| --- |
| 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]]) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
