This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: BAAI/bge-base-en-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    '<> OTPs must provide counseling on preventing exposure to, and the transmission of, human immunodeficiency virus (HIV), viral hepatitis, and sexually transmitted infections (STIs) and either directly provide services and treatments or actively link to treatment each patient admitted or readmitted to treatment who has received positive test results for these conditions from initial and/or periodic medical examinations. <> OTPs must provide directly, or through referral to adequate and reasonably accessible community resources, vocational training, education, and employment services for patients who request such services or for whom these needs have been identified and mutually agreed-upon as beneficial by the patient and program staff.',
    '<> OTPs must provide counseling on preventing exposure to, and the transmission of, human immunodeficiency virus (HIV), viral hepatitis, and sexually transmitted infections (STIs) and either directly provide services and treatments or actively link to treatment each patient admitted or readmitted to treatment who has received positive test results for these conditions from initial and/or periodic medical examinations. <> OTPs must provide directly, or through referral to adequate and reasonably accessible community resources, vocational training, education, and employment services for patients who request such services or for whom these needs have been identified and mutually agreed-upon as beneficial by the patient and program staff.',
    'c. State Practice - The PECOS record should not be switched to "Approved" until the contractor receives the state\'s approval recommendation. However, if the contractor knows that the state in question generally does not review this type of transaction, the contractor need not send the transaction to the state and shall instead follow the instructions in section 10.6.1.2(B)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 1.0000, 0.5026],
#         [1.0000, 1.0000, 0.5026],
#         [0.5026, 0.5026, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 47,056 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 100 samples:
sentence_0 sentence_1
type string string
modality text text
details
min: 23 tokens
mean: 81.8 tokens
max: 225 tokens
min: 35 tokens
mean: 117.99 tokens
max: 225 tokens

Samples:

sentence_0	sentence_1
`<> percent of the site the as determined under paragraph (c)(1) this section; and <> 50 percent the standard Federal prospective rate amount for discharge as determined § 412.523. (1) For section, the discharge payment is a ratio, expressed a percentage, of Medicare that the criteria exclusion site neutral payment rate as under paragraph (a)(2) of to total Medicare discharges under subpart during cost`	<> 50 percent of the site neutral payment rate amount for the discharge as determined under paragraph (c)(1) of this section; and <> 50 percent of the standard Federal prospective payment rate amount for the discharge as determined under § 412.523. (1) For purposes of this section, the discharge payment percentage is a ratio, expressed as a percentage, of Medicare discharges that meet the criteria for exclusion from the site neutral payment rate as described under paragraph (a)(2) of this section to total Medicare discharges paid under this subpart during the cost reporting period.
`Respirators shall equipped a durable bearing markings which applicant's name, the commercial designation of the and all appropriate approval`	`Respirators shall be equipped with a substantial, durable container bearing markings which show the applicant's name, the type and commercial designation of the respirator it contains and all appropriate approval labels.`
`This chapter specifies and procedures to MACs, Recovery Auditors, and the SMRC. The shall instructions to identify verify potential errors to produce the greatest to Medicare Contractors should use analytical methodologies to evaluate potential errors and take administrative unless determined is a priority to the They should archive the error`	This chapter specifies resources and procedures to the MACs, UPICs, Recovery Auditors, and the SMRC. The contractors shall use these instructions to identify and verify potential errors to produce the greatest protection to the Medicare program. Contractors should objectively use analytical methodologies to evaluate potential errors and not take administrative action unless they have verified the error and determined that the error is a high enough priority to justify the action. They should also archive the error including

Loss: DenoisingAutoEncoderLoss with these parameters:
json
1{ 2 "decoder_name_or_path": "BAAI/bge-base-en-v1.5", 3 "need_retokenization": false 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 8
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 8
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.1700	500	7.5851
0.3400	1000	6.3479
0.5100	1500	5.5558
0.6800	2000	4.7201
0.8501	2500	4.28
1.0201	3000	3.9788
1.1901	3500	3.728
1.3601	4000	3.568
1.5301	4500	3.4469
1.7001	5000	3.3317
1.8701	5500	3.2348
2.0401	6000	3.1438
2.2101	6500	3.0118
2.3801	7000	2.9728
2.5502	7500	2.9188
2.7202	8000	2.8691
2.8902	8500	2.8345
3.0602	9000	2.7537
3.2302	9500	2.6734
3.4002	10000	2.6435
3.5702	10500	2.6327
3.7402	11000	2.5934
3.9102	11500	2.5739
4.0802	12000	2.5119
4.2503	12500	2.4507
4.4203	13000	2.4414
4.5903	13500	2.4174
4.7603	14000	2.419
4.9303	14500	2.3955
5.1003	15000	2.3289
5.2703	15500	2.2981
5.4403	16000	2.2936
5.6103	16500	2.2902
5.7803	17000	2.2897
5.9504	17500	2.2659
6.1204	18000	2.2225
6.2904	18500	2.2028
6.4604	19000	2.1958
6.6304	19500	2.205
6.8004	20000	2.2054
6.9704	20500	2.2045
7.1404	21000	2.177
7.3104	21500	2.1662
7.4804	22000	2.1731
7.6505	22500	2.1592
7.8205	23000	2.1557
7.9905	23500	2.1608

Training Time

Training: 2.8 hours

Framework Versions

Python: 3.12.6
Sentence Transformers: 5.6.0
Transformers: 4.56.0
PyTorch: 2.8.0+cu129
Accelerate: 1.10.1
Datasets: 5.0.0
Tokenizers: 0.22.0

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",
}

DenoisingAutoEncoderLoss