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SPLADE for E-Commerce Search

Name: splade-ecommerce-esci
Availability: InStock

A SPLADE sparse encoder fine-tuned on Amazon ESCI for e-commerce product search. Achieves +28% improvement over BM25 on product retrieval tasks.

Benchmark Results

Amazon ESCI (In-Domain)

Model	nDCG@10	vs BM25
BM25 (baseline)	0.305	—
SPLADE (off-the-shelf)	0.326	+7%
This model	0.389	+28%

Cross-Domain Generalization

Dataset	nDCG@10	vs BM25
WANDS (Wayfair)	0.355	+8%
Home Depot	0.384	+10%

Model Description

This is a SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased on the esci dataset 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: SPLADE Sparse Encoder
Base model: distilbert/distilbert-base-uncased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 30522 dimensions
Similarity Function: Dot Product
Training Dataset:
- esci
Languages: en, ja, es

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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)

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("sparse_encoder_model_id")
# Run inference
sentences = [
    'magnetic screen door',
    '[Flux Phenom] | Flux Phenom Magnetic Screen Door - Retractable Mesh with Self Sealing Magnets - Keeps Nature Out | The Flux Phenom magnetic screen door is made for any household. The instant screen door installs in just a few minutes in any doorway of your home. It keeps irritants out, lets fresh air in, and allow | 🔨Installs in an Instant: Our magnetic door screen comes with everything you need to install it quickly; black all metal thumbtacks, a large roll of hook and loop backing, plus video tutorial | 🚪Fits Doorways Up to 38x82 Inches: Door net works on fixed, sliding, metal or wood doors as long as they measure up to 38x82 inches. Important: Measure your door before ordering to ensure fit | ↔️ Opens and Closes Like Magic: Our retractable screen door features a middle seam lined with 26 magnets for walking through any doorway with ease',
    '[HP] | HP VH240a 23.8-Inch Full HD 1080p IPS LED Monitor with Built-In Speakers and VESA Mounting, Rotating Portrait & Landscape, Tilt, and HDMI & VGA Ports (1KL30AA) - Black | RESOLUTION & PANEL — 23.8-inch Full HD monitor (1920 x 1080p at 60 Hz) with 16:9 aspect ratio and an anti-glare matte IPS LED-backlit panel (2 million pixels, 16.7 million colors) | RESPONSE TIME — 5ms with overdrive for a smooth picture that looks crisp and fluid without motion blur | BUILT-IN SPEAKERS — Integrated audio speakers provide great sound for your content (2 watts per channel)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 38.9048,  40.5171,  21.8987],
#         [ 40.5171, 177.9286,  54.3905],
#         [ 21.8987,  54.3905, 183.6391]])

Training Details

Training Dataset

esci

Dataset: esci at 8113b17
Size: 100,000 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 3 tokens
mean: 8.1 tokens
max: 34 tokens
min: 5 tokens
mean: 199.23 tokens
max: 383 tokens

Samples:

anchor	positive
`bathroom fan without light`	[Panasonic] \| Panasonic FV-20VQ3 WhisperCeiling 190 CFM Ceiling Mounted Fan \| WhisperCeiling fans feature a totally enclosed condenser motor and a double-tapered, dolphin-shaped bladed blower wheel to quietly move air \| Designed to give you continuous, trouble-free operation for many years thanks in part to its high-quality components and permanently lubricated motors which wear at a slower pace \| Detachable adaptors, firmly secured duct ends, adjustable mounting brackets (up to 26-in), fan/motor units that detach easily from the housing and uncomplicated wiring all lend themselves to user-friendly installation
`revent 80 cfm`	[Homewerks] \| Homewerks 7141-80 Bathroom Fan Integrated LED Light Ceiling Mount Exhaust Ventilation, 1.1 Sones, 80 CFM \| OUTSTANDING PERFORMANCE: This Homewerk's bath fan ensures comfort in your home by quietly eliminating moisture and humidity in the bathroom. This exhaust fan is 1.1 sones at 80 CFM which means it’s able to manage spaces up to 80 square feet and is very quiet.. \| BATH FANS HELPS REMOVE HARSH ODOR: When cleaning the bathroom or toilet, harsh chemicals are used and they can leave an obnoxious odor behind. Homewerk’s bathroom fans can help remove this odor with its powerful ventilation \| BUILD QUALITY: Designed to be corrosion resistant with its galvanized steel construction featuring a modern style round shape and has an 4000K Cool White Light LED Light. AC motor.
`revent 80 cfm`	[Homewerks] \| Homewerks 7140-80 Bathroom Fan Ceiling Mount Exhaust Ventilation, 1.5 Sones, 80 CFM, White \| OUTSTANDING PERFORMANCE: This Homewerk's bath fan ensures comfort in your home by quietly eliminating moisture and humidity in the bathroom. This exhaust fan is 1. 5 sone at 110 CFM which means it’s able to manage spaces up to 110 square feet \| BATH FANS HELPS REMOVE HARSH ODOR: When cleaning the bathroom or toilet, harsh chemicals are used and they can leave an obnoxious odor behind. Homewerk’s bathroom fans can help remove this odor with its powerful ventilation \| BUILD QUALITY: Designed to be corrosion resistant with its galvanized steel construction featuring a grille modern style.

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: 32
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True
batch_sampler: no_duplicates
router_mapping: {'anchor': 'query', 'positive': '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: 32
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: 2e-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: 1
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
bf16: False
fp16: True
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
project: huggingface
trackio_space_id: trackio
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: 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: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {'anchor': 'query', 'positive': 'document'}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.032	100	335.7698
0.064	200	1.6791
0.096	300	0.5408
0.128	400	0.4655
0.16	500	0.458
0.192	600	0.4366
0.224	700	0.3779
0.256	800	0.371
0.288	900	0.3352
0.32	1000	0.3661
0.352	1100	0.3196
0.384	1200	0.3385
0.416	1300	0.2944
0.448	1400	0.3257
0.48	1500	0.293
0.512	1600	0.3034
0.544	1700	0.2971
0.576	1800	0.2905
0.608	1900	0.2819
0.64	2000	0.2598
0.672	2100	0.2804
0.704	2200	0.2585
0.736	2300	0.2527
0.768	2400	0.2643
0.8	2500	0.2649
0.832	2600	0.2685
0.864	2700	0.2821
0.896	2800	0.2465
0.928	2900	0.2426
0.96	3000	0.2658
0.992	3100	0.2381

Framework Versions

Python: 3.11.10
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 4.4.1
Tokenizers: 0.22.1

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{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}
}

	anchor	positive
type	string	string
details	min: 3 tokens mean: 8.1 tokens max: 34 tokens	min: 5 tokens mean: 199.23 tokens max: 383 tokens

Qdrant/splade-ecommerce-esci

SPLADE for E-Commerce Search

Benchmark Results

Amazon ESCI (In-Domain)

Cross-Domain Generalization

Model Description

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

esci

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

SpladeLoss

SparseMultipleNegativesRankingLoss

FlopsLoss

Qdrant/
splade-ecommerce-esci