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tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:633174
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: distilbert/distilbert-base-multilingual-cased
widget:
- text: '[Styli] Wired Floral Lace Balconette Bra Pink (Pink) | حمالة صدر بالكونيت
دانتيل بتطريز زهري وحواف مقوسة بدون حشوة وسلكية مع لوحة مقصوصة مخيطة (وردي). Category:
Lingerie & Underwear > Bras.'
- text: '[SHAPES] OVERSIZED COMFORT HOODIE (Black) | هودي مريح كبير الحجم (أسود).
Category: > .'
- text: '[Splash] Linen Blend Polo T-shirt Blue (Blue) | Linen Blend Polo T-shirt
(أزرق). Category: Tops > Shirts & Button-Downs.'
- text: '[CAMPUS] Women''s Pastel Low Top Sneakers Beige (Beige) | أحذية رياضية نسائية
قصيرة بتدرجات الباستيل البارزة (بيج). Category: Shoes > Sneakers.'
- text: '[Karl Lagerfeld] Women''s Long Sleeve Printed Top Black (Black) | بلوزة مطبوعة
بالكامل بأكمام طويلة ورقبة دائرية للنساء، متعددة الألوان (أسود). Category: >
.'
pipeline_tag: feature-extraction
library_name: sentence-transformers
---
# SPLADE Sparse Encoder
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the pairs and triplets datasets using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 119547-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-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) <!-- at revision 45c032ab32cc946ad88a166f7cb282f58c753c2e -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 119547 dimensions
- **Similarity Function:** Dot Product
- **Supported Modality:** Text
- **Training Datasets:**
- pairs
- triplets
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### 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': 'DistilBertForMaskedLM'})
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'embedding_dimension': 119547})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
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 = [
'كندرة طولية مطبوعة رجالي',
"[Karl Lagerfeld] Women's Long Sleeve Printed Top Black (Black) | بلوزة مطبوعة بالكامل بأكمام طويلة ورقبة دائرية للنساء، متعددة الألوان (أسود). Category: > .",
'[Splash Fav] Regular Fit Twill Blazer Cream (Cream) | Regular Fit Twill Blazer with Button Closure (كريمي). Category: Suits > Blazers.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 119547]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 30.1986, 30.1103, 15.3038],
# [ 30.1103, 193.7718, 59.9433],
# [ 15.3038, 59.9433, 248.5168]])
```
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You can finetune this model on your own dataset.
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## Training Details
### Training Datasets
#### pairs
* Dataset: pairs
* Size: 605,436 training samples
* Columns: <code>query</code> and <code>positive</code>
* Approximate statistics based on the first 100 samples:
| | query | positive |
|:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| modality | text | text |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.33 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 51.14 tokens</li><li>max: 124 tokens</li></ul> |
* Samples:
| query | positive |
|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>men's watch analog</code> | <code>[CASIO] Leather Strap Analog Watch (Mint Green) \| ساعة انالوج جلد اصلي (أخضر نعناعي). Category: > .</code> |
| <code>black hoodie women</code> | <code>[Tribe of 6] Women's Logo Hooded Sweatshirt Black (Black) \| سويت شيرت نسائي سادة بأكمام طويلة وغطاء رأس، أسود (أسود). Category: Hoodies & Sweatshirt > Hoodies.</code> |
| <code>وشاح مخطط للنساء</code> | <code>[MANGO] Geometric Stripe Scarf Beige (Beige) \| وشاح بنقوش هندسية مخططة (بيج). Category: Accessories > Scarves.</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"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)",
"document_regularizer_weight": 3e-05,
"query_regularizer_weight": 5e-05
}
```
#### triplets
* Dataset: triplets
* Size: 27,738 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 100 samples:
| | query | positive | negative |
|:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| modality | text | text | text |
| details | <ul><li>min: 11 tokens</li><li>mean: 12.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 34 tokens</li><li>mean: 48.58 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 52.9 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:--------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>كندرة أونلاين بسروات زين</code> | <code>[Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts.</code> | <code>[Tchibo] Women's Checkered Pull-On Shorts Dark Blue (Dark Blue) \| شورت بنمط مربعات سهل الارتداء للنساء، أزرق (أزرق غامق). Category: > .</code> |
| <code>كندرة أونلاين بسروات زين</code> | <code>[Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts.</code> | <code>[Styli] Styli Set of 2 Ribbon Detail Socks Light Steel Blue (Light Steel Blue) \| طقم جوارب من قطعتين بتفاصيل شريط (أزرق فولاذي فاتح). Category: Shoes > Socks.</code> |
| <code>كندرة أونلاين بسروات زين</code> | <code>[Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts.</code> | <code>[Tchibo] Women's Checkered Pull-On Shorts Dark Blue (Dark Blue) \| شورت بنمط مربعات سهل الارتداء للنساء، أزرق (أزرق غامق). Category: > .</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"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)",
"document_regularizer_weight": 3e-05,
"query_regularizer_weight": 5e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `num_train_epochs`: 1
- `learning_rate`: 2e-05
- `warmup_steps`: 0.1
- `gradient_accumulation_steps`: 4
- `bf16`: True
- `dataloader_num_workers`: 2
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 16
- `num_train_epochs`: 1
- `max_steps`: -1
- `learning_rate`: 2e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch
- `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`: 4
- `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`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 8
- `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`: False
- `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`: 2
- `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_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0202 | 200 | 518.0875 |
| 0.0404 | 400 | 1.3120 |
| 0.0606 | 600 | 1.0065 |
| 0.0809 | 800 | 0.9330 |
| 0.1011 | 1000 | 0.8807 |
| 0.1213 | 1200 | 0.8054 |
| 0.1415 | 1400 | 0.7627 |
| 0.1617 | 1600 | 0.7404 |
| 0.1819 | 1800 | 0.7277 |
| 0.2022 | 2000 | 0.7013 |
| 0.2224 | 2200 | 0.6515 |
| 0.2426 | 2400 | 0.6359 |
| 0.2628 | 2600 | 0.6334 |
| 0.2830 | 2800 | 0.6284 |
| 0.3032 | 3000 | 0.6302 |
| 0.3234 | 3200 | 0.5996 |
| 0.3437 | 3400 | 0.5933 |
| 0.3639 | 3600 | 0.5846 |
| 0.3841 | 3800 | 0.5783 |
| 0.4043 | 4000 | 0.5907 |
| 0.4245 | 4200 | 0.5669 |
| 0.4447 | 4400 | 0.5768 |
| 0.4650 | 4600 | 0.5507 |
| 0.4852 | 4800 | 0.5403 |
| 0.5054 | 5000 | 0.5372 |
| 0.5256 | 5200 | 0.5382 |
| 0.5458 | 5400 | 0.5308 |
| 0.5660 | 5600 | 0.5453 |
| 0.5862 | 5800 | 0.5218 |
| 0.6065 | 6000 | 0.5058 |
| 0.6267 | 6200 | 0.5229 |
| 0.6469 | 6400 | 0.5245 |
| 0.6671 | 6600 | 0.5115 |
| 0.6873 | 6800 | 0.5023 |
| 0.7075 | 7000 | 0.5049 |
| 0.7278 | 7200 | 0.5155 |
| 0.7480 | 7400 | 0.4976 |
| 0.7682 | 7600 | 0.4965 |
| 0.7884 | 7800 | 0.4968 |
| 0.8086 | 8000 | 0.4950 |
| 0.8288 | 8200 | 0.4889 |
| 0.8490 | 8400 | 0.4918 |
| 0.8693 | 8600 | 0.4870 |
| 0.8895 | 8800 | 0.4723 |
| 0.9097 | 9000 | 0.4808 |
| 0.9299 | 9200 | 0.4791 |
| 0.9501 | 9400 | 0.4829 |
| 0.9703 | 9600 | 0.4654 |
| 0.9905 | 9800 | 0.4931 |
### Training Time
- **Training**: 37.1 minutes
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 5.6.0
- Transformers: 5.12.1
- PyTorch: 2.7.0+cu128
- Accelerate: 1.14.0
- Datasets: 5.0.0
- Tokenizers: 0.22.2
## Additional Resources
- [Training and Finetuning Sparse Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sparse-encoder): the end-to-end guide for training or finetuning SPLADE and other sparse encoder models.
## 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}
}
```
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