SentenceTransformer based on BAAI/bge-large-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-large-en-v1.5. It maps sentences & paragraphs to a 1024-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-large-en-v1.5
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

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, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("rshn-krn/bge-large-legal-billing")
# Run inference
sentences = [
    'Carrier: Coaction Global, Inc.\nAttorney: Senior Counsel | Rate: $245/hr | Units: 0.1\nTask: L120 - Analysis/Strategy | Activity: A103 - Draft/revise\nNarrative: Draft Litigation Status Report regarding the deposition testimony of California Highway Patrol Officer A.S. Johnson with attention to Section I. Testimony, subsection G. Witness Statements, subsection 3. Tuna Taleni ',
    'Carrier: Coaction Global, Inc.\nAttorney: Senior Counsel | Rate: $245/hr | Units: 0.3\nTask: L120 - Analysis/Strategy | Activity: A103 - Draft/revise\nNarrative: Draft Litigation Status Report regarding the deposition testimony of California Highway Patrol Officer A.S. Johnson with attention to Section I. Testimony, subsection G. Witness Statements, subsection 4. Lumafale Oti ',
    'Carrier: Mitsui Sumitomo Marine Management, Inc.\nAttorney: Senior Counsel | Rate: $325/hr | Units: 0.1\nTask: L110 - Fact Investigation/Development | Activity: A107 - Communicat/OUT\nNarrative: Email to current counsel for Google defendants regarding status of dismissal of non-related entities in our to evaluate the case for insured.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9998, 0.4579],
#         [0.9998, 1.0000, 0.4567],
#         [0.4579, 0.4567, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

• Size: 28,621 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 50 tokensmean: 81.19 tokensmax: 210 tokens	min: 44 tokensmean: 81.51 tokensmax: 211 tokens

• Samples:

  anchor	positive
  Carrier: Golden Bear Insurance CompanyAttorney: Partner | Rate: $210/hr | Units: 0.3Task: L120 - Analysis/Strategy | Activity: A104 - Review/analyzeNarrative: Review and analysis of asserted claims with particular attention to elements necessary to establish and likely defenses available to rebut claims and related initial pleadings, investigation, and discovery to leverage same so as to strategically implement ideal defense strategy	Carrier: Golden Bear Insurance CompanyAttorney: Partner | Rate: $250/hr | Units: 0.1Task: L440 - Other Trial Preparation and Support | Activity: A104 - Review/analyzeNarrative: Analyze the notice from the civil division manager for Monmouth County ordering a trial in this matter in April of 2025
  Carrier: Aspen Specialty Insurance CompanyAttorney: Associate | Rate: $240/hr | Units: 0.1Task: L110 - Fact Investigation/Development | Activity: A103 - Draft/reviseNarrative: Draft and evaluate electronic correspondence to Ross McKissick, Plaintiff's counsel, discussing the nature and scope of Plaintiff's meet and confer issues for purposes of defense of same.	Carrier: Aspen Specialty Insurance CompanyAttorney: Associate | Rate: $240/hr | Units: 1.4Task: L310 - Written Discovery | Activity: A103 - Draft/reviseNarrative: Prepare Defendant's First Request for Production to Plaintiff.
  Carrier: Argo Group U.S.Attorney: Paralegal | Rate: $135/hr | Units: 0.2Task: L110 - Fact Investigation/Development | Activity: A103 - Draft/reviseNarrative: Draft r letter to American Pacific Concrete LLC to be in compliance with order issued by Arbitrator and to advise possible default can be entered if failure to respond.	Carrier: Argo Group U.S.Attorney: Paralegal | Rate: $135/hr | Units: 0.2Task: L130 - Experts/Consultants | Activity: A108 - Communicat/MISCNarrative: Emails (4) to and from retained expert Jeff Ambrosia regarding meeting of experts of all parties to discuss landscaping issues in compliance with Arbitrator's order.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32
• warmup_steps: 0.1
• gradient_accumulation_steps: 4
• fp16: True
• gradient_checkpointing: True

All Hyperparameters

• per_device_train_batch_size: 32
• num_train_epochs: 3
• max_steps: -1
• learning_rate: 5e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 4
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: True
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: trackio
• eval_strategy: no
• per_device_eval_batch_size: 8
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Framework Versions

• Python: 3.11.10
• Sentence Transformers: 5.3.0
• Transformers: 5.4.0
• PyTorch: 2.11.0+cu130
• Accelerate: 1.13.0
• Datasets: 4.8.4
• Tokenizers: 0.22.2

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}