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splade-ettin-encoder-17m trained on GooAQ

Name: splade-ettin-encoder-17m-gooaq
Availability: InStock

This is a SPLADE Sparse Encoder model finetuned from jhu-clsp/ettin-encoder-17m on the gooaq dataset using the sentence-transformers library. It maps sentences & paragraphs to a 50368-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: SPLADE Sparse Encoder
Base model: jhu-clsp/ettin-encoder-17m
Maximum Sequence Length: 256 tokens
Output Dimensionality: 50368 dimensions
Similarity Function: Dot Product
Supported Modality: Text
Training Dataset:
- gooaq
Language: en
License: mit

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): Transformer({'transformer_task': 'fill-mask', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'token_embeddings', 'architecture': 'ModernBertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 50368})
)

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("capemox/splade-ettin-encoder-17m-gooaq")
# Run inference
sentences = [
    'how close is the closest star?',
    'The two main stars are Alpha Centauri A and Alpha Centauri B, which form a binary pair. They are an average of 4.3 light-years from Earth. The third star is Proxima Centauri. It is about 4.22 light-years from Earth and is the closest star other than the sun.',
    'One gallon can of paint will cover up to 400 square feet, which is enough to cover a small room like a bathroom. Two gallon cans of paint cover up to 800 square feet, which is enough to cover an average size room. This is the most common amount needed, especially when considering second coat coverage.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 50368]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[144.9772, 158.3885, 140.6785],
#         [158.3885, 496.0056, 344.2902],
#         [140.6785, 344.2902, 528.3322]])

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoMSMARCO_256, NanoNFCorpus_256, NanoNQ_256, NanoClimateFEVER_256, NanoDBPedia_256, NanoFEVER_256, NanoFiQA2018_256, NanoHotpotQA_256, NanoMSMARCO_256, NanoNFCorpus_256, NanoNQ_256, NanoQuoraRetrieval_256, NanoSCIDOCS_256, NanoArguAna_256, NanoSciFact_256 and NanoTouche2020_256
Evaluated with SparseInformationRetrievalEvaluator with these parameters:
json
1{ 2 "max_active_dims": 256 3}

Metric	NanoMSMARCO_256	NanoNFCorpus_256	NanoNQ_256	NanoClimateFEVER_256	NanoDBPedia_256	NanoFEVER_256	NanoFiQA2018_256	NanoHotpotQA_256	NanoQuoraRetrieval_256	NanoSCIDOCS_256	NanoArguAna_256	NanoSciFact_256	NanoTouche2020_256
dot_accuracy@1	0.2	0.22	0.1	0.2	0.4	0.34	0.18	0.58	0.16	0.34	0.04	0.3	0.4898
dot_accuracy@3	0.34	0.34	0.28	0.38	0.66	0.7	0.3	0.7	0.38	0.5	0.32	0.44	0.7755
dot_accuracy@5	0.42	0.38	0.4	0.42	0.74	0.78	0.4	0.78	0.4	0.58	0.42	0.64	0.8776
dot_accuracy@10	0.6	0.52	0.54	0.48	0.84	0.92	0.44	0.84	0.46	0.72	0.48	0.7	0.9796
dot_precision@1	0.2	0.22	0.1	0.2	0.4	0.34	0.18	0.58	0.16	0.34	0.04	0.3	0.4898
dot_precision@3	0.1133	0.1933	0.0933	0.1267	0.3733	0.2333	0.14	0.32	0.1267	0.22	0.1067	0.1533	0.4218
dot_precision@5	0.084	0.18	0.08	0.092	0.348	0.156	0.124	0.216	0.08	0.18	0.084	0.14	0.4
dot_precision@10	0.06	0.14	0.058	0.066	0.324	0.094	0.074	0.126	0.048	0.138	0.048	0.078	0.3469
dot_recall@1	0.2	0.0069	0.09	0.085	0.0438	0.3067	0.0822	0.29	0.16	0.0727	0.04	0.275	0.034
dot_recall@3	0.34	0.0187	0.25	0.1783	0.0853	0.6567	0.1734	0.48	0.354	0.1377	0.32	0.42	0.0824
dot_recall@5	0.42	0.0306	0.36	0.2	0.1356	0.7367	0.2406	0.54	0.374	0.1857	0.42	0.61	0.1281
dot_recall@10	0.6	0.0458	0.52	0.26	0.2097	0.8767	0.2906	0.63	0.444	0.2837	0.48	0.68	0.2193
dot_ndcg@10	0.3714	0.1571	0.2925	0.2151	0.3792	0.5965	0.2237	0.5601	0.3147	0.2673	0.2567	0.4731	0.389
dot_mrr@10	0.302	0.2964	0.2323	0.2989	0.5519	0.5313	0.2532	0.6587	0.277	0.4424	0.1845	0.4177	0.6561
dot_map@100	0.3209	0.0466	0.2305	0.1742	0.2587	0.4997	0.1863	0.494	0.2826	0.1904	0.1898	0.4079	0.27
query_active_dims	252.1	255.62	256.0	256.0	253.96	256.0	240.22	256.0	229.0	256.0	256.0	256.0	216.8571
query_sparsity_ratio	0.995	0.9949	0.9949	0.9949	0.995	0.9949	0.9952	0.9949	0.9955	0.9949	0.9949	0.9949	0.9957
corpus_active_dims	256.0	256.0	256.0	256.0	256.0	256.0	256.0	256.0	234.9956	256.0	256.0	256.0	255.9241
corpus_sparsity_ratio	0.9949	0.9949	0.9949	0.9949	0.9949	0.9949	0.9949	0.9949	0.9953	0.9949	0.9949	0.9949	0.9949
avg_flops	104.6667	101.2659	114.127	129.3998	108.9664	120.7999	116.0173	114.6017	119.7198	123.3701	150.7188	129.6654	86.4117

