--- 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) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 119547 dimensions - **Similarity Function:** Dot Product - **Supported Modality:** Text - **Training Datasets:** - pairs - triplets ### 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]]) ``` ## Training Details ### Training Datasets #### pairs * Dataset: pairs * Size: 605,436 training samples * Columns: query and positive * Approximate statistics based on the first 100 samples: | | query | positive | |:---------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | modality | text | text | | details | | | * Samples: | query | positive | |:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | men's watch analog | [CASIO] Leather Strap Analog Watch (Mint Green) \| ساعة انالوج جلد اصلي (أخضر نعناعي). Category: > . | | black hoodie women | [Tribe of 6] Women's Logo Hooded Sweatshirt Black (Black) \| سويت شيرت نسائي سادة بأكمام طويلة وغطاء رأس، أسود (أسود). Category: Hoodies & Sweatshirt > Hoodies. | | وشاح مخطط للنساء | [MANGO] Geometric Stripe Scarf Beige (Beige) \| وشاح بنقوش هندسية مخططة (بيج). Category: Accessories > Scarves. | * Loss: [SpladeLoss](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: query, positive, and negative * Approximate statistics based on the first 100 samples: | | query | positive | negative | |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | modality | text | text | text | | details | | | | * Samples: | query | positive | negative | |:--------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | كندرة أونلاين بسروات زين | [Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts. | [Tchibo] Women's Checkered Pull-On Shorts Dark Blue (Dark Blue) \| شورت بنمط مربعات سهل الارتداء للنساء، أزرق (أزرق غامق). Category: > . | | كندرة أونلاين بسروات زين | [Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts. | [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. | | كندرة أونلاين بسروات زين | [Khizana] Denim A-line Skirt Steel Blue (Steel Blue) \| تنورة - من الدنيم (أزرق فولاذي). Category: Denim > Denim Skirts. | [Tchibo] Women's Checkered Pull-On Shorts Dark Blue (Dark Blue) \| شورت بنمط مربعات سهل الارتداء للنساء، أزرق (أزرق غامق). Category: > . | * Loss: [SpladeLoss](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
Click to expand - `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`: {}
### 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} } ```