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jebish7
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cde-small-v1_MNR_3

Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:29545
loss:MultipleNegativesRankingLoss
custom_code
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SentenceTransformer based on jxm/cde-small-v1

This is a sentence-transformers model finetuned from jxm/cde-small-v1 on the csv dataset. It maps sentences & paragraphs to a None-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: jxm/cde-small-v1
  • Maximum Sequence Length: None tokens
  • Output Dimensionality: None tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • csv

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({}) with Transformer model: DatasetTransformer 
)

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/cde-small-v1_MNR_3")
# Run inference
sentences = [
    'What are the common scenarios or instances where assets and liabilities are not covered by the bases of accounting in Rule 5.3.2, and how should an Insurer address these in their reporting?',
    'DocumentID: 12 | PassageID: 5.3.1.Guidance | Passage: \nThe exceptions provided in this Chapter relate to the following:\na.\tspecific Rules in respect of certain assets and liabilities, intended to achieve a regulatory objective not achieved by application of either or both of the bases of accounting set out in Rule \u200e5.3.2;\nb.\tassets and liabilities that are not dealt with in either or both of the bases of accounting set out in Rule \u200e5.3.2; and\nc.\tthe overriding power of the Regulator, set out in Rule \u200e5.1.6, to require an Insurer to adopt a particular measurement for a specific asset or liability.',
    'DocumentID: 1 | PassageID: 14.4.1.Guidance.1. | Passage: Relevant Persons are reminded that in accordance with Federal AML Legislation, Relevant Persons or any of their Employees must not tip off any Person, that is, inform any Person that he is being scrutinised, or investigated by any other competent authority, for possible involvement in suspicious Transactions or activity related to money laundering or terrorist financing.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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: 16 tokens
    • mean: 34.95 tokens
    • max: 68 tokens
    • min: 35 tokens
    • mean: 132.0 tokens
    • max: 512 tokens
  • Samples:
    anchor positive
    If a financial institution offers Money Remittance as one of its services, under what circumstances is it deemed to be holding Relevant Money and therefore subject to regulatory compliance (a)? DocumentID: 13
    What are the consequences for a Recognised Body or Authorised Person if they fail to comply with ADGM's requirements regarding severance payments? DocumentID: 7
    If a Public Fund is structured as an Investment Trust, to whom should the Fund Manager report the review findings regarding delegated Regulated Activities or outsourced functions? DocumentID: 6
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • learning_rate: 2e-05
  • 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: 16
  • 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: 3
  • 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
0.1082 100 1.9962
0.2165 200 1.1626
0.3247 300 0.9907
0.4329 400 0.8196
0.5411 500 0.8082
0.6494 600 0.6944
0.7576 700 0.6559
0.8658 800 0.6242
0.9740 900 0.6299
1.0823 1000 0.6051
1.1905 1100 0.567
1.2987 1200 0.4679
1.4069 1300 0.3443
1.5152 1400 0.3356
1.6234 1500 0.2958
1.7316 1600 0.254
1.8398 1700 0.2694
1.9481 1800 0.2497
2.0563 1900 0.2671
2.1645 2000 0.2558
2.2727 2100 0.1943
2.3810 2200 0.1242
2.4892 2300 0.116
2.5974 2400 0.1081
2.7056 2500 0.1056
2.8139 2600 0.107
2.9221 2700 0.1154

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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