Sparse Nano BEIR

Dataset: NanoBEIR_mean_256
Evaluated with SparseNanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "msmarco", 4 "nfcorpus", 5 "nq" 6 ], 7 "dataset_id": "sentence-transformers/NanoBEIR-en", 8 "max_active_dims": 256 9}

Metric	Value
dot_accuracy@1	0.18
dot_accuracy@3	0.3267
dot_accuracy@5	0.42
dot_accuracy@10	0.54
dot_precision@1	0.18
dot_precision@3	0.1356
dot_precision@5	0.1173
dot_precision@10	0.0867
dot_recall@1	0.0991
dot_recall@3	0.2158
dot_recall@5	0.2848
dot_recall@10	0.379
dot_ndcg@10	0.273
dot_mrr@10	0.2783
dot_map@100	0.1991
query_active_dims	254.54
query_sparsity_ratio	0.9949
corpus_active_dims	256.0
corpus_sparsity_ratio	0.9949
avg_flops	105.0486

Sparse Nano BEIR

Dataset: NanoBEIR_mean_256
Evaluated with SparseNanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "climatefever", 4 "dbpedia", 5 "fever", 6 "fiqa2018", 7 "hotpotqa", 8 "msmarco", 9 "nfcorpus", 10 "nq", 11 "quoraretrieval", 12 "scidocs", 13 "arguana", 14 "scifact", 15 "touche2020" 16 ], 17 "dataset_id": "sentence-transformers/NanoBEIR-en", 18 "max_active_dims": 256 19}

Metric	Value
dot_accuracy@1	0.2731
dot_accuracy@3	0.4704
dot_accuracy@5	0.5567
dot_accuracy@10	0.6554
dot_precision@1	0.2731
dot_precision@3	0.2017
dot_precision@5	0.1665
dot_precision@10	0.1231
dot_recall@1	0.1297
dot_recall@3	0.269
dot_recall@5	0.337
dot_recall@10	0.4261
dot_ndcg@10	0.3459
dot_mrr@10	0.3925
dot_map@100	0.2732
query_active_dims	249.2619
query_sparsity_ratio	0.9951
corpus_active_dims	254.1114
corpus_sparsity_ratio	0.995
avg_flops	109.026

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 99,000 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.93 tokens
max: 21 tokens
min: 15 tokens
mean: 58.27 tokens
max: 137 tokens

Samples:

question	answer
`what are the 5 characteristics of a star?`	`Key Concept: Characteristics used to classify stars include color, temperature, size, composition, and brightness.`
`are copic markers alcohol ink?`	`Copic Ink is alcohol-based and flammable. Keep away from direct sunlight and extreme temperatures.`
`what is the difference between appellate term and appellate division?`	`Appellate terms An appellate term is an intermediate appellate court that hears appeals from the inferior courts within their designated counties or judicial districts, and are intended to ease the workload on the Appellate Division and provide a less expensive forum closer to the people.`

Loss: SpladeLoss with these parameters:
json
1{ 2 "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)", 3 "document_regularizer_weight": 3e-05, 4 "query_regularizer_weight": 5e-05 5}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 1,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 12.05 tokens
max: 23 tokens
min: 15 tokens
mean: 58.98 tokens
max: 186 tokens

