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.

python

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 tokens
mean: 81.19 tokens
max: 210 tokens
min: 44 tokens
mean: 81.51 tokens
max: 211 tokens

Samples:

anchor	positive
`Carrier: Golden Bear Insurance Company[object Object]Attorney: Partner \| Rate: $210/hr \| Units: 0.3[object Object]Task: L120 - Analysis/Strategy \| Activity: A104 - Review/analyze[object Object]Narrative: 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 Company[object Object]Attorney: Partner \| Rate: $250/hr \| Units: 0.1[object Object]Task: L440 - Other Trial Preparation and Support \| Activity: A104 - Review/analyze[object Object]Narrative: 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 Company[object Object]Attorney: Associate \| Rate: $240/hr \| Units: 0.1[object Object]Task: L110 - Fact Investigation/Development \| Activity: A103 - Draft/revise[object Object]Narrative: 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 Company[object Object]Attorney: Associate \| Rate: $240/hr \| Units: 1.4[object Object]Task: L310 - Written Discovery \| Activity: A103 - Draft/revise[object Object]Narrative: Prepare Defendant's First Request for Production to Plaintiff.`
`Carrier: Argo Group U.S.[object Object]Attorney: Paralegal \| Rate: $135/hr \| Units: 0.2[object Object]Task: L110 - Fact Investigation/Development \| Activity: A103 - Draft/revise[object Object]Narrative: 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.[object Object]Attorney: Paralegal \| Rate: $135/hr \| Units: 0.2[object Object]Task: L130 - Experts/Consultants \| Activity: A108 - Communicat/MISC[object Object]Narrative: 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:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

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

Click to expand

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

Epoch	Step	Training Loss
0.0447	10	1.5338
0.0894	20	0.4740
0.1341	30	0.4362
0.1788	40	0.3799
0.2235	50	0.4078
0.2682	60	0.3891
0.3128	70	0.4182
0.3575	80	0.3938
0.4022	90	0.4459
0.4469	100	0.4123
0.4916	110	0.3640
0.5363	120	0.4194
0.5810	130	0.3928
0.6257	140	0.4267
0.6704	150	0.4228
0.7151	160	0.4358
0.7598	170	0.4309
0.8045	180	0.4161
0.8492	190	0.4289
0.8939	200	0.4091
0.9385	210	0.3994
0.9832	220	0.4184
1.0268	230	0.4119
1.0715	240	0.4279
1.1162	250	0.3907
1.1609	260	0.4242
1.2056	270	0.4049
1.2503	280	0.3787
1.2950	290	0.4150
1.3397	300	0.4472
1.3844	310	0.3944
1.4291	320	0.4288
1.4737	330	0.3718
1.5184	340	0.4148
1.5631	350	0.4160
1.6078	360	0.3907
1.6525	370	0.3918
1.6972	380	0.3777
1.7419	390	0.4300
1.7866	400	0.3913
1.8313	410	0.4205
1.8760	420	0.3863
1.9207	430	0.4370
1.9654	440	0.4225
2.0089	450	0.4057
2.0536	460	0.3843
2.0983	470	0.4034
2.1430	480	0.4115
2.1877	490	0.4128
2.2324	500	0.4028
2.2771	510	0.4198
2.3218	520	0.3613
2.3665	530	0.4017
2.4112	540	0.3639
2.4559	550	0.3978
2.5006	560	0.3982
2.5453	570	0.4059
2.5899	580	0.4175
2.6346	590	0.4510
2.6793	600	0.4210
2.7240	610	0.4098
2.7687	620	0.4082
2.8134	630	0.3970
2.8581	640	0.3846
2.9028	650	0.4155
2.9475	660	0.4071
2.9922	670	0.4293

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

bibtex

@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

bibtex

@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},
}

anchor	positive
`Carrier: Golden Bear Insurance Company[object Object]Attorney: Partner \| Rate: $210/hr \| Units: 0.3[object Object]Task: L120 - Analysis/Strategy \| Activity: A104 - Review/analyze[object Object]Narrative: 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 Company[object Object]Attorney: Partner \| Rate: $250/hr \| Units: 0.1[object Object]Task: L440 - Other Trial Preparation and Support \| Activity: A104 - Review/analyze[object Object]Narrative: 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 Company[object Object]Attorney: Associate \| Rate: $240/hr \| Units: 0.1[object Object]Task: L110 - Fact Investigation/Development \| Activity: A103 - Draft/revise[object Object]Narrative: 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 Company[object Object]Attorney: Associate \| Rate: $240/hr \| Units: 1.4[object Object]Task: L310 - Written Discovery \| Activity: A103 - Draft/revise[object Object]Narrative: Prepare Defendant's First Request for Production to Plaintiff.`
`Carrier: Argo Group U.S.[object Object]Attorney: Paralegal \| Rate: $135/hr \| Units: 0.2[object Object]Task: L110 - Fact Investigation/Development \| Activity: A103 - Draft/revise[object Object]Narrative: 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.[object Object]Attorney: Paralegal \| Rate: $135/hr \| Units: 0.2[object Object]Task: L130 - Experts/Consultants \| Activity: A108 - Communicat/MISC[object Object]Narrative: 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.`

	anchor	positive
type	string	string
details	min: 50 tokens mean: 81.19 tokens max: 210 tokens	min: 44 tokens mean: 81.51 tokens max: 211 tokens