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 the current status of reviewing the attached document referenced by Jennifer Morrison for Margaret L. Hendricks?",
]
documents = [
    'Subject: See Attached Document\nDate: 2025-11-12T10:37:00\nFrom: Jennifer Morrison\nParticipants: Margaret L. Hendricks\n\nBody:\nHello Margaret,\n\nI am reaching out to provide you with the attached file as previously referenced in our correspondence. Please take a moment to review the attached document at your earliest convenience, as it contains important information relevant to your current inquiry. If you have any questions or require clarification regarding the content, do not hesitate to reach out to me by reply email or phone. I am committed to supporting you throughout this process and ensuring you have all necessary documentation for your review.\n\nKind regards,\nJennifer Morrison',
    'Subject: Confirmation of Attendance for Upcoming Meeting\nDate: 2025-12-08T12:46:00\nFrom: Barbara J. Young\nParticipants: Carol A. Campbell\n\nBody:\nHi Carol,\n\nThank you for reaching out regarding your attendance at the upcoming meeting. I can confirm that your slot is secured and you are listed on our schedule for Thursday at 10:00 AM. If you have any specific topics or materials you intend to present, please forward those to me so I can ensure the agenda accommodates your contributions. Feel free to notify me if there are any changes to your availability.\n\nRegards,\nBarbara\n\n--\nBarbara J. Young | Executive Office',
    "Subject: Re: Urgent: Suction Equipment Malfunction Impacting Medication Administration\nDate: 2025-10-20T15:51:00\nFrom: Karen M. Phillips\nParticipants: Taylor A. Richardson\n\nBody:\nHi Taylor,\n\nThank you for raising this concern so promptly. I recommend we temporarily review all patient regimens in the east wing, especially those involving oral and respiratory medications, to identify any high-risk cases where delays could impact drug absorption or safety. In the interim, please ensure that nursing staff document any instances where medication administration is delayed due to equipment issues. I will coordinate with pharmacy and clinical teams to develop interim precautions and will follow up once we've completed the assessment.\n\nBest regards,\nKaren",
]
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([[ 0.6719,  0.0349, -0.0535]], dtype=torch.bfloat16)

Evaluation

Metrics

Information Retrieval

Dataset: val_full_corpus
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8289
cosine_accuracy@3	0.9086
cosine_accuracy@5	0.9402
cosine_accuracy@10	0.9718
cosine_precision@1	0.8289
cosine_precision@3	0.3029
cosine_precision@5	0.188
cosine_precision@10	0.0972
cosine_recall@1	0.8289
cosine_recall@3	0.9086
cosine_recall@5	0.9402
cosine_recall@10	0.9718
cosine_ndcg@10	0.9003
cosine_mrr@10	0.8774
cosine_map@100	0.8793

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: 26.82 tokens
max: 73 tokens
min: 109 tokens
mean: 177.36 tokens
max: 470 tokens

Samples:

sentence_0	sentence_1
`Who is the selected EHR vendor for the upgrade?`	Subject: Upcoming Electronic Health Record System Upgrade: Strategic Overview & Impact[object Object]Date: 2025-09-08T15:43:00[object Object]From: Catherine R. Miller[object Object]Participants: All Hospital Staff[object Object][object Object]Body:[object Object]Dear St. Catherine's Regional Hospital Team,[object Object][object Object]I am pleased to announce that our hospital will be implementing a major upgrade to our Electronic Health Record (EHR) system, scheduled for Q4 this fiscal year. This strategic investment, reflective of our ongoing commitment to operational excellence, efficiency, and compliance, has been carefully budgeted at $1.2M. While this represents a significant outlay, our projected long-term savings on administrative costs and revenue cycle optimization are estimated to exceed $500K annually.[object Object][object Object]We evaluated several vendors to ensure our chosen solution will maximize data security, interoperability, and regulatory compliance. The transition is expected to have a direct positive effect on both patient outcomes and our financial performance. Training sessions and resource guide...
`What is the plan to develop a comprehensive documentation checklist to address anesthesia supply claim rejections caused by documentation gaps?`	Subject: Re: Claim Rejection Analysis: Supply Documentation Concern[object Object]Date: 2026-01-28T17:43:00[object Object]From: Maya S. Patel[object Object]Participants: Carol A. Campbell[object Object][object Object]Body:[object Object]Hi Carol,[object Object]Thank you for highlighting the ongoing issues with anesthesia supply claim rejections tied to documentation gaps. I agree this is a significant concern that requires immediate attention to prevent further disruptions with vendor reconciliations and ensure claims compliance. I’m available later this week to review our current documentation process in detail and collaborate on a comprehensive checklist. Please let me know your availability so we can set up a meeting.[object Object]Best regards,[object Object]Maya
`What decisions, owners, and timelines exist for implementing a prioritization protocol for lab draws due to limited isolation room capacity, including proposed criteria, scheduled collection intervals, and cross-functional actions with lab and nursing teams?`	Subject: Re: Isolation Room Capacity and Lab Processing Constraints[object Object]Date: 2025-12-29T17:47:00[object Object]From: David S. Wilson[object Object]Participants: Linda R. Taylor[object Object][object Object]Body:[object Object]Hi Linda,[object Object][object Object]Thank you for reaching out and bringing this concern to my attention. I completely understand the challenges that our limited isolation room capacity is causing, especially as it relates to delays with critical specimen processing. I agree that implementing a prioritization protocol for lab draws, coupled with scheduled collection intervals, could help mitigate some of these bottlenecks. Let's set up a meeting with the lab and nursing teams to review current workflows and identify actionable strategies to streamline urgent cases. In the meantime, please let me know if you have specific cases that require immediate intervention, and I'll coordinate with the necessary departments to ensure we address those promptly.[object Object][object Object]Looking forward to collaborating on this important issue.[object Object][object Object]Best regards,[object Object]David

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	0.8884
2.0	298	0.8970
3.0	447	0.9003

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.3
Transformers: 5.0.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
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