This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-4B on the flash_rag_datasets dataset. It maps sentences & paragraphs to a 2560-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-4B
Maximum Sequence Length: 40960 tokens
Output Dimensionality: 2560 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': 40960, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Pooling({'word_embedding_dimension': 2560, '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-4b_search-r1_hotpotqa_lsr")
# Run inference
queries = [
    "Huddersfield Giants R.L.F.C. are an English professional rugby league club from Huddersfield, West Yorkshire, the birthplace of rugby league, who play in the Super League competition, they play their home games at the Kirklees Stadium which is shared with Huddersfield Town F.C., is a multi-use sports stadium in Huddersfield in West Yorkshire, in which country?",
]
documents = [
    'England',
    'Alan Menken',
    'lead singer',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 2560] [3, 2560]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.2828, 0.1562, 0.1402]])

Training Details

Training Dataset

flash_rag_datasets

Dataset: flash_rag_datasets at bcafb8d
Size: 90,447 training samples
Columns: query and response
Approximate statistics based on the first 1000 samples:
query response
type string string
details
min: 8 tokens
mean: 25.45 tokens
max: 147 tokens
min: 2 tokens
mean: 4.93 tokens
max: 73 tokens

Samples:

query	response
`Which magazine was started first Arthur's Magazine or First for Women?`	`Arthur's Magazine`
`The Oberoi family is part of a hotel company that has a head office in what city?`	`Delhi`
`Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?`	`President Richard Nixon`

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
0.0002	1	0.0009
0.0004	2	0.0009
0.0005	3	0.0006
0.0007	4	0.0005
0.0009	5	0.001
0.0011	6	0.0004
0.0012	7	0.0008
0.0014	8	0.0009
0.0016	9	0.0011
0.0018	10	0.0014
0.0019	11	0.0012
0.0021	12	0.0015
0.0023	13	0.0009
0.0025	14	0.0005
0.0027	15	0.0005
0.0028	16	0.0013
0.0030	17	0.001
0.0032	18	0.0005
0.0034	19	0.0005
0.0035	20	0.001
0.0037	21	0.0007
0.0039	22	0.0015
0.0041	23	0.0013
0.0042	24	0.0014
0.0044	25	0.0009
0.0046	26	0.0012
0.0048	27	0.0011
0.0050	28	0.0011
0.0051	29	0.0007
0.0053	30	0.0007
0.0055	31	0.0011
0.0057	32	0.0014
0.0058	33	0.0006
0.0060	34	0.0008
0.0062	35	0.0012
0.0064	36	0.0006
0.0065	37	0.0008
0.0067	38	0.0006
0.0069	39	0.0008
0.0071	40	0.0005
0.0073	41	0.0008
0.0074	42	0.0008
0.0076	43	0.0013
0.0078	44	0.0005
0.0080	45	0.0009
0.0081	46	0.0007
0.0083	47	0.001
0.0085	48	0.0009
0.0087	49	0.001
0.0088	50	0.001
0.0090	51	0.0015
0.0092	52	0.0006
0.0094	53	0.0009
0.0096	54	0.0009
0.0097	55	0.0012
0.0099	56	0.0007
0.0101	57	0.0006
0.0103	58	0.0006
0.0104	59	0.0006
0.0106	60	0.0005
0.0108	61	0.0004
0.0110	62	0.0008
0.0111	63	0.001
0.0113	64	0.0012
0.0115	65	0.0011
0.0117	66	0.001
0.0119	67	0.0011
0.0120	68	0.0011
0.0122	69	0.0012
0.0124	70	0.0008
0.0126	71	0.0006
0.0127	72	0.0006
0.0129	73	0.0006
0.0131	74	0.0015
0.0133	75	0.0011
0.0134	76	0.0009
0.0136	77	0.0012
0.0138	78	0.0008
0.0140	79	0.0011
0.0142	80	0.0008
0.0143	81	0.0009
0.0145	82	0.0009
0.0147	83	0.0007
0.0149	84	0.0007
0.0150	85	0.0008
0.0152	86	0.0012
0.0154	87	0.001
0.0156	88	0.0003
0.0157	89	0.0003
0.0159	90	0.001
0.0161	91	0.0008
0.0163	92	0.0006
0.0165	93	0.0009
0.0166	94	0.0012
0.0168	95	0.0012
0.0170	96	0.0007
0.0172	97	0.0007
0.0173	98	0.001
0.0175	99	0.0008
0.0177	100	0.0007

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: 8 tokens mean: 25.45 tokens max: 147 tokens	min: 2 tokens mean: 4.93 tokens max: 73 tokens

zyc-zju/qwen3-embedding-4b_search-r1_hotpotqa_lsr

SentenceTransformer based on Qwen/Qwen3-Embedding-4B