Asymmetric Inference-free SPLADE Sparse Encoder

This is a Asymmetric Inference-free SPLADE Sparse Encoder model finetuned from opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: Asymmetric Inference-free SPLADE Sparse Encoder
Base model: opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

Full Model Architecture

SparseEncoder(
  (0): Router(
    (sub_modules): ModuleDict(
      (query): Sequential(
        (0): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=DistilBertTokenizerFast)
      )
      (document): Sequential(
        (0): MLMTransformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'NewForMaskedLM'})
        (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'log1p_relu', 'word_embedding_dimension': 30522})
      )
    )
  )
)

Metrics

{
    "NDCG": {
        "NDCG@2": 0.90484,
        "NDCG@10": 0.91822,
        "NDCG@20": 0.9204,
        "NDCG@100": 0.92605
    },
    "MAP": {
        "MAP@2": 0.90125,
        "MAP@10": 0.91146,
        "MAP@20": 0.91216,
        "MAP@100": 0.91316
    },
    "Recall": {
        "Recall@2": 0.9045,
        "Recall@10": 0.931,
        "Recall@20": 0.938,
        "Recall@100": 0.963
    },
    "Precision": {
        "P@2": 0.9045,
        "P@10": 0.1862,
        "P@20": 0.0938,
        "P@100": 0.01926
    }
}

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 SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("Frinkleko/opensearch-project_opensearch-neural-sparse-encoding-doc-v3-gte-limit-samples-1700")
# Run inference
queries = [
    "Who likes Sonatas?",
]
documents = [
    ' Kamron Rose likes Landscaping, Spinning, Rugs, Model Building, Figure Skating, Extension Cords, Doctors, Sonatas, Owls.',
    ' Ege Kennedy likes Excitement, Tap Dancing, the Houston Astros, Agave Nectar, Cobras.',
    ' Codey Luna likes Bodyboarding, Keys, Tarantulas, Bonsai Trees, Balsamic Vinegar, Kale, Ticket to Ride, Ricotta, Tuning Forks, Silver, Sperm Whales, The Roaring Twenties, Manchego.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[16.2629,  6.6099,  5.9649]])

Training Details

Training Dataset

Unnamed Dataset

Size: 1,700 training samples
Columns: query and document
Approximate statistics based on the first 1000 samples:
query document
type string string
details
min: 6 tokens
mean: 7.37 tokens
max: 12 tokens

min: 19 tokens
mean: 44.42 tokens
max: 67 tokens

	query	document
type	string	string
details	min: 6 tokens mean: 7.37 tokens max: 12 tokens	min: 19 tokens mean: 44.42 tokens max: 67 tokens

Samples:

query	document
`Who likes Sunflowers?`	`Rasmus Logan likes Dark Chocolate, Documentary Series, Washing Machines, Softball, Sunflowers, Gregorian chants, Za'atar, Abacuses, Dolphins, Root Beer Floats, Cumin, Coconut Flour.`
`Who likes Shaved Ice?`	`Abdulkarem Boyer likes Stag Beetles, Acacia Trees, Olives, Landscape Photography, Neoclassicism, Guinea Pigs, Mentoring, Parsley, Chemistry, Vases, Shaved Ice.`
`Who likes Rock Balancing?`	`Tanay Melton likes Poetry Slams, Sperm Whales, Tonic Water, Bat Flowers, Rock Balancing.`

Loss: SpladeLoss with these parameters:

{
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
    "document_regularizer_weight": 3e-05,
    "query_regularizer_weight": 5e-05
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 85
num_train_epochs: 4
warmup_ratio: 0.1
batch_sampler: no_duplicates
router_mapping: {'query': 'query', 'document': 'document'}

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 85
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: 1.0
num_train_epochs: 4
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: 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}
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
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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {'query': 'query', 'document': 'document'}
learning_rate_mapping: {}

Framework Versions

Python: 3.12.9
Sentence Transformers: 5.1.0
Transformers: 4.56.0
PyTorch: 2.8.0+cu128
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.22.0

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",
}

LIMIT

@misc{weller2025theoreticallimit,
      title={On the Theoretical Limitations of Embedding-Based Retrieval}, 
      author={Orion Weller and Michael Boratko and Iftekhar Naim and Jinhyuk Lee},
      year={2025},
      eprint={2508.21038},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2508.21038}, 
}

OpenSearch Models

@inproceedings{Shen_2025, series={SIGIR ’25},
   title={Exploring $\ell_0$ parsification for Inference-free Sparse Retrievers},
   url={http://dx.doi.org/10.1145/3726302.3730192},
   DOI={10.1145/3726302.3730192},
   booktitle={Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
   publisher={ACM},
   author={Shen, Xinjie and Geng, Zhichao and Yang, Yang},
   year={2025},
   month=jul, pages={2572–2576},
   collection={SIGIR ’25} 
}

@misc{geng2025competitivesearchrelevanceinferencefree,
      title={Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers}, 
      author={Zhichao Geng and Yiwen Wang and Dongyu Ru and Yang Yang},
      year={2025},
      eprint={2411.04403},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2411.04403}, 
}

SpladeLoss

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}

SparseMultipleNegativesRankingLoss

@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}
}

FlopsLoss

@article{paria2020minimizing,
    title={Minimizing flops to learn efficient sparse representations},
    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
    journal={arXiv preprint arXiv:2004.05665},
    year={2020}
}

Frinkleko
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opensearch-doc-v3-gte-finetune-limit-samples-1700