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: 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Day1Kim/Qwen3-Embedding-0.6B-Korean")
# Run inference
queries = [
    "\uc5b5\uc9c0\ub9d0\ub69d\uc73c\ub85c \ubcf4\uac15\ub41c \ube44\ud0c8\uba74\uc758 \ub0b4\uc9c4\uc124\uacc4 \uacb0\uc815 \uae30\uc900\uc740 \ubb34\uc5c7\uc778\uac00\uc694?",
]
documents = [
    '비탈면보강공법 KDS117015:2020KDS110000지반설계기준 9입시켜야한다 .4.3.3내진설계여부(1)억지말뚝으로보강된비탈면의내진설계는보강되지않은비탈면의내진설계여부에따라결정하며 ,KDS 119000의비탈면내진등급을참고한다 .(2)억지말뚝으로보강된비탈면의지진시안정해석은 4.4및KDS 119000을참조한다 .4.4지진시안정해석4.4.1네일(1)지진시네일로보강된비탈면의안정해석에서는내적안정과외적안정성을검토한다 .(2)네일로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며 ,파괴토체의중심에횡방향으로작용시킨다 .(3)지진에의한지진계수는 KDS 119000(1.6.5)에서제시하는유효수평지반가 속도(S)를이용하여산정한다 .4.4.2록볼트(1)지진시록볼트로보강된비탈면의안정해석에서는외적안정성을검토한다 .(2)록볼트로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며 ,파괴토체의중심에횡방향으로작용시킨다 .',
    '지반계측 KDS111015:2021KDS110000지반설계기준 344.2.2.2 계측기기운용기법(1) 인력에의한계측기기운용과자동화장비에의한운용기법으로크게구분할수있으며, 붕괴및활동의진행특성, 계측대상시설물의중요도 , 피해발생시영향, 경제성, 계측빈도등을고려하여운용기법을선택하여야한다.4.2.2.3 일반적인계측관리의자동화(1) 기록지또는저장장치에계측자료를기록할때까지를자동화하고 , 그후의처리는별도로컴퓨터로실시하는반자동계측관리기법과 , 자료수집ㆍ해석ㆍ그래프화까지를유선ㆍ무선으로온라인화된시스템으로일관하여실시하는전자동계측관리기법및상기의두가지방법을병용하는기법으로구분하며 , 계측대상조건을고려하여운용기법을선정하여야한다.4.2.2.4 피해방지및최소화방법(1) 조기에징후를감지하는것이중요하고 , 모니터링과동시에신속하게그정보를전달ㆍ처리하는것이필요하며계측자료의수집ㆍ처리ㆍ해석까지를일괄하여처리하는자동화기술을사용하는것을고려하여야한다.',
    '(9)매립지반에설치하는기초는특별히주의하여야하는데 ,기초하부의접지압력은가급적균등하게분포하도록설계하고 ,접지압력의분포는흙또는암,그리고구조물의특성에따라달라지며 ,토질역학및암석역학적원리들과일치하여야한다 .2.조사및계획∙내용없음 .3.재료∙내용없음 .4.설계4.1지지력산정4.1.1지지력산정을위한고려사항(1)기초설계시시추조사 ,현장및실내시험을통하여지반특성을파악한후지지력을산정한다 .그러나상재하중이작은구조물또는가설구조물의기초는인근구조물의경험값,기초설계및시공성과 ,현장시험자료를통하여지지력을추정할수있다 .',
]
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([[ 0.5634, -0.1310,  0.1097]])

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5509
cosine_accuracy@3	0.7131
cosine_accuracy@5	0.7673
cosine_accuracy@10	0.837
cosine_precision@1	0.5509
cosine_precision@3	0.2377
cosine_precision@5	0.1535
cosine_precision@10	0.0837
cosine_recall@1	0.5509
cosine_recall@3	0.7131
cosine_recall@5	0.7673
cosine_recall@10	0.837
cosine_ndcg@10	0.6912
cosine_mrr@10	0.6449
cosine_map@100	0.6517

Training Details

Training Dataset

Unnamed Dataset

Size: 41,881 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: 29.79 tokens
max: 67 tokens

min: 15 tokens
mean: 271.15 tokens
max: 577 tokens

	sentence_0	sentence_1
type	string	string
details	min: 10 tokens mean: 29.79 tokens max: 67 tokens	min: 15 tokens mean: 271.15 tokens max: 577 tokens

Samples:

sentence_0	sentence_1
`KDS 10 00 00 설계기준은 어느 나라의 표준인가요?`	`KDS 10 00 00설계기준 Korean Design StandardKDS 10 00 00 : 2021공통설계기준.2021년5월12일개정http://www.kcsc.re.kr`
`KDS 10 00 00 설계기준은 최근에 언제 개정되었나요?`	`KDS 10 00 00설계기준 Korean Design StandardKDS 10 00 00 : 2021공통설계기준.2021년5월12일개정http://www.kcsc.re.kr`
`KDS 10 10 00 설계총칙 문서는 어떤 분야의 설계 기준을 다루고 있나요?`	공통설계기준체계KDS 10 10 00 설계총칙 `21.05

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
num_train_epochs: 13
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 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: 13
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}
tp_size: 0
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
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
router_mapping: {}
learning_rate_mapping: {}

Training Logs