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Frinkleko/
opensearch-doc-v3-gte-finetune-limit-samples-2

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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": 0.82678,
        "NDCG@10": 0.86919,
        "NDCG@20": 0.87429,
        "NDCG@100": 0.87886
    },
    "MAP": {
        "MAP@2": 0.8145,
        "MAP@10": 0.84849,
        "MAP@20": 0.85042,
        "MAP@100": 0.85119
    },
    "Recall": {
        "Recall@2": 0.8235,
        "Recall@10": 0.905,
        "Recall@20": 0.9215,
        "Recall@100": 0.942
    },
    "Precision": {
        "P@2": 0.8235,
        "P@10": 0.181,
        "P@20": 0.09215,
        "P@100": 0.01884
    }
}

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 SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("Frinkleko/opensearch-project_opensearch-neural-sparse-encoding-doc-v3-gte-limit-samples-2")
# Run inference
queries = [
    "Which planet is known as the Red Planet?",
]
documents = [
    "Venus is often called Earth's twin because of its similar size and proximity.",
    'Mars, known for its reddish appearance, is often referred to as the Red Planet.',
    'Saturn, famous for its rings, is sometimes mistaken for the Red Planet.',
]
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([[ 6.8167, 15.0012, 13.5434]])

Training Details

Training Dataset

Unnamed Dataset

Size: 2 training samples
Columns: query and document
Approximate statistics based on the first 2 samples:
query document
type string string
details
min: 7 tokens
mean: 7.5 tokens
max: 8 tokens
min: 41 tokens
mean: 43.0 tokens
max: 45 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.`

Loss: SpladeLoss with these parameters:
json
1{ 2 "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)", 3 "document_regularizer_weight": 3e-05, 4 "query_regularizer_weight": 5e-05 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 1
num_train_epochs: 40
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: 1
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: 40
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

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",
}

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

bibtex

@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

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}
}

FlopsLoss

bibtex

@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}
}

	query	document
type	string	string
details	min: 7 tokens mean: 7.5 tokens max: 8 tokens	min: 41 tokens mean: 43.0 tokens max: 45 tokens

Frinkleko/opensearch-doc-v3-gte-finetune-limit-samples-2

Asymmetric Inference-free SPLADE Sparse Encoder

Model Details

Model Description

Model Sources

Full Model Architecture

Metrics

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Framework Versions

Citation

BibTeX

Sentence Transformers

LIMIT

OpenSearch Models

SpladeLoss

SparseMultipleNegativesRankingLoss

FlopsLoss

Frinkleko/
opensearch-doc-v3-gte-finetune-limit-samples-2