This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the json dataset. 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: 256 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json

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, '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("b00l26/Qwen3-Embedding-0.6B-finetune-news")
# Run inference
queries = [
    "M\u1ef9 \u0111i\u1ec1u_tra tu\u00e2n_th\u1ee7 tho\u1ea3_thu\u1eadn th\u01b0\u01a1ng_m\u1ea1i Trung_Qu\u1ed1c . V\u0103n_ph\u00f2ng \u0110\u1ea1i_di\u1ec7n Th\u01b0\u01a1ng_m\u1ea1i M\u1ef9 ( USTR ) \u0111i\u1ec1u_tra tu\u00e2n_th\u1ee7 tho\u1ea3_thu\u1eadn th\u01b0\u01a1ng_m\u1ea1i Trung_Qu\u1ed1c k\u00fd_k\u1ebft 2020 .",
]
documents = [
    "Trung_Quốc đồng_ý đàm_phán thương_mại ' ' Mỹ . Trung_Quốc đồng_ý tiến_hành vòng đàm_phán thương_mại Mỹ   .",
    'Thời_điểm đẹp Hà_Giang ngắm tam_giác mạch ? . Du_khách Hà_Nội ngắm hoa tam_giác mạch Hà_Giang , tư_vấn hoa nở_rộ , rực_rỡ .',
    'Bất_động_sản Nam trở_lại đường_đua sáp_nhập địa_giới hành_chính . ( Dân_trí ) - Trong cung lõi TPHCM khan_hiếm khu_vực lân_cận Bình_Dương , Long_An Đồng_Nai ( cũ ) dồi_dào , đa_dạng phân khúc .',
]
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([[ 0.6408,  0.0052, -0.0460]])

Training Details

Training Dataset

json

Dataset: json
Size: 26,696 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 21 tokens
mean: 66.23 tokens
max: 119 tokens
min: 26 tokens
mean: 67.17 tokens
max: 124 tokens

Samples:

anchor	positive
`Nam_Định : Thuê thần đèn di_dời 500 đường điện . ( Dân_trí ) - Tiếc 2 tầng xây_dựng khả_năng dỡ , Thuế mua chi nửa tỷ đồng thuê thần đèn di_chuyển đất .`	`Mục_sở_thị mỏng Vương_quốc_Anh rao 1,3 triệu USD . Ngôi nằm ga tàu_điện_ngầm Goldhawk_Road tây London , rộng 6 mét , 5 tầng tổng diện_tích 1.034 mét_vuông . Tuy mỏng , rao giá 950.000 bảng Anh ( 1,3 triệu USD ) .`
`Trang_trí đám_cưới , đám_hỏi kiêng sen trắng cành trúc ? . Theo chuyên_gia văn_hoá , không_gian chùa_chiền , đám hiếu ngược đám_cưới đám_cưới . Tuy_nhiên , , quan_niệm dần .`	`Đại_gia TPHCM chi tiền tỷ tổ_chức đám_cưới bảo_mẫu . ( Dân_trí ) - Vì đem nữ bảo_mẫu gia_đình đám_cưới , nữ đại_gia TPHCM chi tiền tổ_chức tiệc cưới bảo_mẫu .`
`Đệ phu_nhân Pháp chỉ_trích miệt_thị biểu_tình . Những nổi_tiếng chính_trị_gia cánh tả Pháp bày_tỏ phẫn_nộ Brigitte_Macron cụm_từ lũ đàn_bà ngu_ngốc hoạt_động nữ_quyền .`	`Đệ phu_nhân Pháp ám_ảnh đồn giới_tính . Auziere , gái của Đệ nhất phu_nhân Pháp , nói rằng mẹ lo_âu sâu_sắc xoay quanh ̃ng đồn thất thiệt về giới tính .`

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

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
gradient_accumulation_steps: 2
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True
dataloader_num_workers: 4
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 2
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: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 4
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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.4785	100	1.2754
0.9569	200	0.6613

Framework Versions

Python: 3.10.19
Sentence Transformers: 5.2.0
Transformers: 4.51.0
PyTorch: 2.1.2+cu118
Accelerate: 0.34.2
Datasets: 4.2.0
Tokenizers: 0.21.4

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

bibtex

@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}
}

	anchor	positive
type	string	string
details	min: 21 tokens mean: 66.23 tokens max: 119 tokens	min: 26 tokens mean: 67.17 tokens max: 124 tokens