This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the flash_rag_datasets 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: 32768 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- flash_rag_datasets
Language: en

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': 'PeftModelForFeatureExtraction'})
  (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("qwen3-embedding-0.6b_qwen3-8b_2wiki_lsr")
# Run inference
queries = [
    "Do both films Country (film) and Raid in St. Pauli have the directors that share the same nationality?",
]
documents = [
    'no',
    '13 October 1952',
    'yes',
]
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([[0.2584, 0.2811, 0.2387]])

Training Details

Training Dataset

flash_rag_datasets

Dataset: flash_rag_datasets at bcafb8d
Size: 15,000 training samples
Columns: query and response
Approximate statistics based on the first 1000 samples:
query response
type string string
details
min: 9 tokens
mean: 18.86 tokens
max: 38 tokens
min: 2 tokens
mean: 4.44 tokens
max: 15 tokens

Samples:

query	response
`Are director of film Move (1970 Film) and director of film Méditerranée (1963 Film) from the same country?`	`no`
`Do both films The Falcon (Film) and Valentin The Good have the directors from the same country?`	`no`
`Which film whose director is younger, Charge It To Me or Danger: Diabolik?`	`Danger: Diabolik`

Loss: fed_rag.loss.pytorch.lsr.LSRLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 1
gradient_accumulation_steps: 16
learning_rate: 1e-05
max_steps: 100
lr_scheduler_type: constant
remove_unused_columns: False
dataloader_pin_memory: False
push_to_hub: 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: 1
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 16
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 1e-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: 3.0
max_steps: 100
lr_scheduler_type: constant
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
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: False
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}
parallelism_config: 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
project: huggingface
trackio_space_id: trackio
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: False
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: True
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: 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
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.0011	1	0.0005
0.0021	2	0.0006
0.0032	3	0.0004
0.0043	4	0.0003
0.0053	5	0.0008
0.0064	6	0.0003
0.0075	7	0.0005
0.0085	8	0.0005
0.0096	9	0.0002
0.0107	10	0.0005
0.0117	11	0.0005
0.0128	12	0.0004
0.0139	13	0.0005
0.0149	14	0.0004
0.016	15	0.0002
0.0171	16	0.0005
0.0181	17	0.0006
0.0192	18	0.0004
0.0203	19	0.0004
0.0213	20	0.0002
0.0224	21	0.0003
0.0235	22	0.0004
0.0245	23	0.0006
0.0256	24	0.0004
0.0267	25	0.0003
0.0277	26	0.0006
0.0288	27	0.0003
0.0299	28	0.0006
0.0309	29	0.0006
0.032	30	0.0005
0.0331	31	0.0003
0.0341	32	0.0003
0.0352	33	0.0003
0.0363	34	0.0005
0.0373	35	0.0004
0.0384	36	0.0004
0.0395	37	0.0004
0.0405	38	0.0007
0.0416	39	0.0003
0.0427	40	0.0003
0.0437	41	0.0002
0.0448	42	0.0004
0.0459	43	0.0006
0.0469	44	0.0005
0.048	45	0.0003
0.0491	46	0.0006
0.0501	47	0.0005
0.0512	48	0.0004
0.0523	49	0.0007
0.0533	50	0.0006
0.0544	51	0.0005
0.0555	52	0.0004
0.0565	53	0.0004
0.0576	54	0.0006
0.0587	55	0.0005
0.0597	56	0.0003
0.0608	57	0.0003
0.0619	58	0.0004
0.0629	59	0.0004
0.064	60	0.0007
0.0651	61	0.0007
0.0661	62	0.0004
0.0672	63	0.0004
0.0683	64	0.0005
0.0693	65	0.0004
0.0704	66	0.0003
0.0715	67	0.0007
0.0725	68	0.0003
0.0736	69	0.0005
0.0747	70	0.0005
0.0757	71	0.0004
0.0768	72	0.0004
0.0779	73	0.0003
0.0789	74	0.0003
0.08	75	0.0007
0.0811	76	0.0007
0.0821	77	0.0006
0.0832	78	0.0006
0.0843	79	0.0002
0.0853	80	0.0004
0.0864	81	0.0008
0.0875	82	0.0005
0.0885	83	0.0005
0.0896	84	0.0004
0.0907	85	0.0004
0.0917	86	0.0006
0.0928	87	0.0007
0.0939	88	0.0006
0.0949	89	0.0004
0.096	90	0.0004
0.0971	91	0.0004
0.0981	92	0.0005
0.0992	93	0.0006
0.1003	94	0.0007
0.1013	95	0.0004
0.1024	96	0.0004
0.1035	97	0.0005
0.1045	98	0.0005
0.1056	99	0.0003
0.1067	100	0.0004

Framework Versions

Python: 3.11.14
Sentence Transformers: 5.2.0
Transformers: 4.57.2
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 4.5.0
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",
}

	query	response
type	string	string
details	min: 9 tokens mean: 18.86 tokens max: 38 tokens	min: 2 tokens mean: 4.44 tokens max: 15 tokens

zyc-zju/qwen3-embedding-0.6b_qwen3-8b_2wiki_lsr

SentenceTransformer based on Qwen/Qwen3-Embedding-0.6B