Samples:

question	answer
`should you take ibuprofen with high blood pressure?`	`In general, people with high blood pressure should use acetaminophen or possibly aspirin for over-the-counter pain relief. Unless your health care provider has said it's OK, you should not use ibuprofen, ketoprofen, or naproxen sodium. If aspirin or acetaminophen doesn't help with your pain, call your doctor.`
`how old do you have to be to work in sc?`	`The general minimum age of employment for South Carolina youth is 14, although the state allows younger children who are performers to work in show business. If their families are agricultural workers, children younger than age 14 may also participate in farm labor.`
`how to write a topic proposal for a research paper?`	`['Write down the main topic of your paper. ... ', 'Write two or three short sentences under the main topic that explain why you chose that topic. ... ', 'Write a thesis sentence that states the angle and purpose of your research paper. ... ', 'List the items you will cover in the body of the paper that support your thesis statement.']`

Loss: SpladeLoss with these parameters:
json
1{ 2 "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False, directions=('query_to_doc',), partition_mode='joint', hardness_mode=None, hardness_strength=0.0)", 3 "document_regularizer_weight": 3e-05, 4 "query_regularizer_weight": 5e-05 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
num_train_epochs: 1
learning_rate: 2e-05
bf16: True
per_device_eval_batch_size: 32
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

per_device_train_batch_size: 32
num_train_epochs: 1
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
per_device_eval_batch_size: 32
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
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
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoMSMARCO_256_dot_ndcg@10	NanoNFCorpus_256_dot_ndcg@10	NanoNQ_256_dot_ndcg@10	NanoBEIR_mean_256_dot_ndcg@10	NanoClimateFEVER_256_dot_ndcg@10	NanoDBPedia_256_dot_ndcg@10	NanoFEVER_256_dot_ndcg@10	NanoFiQA2018_256_dot_ndcg@10	NanoHotpotQA_256_dot_ndcg@10	NanoQuoraRetrieval_256_dot_ndcg@10	NanoSCIDOCS_256_dot_ndcg@10	NanoArguAna_256_dot_ndcg@10	NanoSciFact_256_dot_ndcg@10	NanoTouche2020_256_dot_ndcg@10
0.0323	100	954.7106	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	200	17.8241	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0970	300	4.5136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1293	400	2.5427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1616	500	1.4733	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1939	600	1.0940	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1972	610	-	0.8522	0.1440	0.0453	0.0771	0.0888	-	-	-	-	-	-	-	-	-	-
0.2262	700	0.7541	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2586	800	0.7425	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2909	900	0.5966	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3232	1000	0.5606	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3555	1100	0.5440	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3878	1200	0.4032	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3943	1220	-	0.4165	0.2494	0.0613	0.2065	0.1724	-	-	-	-	-	-	-	-	-	-
0.4202	1300	0.3995	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4525	1400	0.2976	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4848	1500	0.2971	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5171	1600	0.2716	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5495	1700	0.2577	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5818	1800	0.2370	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5915	1830	-	0.2104	0.3406	0.1173	0.2414	0.2331	-	-	-	-	-	-	-	-	-	-
0.6141	1900	0.2360	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6464	2000	0.2238	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6787	2100	0.2237	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7111	2200	0.2162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7434	2300	0.2044	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7757	2400	0.2202	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7886	2440	-	0.1736	0.3932	0.1545	0.2717	0.2731	-	-	-	-	-	-	-	-	-	-
0.8080	2500	0.1672	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8403	2600	0.2122	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8727	2700	0.1704	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	2800	0.1870	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9373	2900	0.1671	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9696	3000	0.1386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9858	3050	-	0.17	0.3714	0.1571	0.2925	0.2737	-	-	-	-	-	-	-	-	-	-
1.0	3094	-	0.1706	0.3675	0.1624	0.2892	0.2730	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.3714	0.1571	0.2925	0.3459	0.2151	0.3792	0.5965	0.2237	0.5601	0.3147	0.2673	0.2567	0.4731	0.3890

The bold row denotes the saved checkpoint.

Training Time

Training: 7.7 minutes
Evaluation: 2.0 minutes
Total: 9.7 minutes

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.4.1
Transformers: 5.5.4
PyTorch: 2.11.0+cu130
Accelerate: 1.13.0
Datasets: 4.8.4
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",
}

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{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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}
}

	question	answer
type	string	string
details	min: 8 tokens mean: 11.93 tokens max: 21 tokens	min: 15 tokens mean: 58.27 tokens max: 137 tokens