SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B

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: 1024 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': 1024, '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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
    "Instruct: \nQuery:  It is branded as Dynorm, Inhibace, Vascace and many other names in various countries. None of these are available in the United States as of May 2010.",
]
documents = [
    'Instruct: \nQuery:  Enantiopure drugs',
    'Instruct: \nQuery:  Bioluminescence',
    'Instruct: \nQuery:  Electrochemists',
]
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([[0.4157, 0.1706, 0.1681]])

Training Details

Training Dataset

Unnamed Dataset

Size: 2,976 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 10 tokens
mean: 228.29 tokens
max: 1024 tokens

min: 10 tokens
mean: 28.26 tokens
max: 595 tokens

	anchor	positive
type	string	string
details	min: 10 tokens mean: 228.29 tokens max: 1024 tokens	min: 10 tokens mean: 28.26 tokens max: 595 tokens

Samples:

anchor	positive
`Instruct: Query: Control Measures`
*
*
Contractor Summary
*
*
Ingredients
*
% Wt: <0.1
OSHA PEL: C 0.1 MG(CRO3)/M3
ACGIH TLV: 0.5 MG(CR)/M3, A4
------------------------------
OSHA PEL: N/K (FP N)
ACGIH TLV: N/K (FP N)
------------------------------
OSHA PEL: N/K (FP N)
ACGIH TLV: N/K (FP N)
-----------------------------
GUIDELINES ARE BASED ON FEDERAL REGS &
-----------------------------
PLEASE CONSULT LOCAL ENVIRONMENTAL
-----------------------------
*
Health Hazards Data
*
Route Of Entry Inds - Inhalation: YES
Skin: YES
Ingestion: YES
Carcinogenicity Inds - NTP: YES
IARC: YES
OSHA: NO
Effects of Exposure: ACUTE: EYE CONTACT, SKIN CONTACT, SKIN ABSORP: NO EFFECTS
ANTICIPATED. INGESTION: PRACTICALLY NON-TOXIC. INHALATION: NO EFFECTS
ANTICIPATED. POTASSIUM DICHROMATE: CARCINOGEN. EXPERMENTAL MUTAGEN. EXPE
RIMENTAL TERATOGEN. CAUSES BURNS. OXIDIZER. TOXIC. MAY CAUSE ALLERGIC
REACTION. CHRONIC EFFECTS: NO EFTS ANTICIPATED.
Signs And S...	`Instruct: Query: eyes_protection_mandatory`
Instruct: Query: * Exposure Controls/Personal Protection Respiratory Protection:FOLLOW THE OSHA RESPIRATOR REGULATIONS FOUND IN NECESSARY. Ventilation:USE ADEQUATE GENERAL OR LOCAL EXHAUST VENTILATION TO KEEP AIRBORNE CONCENTRATIONS BELOW THE PERMISSIBLE EXPOSURE LIMITS. Other Protective Equipment:ANSI APPROVED EMERGENCY EYEWASH AND DELUGE SHOWER . WEAR APPROPRIATE PROTECTIVE CLOTHING TO PREVENT SKIN EXPOSURE. Work Hygienic Practices:WASH THOROUGHLY AFTER HANDLING. REMOVE CONTAMINATED CLOTHING AND WASH BEFORE REUSE. Supplemental Safety and Health MATLS TO AVOID: MATERIALS AND COMBUSTIBLES. FIRST AID PROC: IMMED. IF NOT BRTHG, GIVE ARTF RESP. IF BRTHG IS DFCLT, GIVE OXYGEN. GET MED AID. NOTES TO MD: TREAT SYMPTOMATICALLY & SUPPORTIVELY. NO SPEC IFIC ANTIDOTE EXISTS. Product Identification Composition/Information on Ingredients OSHA PEL:2 PPM ACGIH TLV:2 PPM/4 STEL OSHA PEL:N/K ACGIH TLV:N/K Hazards Identification * Routes of Entry:...	`Instruct: Query: gloves_mandatory`
Instruct: Query: The isomerization of uridine to pseudoridine is the second most common rRNA modification. These pseudoridines are also introduced by the same classes of snoRNPs that participate in methylation. Psuedouridine synthases are the major participating enzymes in the reaction. The H/ACA box snoRNPs introduce guide sequences that are about 14-15 nucleotides long. Pseudouridylation is triggered in numerous places of rRNAs at once to preserve the thermal stability of RNA. Pseudouridine allows for increased hydrogen bonding and alters translation in rRNA and tRNA. It alters translation by increasing the affinity of the ribosome subunit to specific mRNAs. Base Editing: Base editing is the third major class of rRNA modification, specifically in eukaryotes. There are 8 categories of base edits that can occur at the gap between the small and large ribosomal subunits. RNA methyltransferases are the enzymes that introduce base methylation. Acetyltransferases are the enzymes responsib...	`Instruct: Query: Gene expression + Signal Transduction`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

learning_rate: 0.0001
num_train_epochs: 1
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 8
per_device_eval_batch_size: 8
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: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
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: True
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_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: proportional
router_mapping: {}
learning_rate_mapping: {}

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.1.0
Transformers: 4.55.0
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 3.6.0
Tokenizers: 0.21.4

Citation

BibTeX

Sentence Transformers

@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

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}