This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'To evaluate the cost-effectiveness of ED-initiated buprenorphine with peer navigator support compared to enhanced referral to treatment.',
    'Concerns about withdrawal precipitation',
    '11.1.2 Steering Committee\n\nComposition:\n- Executive Committee members\n- Site investigators\n- Patient/community representatives\n- Key co-investigators\n\nResponsibilities:\n- Protocol revisions\n- Implementation monitoring\n- Recruitment oversight\n- Review of study progress\n- Addressing operational challenges',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 247,936 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 48.16 tokens
max: 256 tokens
min: 3 tokens
mean: 44.76 tokens
max: 256 tokens
min: 0.5
mean: 0.5
max: 1.0

Samples:

sentence_0	sentence_1	label
`10.4 Participant Confidentiality`	`9.1.1 Data and Safety Monitoring Board (DSMB)[object Object][object Object]An independent DSMB will be established, consisting of experts in emergency medicine, addiction medicine, biostatistics, and ethics. The DSMB will:[object Object]- Review and approve the monitoring plan[object Object]- Meet at least annually to review study progress and safety[object Object]- Review any serious adverse events[object Object]- Make recommendations regarding study continuation or modification`	`0.5`
`7.1 Randomization[object Object][object Object]Participants will be randomly assigned in a 1:1 ratio to receive either BUP-NX or XR-NTX using a computer-generated randomization sequence with permuted blocks of varying sizes. Randomization will be stratified by site and by opioid type (short-acting prescription opioids, heroin, or fentanyl as primary opioid of use).`	`10.3 Risk Mitigation`	`0.5`
`11.1 Study Leadership and Governance`	`To examine patient perspectives on intervention acceptability and barriers/facilitators to engagement through qualitative interviews with a subset of participants.`	`0.5`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
multi_dataset_batch_sampler: round_robin

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: 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: 3
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}
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: None
hub_always_push: False
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
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.0323	500	0.0107
0.0645	1000	0.0025
0.0968	1500	0.0023
0.1291	2000	0.0023
0.1613	2500	0.0021
0.1936	3000	0.002
0.2259	3500	0.0018
0.2581	4000	0.0018
0.2904	4500	0.0017
0.3227	5000	0.0017
0.3549	5500	0.0017
0.3872	6000	0.0016
0.4195	6500	0.0015
0.4517	7000	0.0016
0.4840	7500	0.0016
0.5163	8000	0.0015
0.5485	8500	0.0015
0.5808	9000	0.0014
0.6131	9500	0.0015
0.6453	10000	0.0015
0.6776	10500	0.0014
0.7099	11000	0.0015
0.7421	11500	0.0013
0.7744	12000	0.0013
0.8067	12500	0.0013
0.8389	13000	0.0013
0.8712	13500	0.0013
0.9035	14000	0.0013
0.9357	14500	0.0013
0.9680	15000	0.0012
1.0003	15500	0.0012
1.0325	16000	0.0011
1.0648	16500	0.0011
1.0971	17000	0.0011
1.1293	17500	0.0011
1.1616	18000	0.0011
1.1939	18500	0.001
1.2261	19000	0.001
1.2584	19500	0.0011
1.2907	20000	0.001
1.3229	20500	0.0011
1.3552	21000	0.001
1.3875	21500	0.001
1.4197	22000	0.001
1.4520	22500	0.001
1.4843	23000	0.001
1.5165	23500	0.0009
1.5488	24000	0.001
1.5811	24500	0.001
1.6133	25000	0.0009
1.6456	25500	0.001
1.6779	26000	0.001
1.7101	26500	0.001
1.7424	27000	0.001
1.7747	27500	0.001
1.8069	28000	0.001
1.8392	28500	0.001
1.8715	29000	0.001
1.9037	29500	0.0009
1.9360	30000	0.0009
1.9682	30500	0.0009
2.0005	31000	0.0009
2.0328	31500	0.0008
2.0650	32000	0.0008
2.0973	32500	0.0007
2.1296	33000	0.0008
2.1618	33500	0.0008
2.1941	34000	0.0008
2.2264	34500	0.0008
2.2586	35000	0.0008
2.2909	35500	0.0008
2.3232	36000	0.0008
2.3554	36500	0.0008
2.3877	37000	0.0008
2.4200	37500	0.0008
2.4522	38000	0.0008
2.4845	38500	0.0008
2.5168	39000	0.0008
2.5490	39500	0.0008
2.5813	40000	0.0007
2.6136	40500	0.0008
2.6458	41000	0.0008
2.6781	41500	0.0007
2.7104	42000	0.0007
2.7426	42500	0.0007
2.7749	43000	0.0008
2.8072	43500	0.0008
2.8394	44000	0.0007
2.8717	44500	0.0008
2.9040	45000	0.0008
2.9362	45500	0.0007
2.9685	46000	0.0007

Framework Versions

Python: 3.13.2
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0
Accelerate: 1.4.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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",
}

HEAL Protocol Embeddings

This model is fine-tuned from all-MiniLM-L6-v2 on HEAL Initiative clinical protocols.

Performance Evaluation

Comparison with OpenAI embeddings:

Metric	OpenAI	Fine-tuned	Change
Faithfulness	0.667	0.833	⬆️ +0.166
Answer Relevancy	0.986	0.986	=
Context Precision	1.000	1.000	=
Context Recall	1.000	0.000	⬇️ -1.000

Key Findings

Improved faithfulness to source material
Maintained high answer relevancy
Trade-off in context recall

Future Improvements

Retrieval Strategy
- Implement hybrid search combining semantic and keyword matching
- Add re-ranking for better result ordering
Model Architecture
- Experiment with larger base models
- Fine-tune with domain-specific loss functions
Data Processing
- Optimize chunking strategy
- Increase training data diversity

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 48.16 tokens max: 256 tokens	min: 3 tokens mean: 44.76 tokens max: 256 tokens	min: 0.5 mean: 0.5 max: 1.0

lsy9874205/heal-protocol-embeddings

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2