This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B. 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: Qwen/Qwen3-Embedding-0.6B
Maximum Sequence Length: 32768 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': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, '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': True, '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
queries = [
    "What is Patricia Vasquez\u0027s availability for a risk mitigation meeting today regarding the OR3 surgical incident?",
]
documents = [
    'Subject: Re: URGENT: Incident Report - OR3 Surgical Case\nDate: 2026-01-28T09:16:00\nFrom: David R. Park\nParticipants: Patricia Vasquez\n\nBody:\nDear Patricia,\n\nThank you for your prompt and thorough notification regarding the OR3 surgical incident. Given the seriousness of the situation, I strongly recommend convening a risk mitigation meeting with the core team as soon as possible today. It is essential that we conduct a careful review of all documentation related to the event and ensure our messaging to the patient, family, and regulatory bodies remains consistent and accurate. Please remind all involved staff to refrain from independent written or verbal statements about the incident; all communications should be directed through my office to preserve attorney-client privilege. Additionally, we must protect peer review privilege wherever it may apply.\n\nLet me know your availability for a meeting, and I will coordinate accordingly. I appreciate your diligence as we navigate this sensitive matter together.\n\nBest regards,\nDavid',
    'Subject: Re: Cultural Competency Training Compliance Concerns\nDate: 2025-10-23T12:42:00\nFrom: Maria C. Gonzalez\nParticipants: Carlos J. Rodriguez\n\nBody:\nHi Carlos,\n\nThank you for your prompt response and for taking immediate action to reinforce the cultural competency training requirements with your team. I appreciate your commitment to ensuring that all staff members complete the training by the outlined deadline. Please keep me updated if you encounter any challenges or need additional support, as maintaining regulatory compliance is a top priority for our department. Your attention to this matter is greatly appreciated.\n\nBest regards,\nMaria',
    'Subject: Fuera de la oficina: Karen M. Phillips\nDate: 2025-09-29T14:19:00\nFrom: Karen M. Phillips\nParticipants: Remitente\n\nBody:\nEstimado/a,\n\nGracias por su correo. Lamentablemente, hoy me encuentro fuera de la oficina debido a una enfermedad imprevista. Mi ausencia se debe a la necesidad de un monitoreo riguroso de mis síntomas y ajustes de medicación, prestando especial atención a posibles interacciones farmacológicas y la dosificación precisa para evitar complicaciones.\n\nSi su consulta es urgente, o requiere revisión inmediata de interacción de medicamentos o recomendaciones sobre dosificación, por favor contacte a mi colega Dr. Samuel Rivera al correo s.rivera@patientsafetyinstitute.org, quien podrá asistirle y revisar cualquier cuestión relativa a protocolos de seguridad farmacológica en mi ausencia.\n\nAgradezco su comprensión ante la situación. Revisaré y responderé su mensaje a mi regreso tan pronto me sea posible.\n\nAtentamente,\nKaren M. Phillips\n\n--\nKaren Phillips, PharmD | Pharmacy Services',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7148, -0.0698,  0.0708]], dtype=torch.bfloat16)

Evaluation

Metrics

Information Retrieval

Dataset: val_full_corpus
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8173
cosine_accuracy@3	0.887
cosine_accuracy@5	0.9203
cosine_accuracy@10	0.9651
cosine_precision@1	0.8173
cosine_precision@3	0.2957
cosine_precision@5	0.1841
cosine_precision@10	0.0965
cosine_recall@1	0.8173
cosine_recall@3	0.887
cosine_recall@5	0.9203
cosine_recall@10	0.9651
cosine_ndcg@10	0.8867
cosine_mrr@10	0.8623
cosine_map@100	0.8644

Training Details

Training Dataset

Unnamed Dataset

Size: 2,384 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 10 tokens
mean: 26.23 tokens
max: 73 tokens
min: 114 tokens
mean: 175.45 tokens
max: 391 tokens

Samples:

sentence_0	sentence_1
`Which specific crash cart items should be prioritized for procurement outreach during this week's supply review?`	Subject: Re: Emergency Code Response Drill – Supply Issues Noted[object Object]Date: 2025-10-02T18:56:00[object Object]From: Dr. Samantha L. Chen[object Object]Participants: Carol A. Campbell[object Object][object Object]Body:[object Object]Hi Carol,[object Object][object Object]Thank you for bringing these supply challenges to my attention. I will have my team conduct a thorough review of all crash cart inventories during our upcoming drills this week, and we will keep a detailed record of any additional shortages or supply concerns we encounter. Additionally, I’ll reach out to our procurement contacts to identify potential alternative suppliers and expedite any viable options. Please let me know if there are specific items you need us to prioritize or if you require immediate assistance coordinating with vendors.[object Object][object Object]Best regards,[object Object]Samantha
`Who is responsible for coordinating this week's biannual calibration with Biomedical Engineering for ADL lab equipment (electronic lifts and hand dynamometer) and what is the preferred time window?`	Subject: Lab Equipment Calibration Reminder – Request for Coordination[object Object]Date: 2026-01-04T17:47:00[object Object]From: Jordan P. Anderson[object Object]Participants: Chloe R. Anderson[object Object][object Object]Body:[object Object]Hello Chloe,[object Object][object Object]I wanted to bring to your attention that several pieces of adaptive equipment in our ADL lab are due for their biannual calibration, including the electronic lifts and the hand strength dynamometer. As maintaining accurate calibration is critical for patient safety and functional assessments, could you assist in coordinating a time for calibration with biomedical engineering this week? I’m hoping to minimize disruptions to our daily ADL training schedule, so your input on the best window would be invaluable.[object Object][object Object]Let me know how you’d like to proceed, and thanks for your help in ensuring safe practice environments for our patients.[object Object][object Object]Best,[object Object]Jordan[object Object][object Object]--[object Object]Jordan Anderson, OTR/L \| Occupational Therapy
`Can the phlebotomy slot be moved to before 9:30 AM to resolve the overlap with the CT with IV contrast and blood draw?`	Subject: OR Schedule Conflict: Contrast Protocol Coordination Needed[object Object]Date: 2026-01-12T21:21:00[object Object]From: Emily T. O'Brien[object Object]Participants: Carlos J. Rodriguez[object Object][object Object]Body:[object Object]Hi Carlos,[object Object][object Object]I've noticed a scheduling overlap for tomorrow's 10:00 AM ortho case—the patient is listed for both a CT with IV contrast and a blood draw in the OR holding area at the same time. To avoid protocol delays and ensure contrast safety, could you shift the phlebotomy slot to before 9:30 AM? Let me know if that works or if you need me to adjust the CT start time instead.[object Object][object Object]Thanks for collaborating on this.[object Object][object Object]Best,[object Object]Emily

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

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

do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: None
warmup_ratio: None
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
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_for_metrics: []
eval_do_concat_batches: True
auto_find_batch_size: False
full_determinism: False
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_num_input_tokens_seen: no
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: True
use_cache: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	val_full_corpus_cosine_ndcg@10
1.0	149	0.8776
2.0	298	0.8866
3.0	447	0.8867

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.3
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.12.0
Datasets: 4.0.0
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