measures to support a wider framework preventing applicable Financial Crime; and\nd.\ttake appropriate steps to identify, assess and mitigate the risk of the eKYC system being misused for the purposes of Financial Crime.',
]
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]

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: 15 tokens
    • mean: 34.89 tokens
    • max: 96 tokens
    • min: 14 tokens
    • mean: 115.11 tokens
    • max: 384 tokens
  • Samples:
    Question positive
    Under Rules 7.3.2 and 7.3.3, what are the two specific conditions related to the maturity of a financial instrument that would trigger a disclosure requirement? Events that trigger a disclosure. For the purposes of Rules 7.3.2 and 7.3.3, a Person is taken to hold Financial Instruments in or relating to a Reporting Entity, if the Person holds a Financial Instrument that on its maturity will confer on him:
    (1) an unconditional right to acquire the Financial Instrument; or
    (2) the discretion as to his right to acquire the Financial Instrument.
    Best Execution and Transaction Handling: What constitutes 'Best Execution' under Rule 6.5 in the context of virtual assets, and how should Authorised Persons document and demonstrate this? The following COBS Rules should be read as applying to all Transactions undertaken by an Authorised Person conducting a Regulated Activity in relation to Virtual Assets, irrespective of any restrictions on application or any exception to these Rules elsewhere in COBS -
    (a) Rule 3.4 (Suitability);
    (b) Rule 6.5 (Best Execution);
    (c) Rule 6.7 (Aggregation and Allocation);
    (d) Rule 6.10 (Confirmation Notes);
    (e) Rule 6.11 (Periodic Statements); and
    (f) Chapter 12 (Key Information and Client Agreement).
    How does the FSRA define and evaluate "principal risks and uncertainties" for a Petroleum Reporting Entity, particularly for the remaining six months of the financial year? A Reporting Entity must:
    (a) prepare such report:
    (i) for the first six months of each financial year or period, and if there is a change to the accounting reference date, prepare such report in respect of the period up to the old accounting reference date; and
    (ii) in accordance with the applicable IFRS standards or other standards acceptable to the Regulator;
    (b) ensure the financial statements have either been audited or reviewed by auditors, and the audit or review by the auditor is included within the report; and
    (c) ensure that the report includes:
    (i) except in the case of a Mining Exploration Reporting Entity or a Petroleum Exploration Reporting Entity, an indication of important events that have occurred during the first six months of the financial year, and their impact on the financial statements;
    (ii) except in the case of a Mining Exploration Reporting Entity or a Petroleum Exploration Reporting Entity, a description of the principal risks and uncertainties for the remaining six months of the financial year; and
    (iii) a condensed set of financial statements, an interim management report and associated responsibility statements.
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 24
  • learning_rate: 2e-05
  • num_train_epochs: 2
  • warmup_ratio: 0.1
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 24
  • 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: 2
  • 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: False
  • 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
0.1623 100 0.4433
0.3247 200 0.3978
0.4870 300 0.4173
0.6494 400 0.4892
0.8117 500 0.5729
0.9740 600 0.5901
1.1331 700 0.4664
1.2955 800 0.3703
1.4578 900 0.3813
1.6201 1000 0.3964
1.7825 1100 0.4536
1.9448 1200 0.4513

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 

@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",
}

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}
}
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