--- tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:1298215 - loss:SpladeLoss - loss:SparseMultipleNegativesRankingLoss - loss:FlopsLoss base_model: distilbert/distilbert-base-multilingual-cased widget: - text: '[Clan London] Women''s Logo Tote Bag Olive (Olive) | حقيبة توت بشعار العلامة التجارية للنساء، زيتي (زيتي). Category: Bags > Shoppers & Totes.' - text: '[Fire & Glory] Women''s Animal Print Midi Dress Light Gray (Light Gray) | فستان ميدي بأكمام طويلة وطبعة جرافيك للنساء، أسود (رمادي فاتح). Category: Dresses > Midi Dresses.' - text: '[melissa] Melissa Becky AD Pink (Pink) | ميليسا بيكي إيه دي (وردي). Category: Shoes > Slides & Flip-Flops.' - text: '[H&M] Curvy Ultra High-Rise Wide-Leg Jeans (Pastel Blue) | جينز كيرفي بخصر مرتفع جداً وأرجل واسعة (أزرق باستيل). Category: Denim > Jeans.' - text: '[Splash Basics] Flexi Comfort Drawstring A-line Midi Skirt Blue (Blue) | الرباط ألف خط تنورة ميدي (أزرق). Category: Bottoms > Skirts.' 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 = [ 'شنطة زهّانة', '[PANDORA] Aquarius Zodiac Charm (Silver) | دلاية برج الدلو (فضي). Category: > Accessories.', "[Hurley] Women's Crew Neck Solid Sweatshirt Dark Salmon (Dark Salmon) | سويت شيرت نسائي سادة بياقة دائرية، برتقالي (سلموني غامق). Category: Hoodies & Sweatshirt > Sweatshirts.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 119547] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 11.9930, 13.0235, 7.8521], # [ 13.0235, 491.0651, 65.4858], # [ 7.8521, 65.4858, 246.8788]]) ``` ## Training Details ### Training Datasets #### pairs * Dataset: pairs * Size: 1,292,307 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: 5,908 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 | |:----------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | don't call me jennyfer jumpsuits & bodysuits | [Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits. | [Ashita Fernandes] Kenza Cupro V-Neck Jumpsuit Royal Blue (Facebook Blue) \| كينزا جمبسوت كوبرو بفتحة رقبة V أزرق ملكي (أزرق فيسبوك). Category: Jumpsuits & Bodysuits > Jumpsuits. | | don't call me jennyfer jumpsuits & bodysuits | [Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits. | [Splash Intimates] Mesh Panel Shaping Bodysuit (Black) \| بودي سوت مشد بأجزاء شبكية (أسود). Category: Jumpsuits & Bodysuits > Body Suits. | | don't call me jennyfer jumpsuits & bodysuits | [Don't Call Me Jennyfer] Women's One Sleeve Bodysuit Green (Green) \| بودي سوت سادة بأكمام قصيرة للنساء، أخضر (أخضر). Category: Jumpsuits & Bodysuits > Body Suits. | [Ashita Fernandes] Kenza Cupro V-Neck Jumpsuit Royal Blue (Facebook Blue) \| كينزا جمبسوت كوبرو بفتحة رقبة V أزرق ملكي (أزرق فيسبوك). Category: Jumpsuits & Bodysuits > Jumpsuits. | * 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
Click to expand | Epoch | Step | Training Loss | |:------:|:-----:|:-------------:| | 0.0099 | 200 | 679.9589 | | 0.0197 | 400 | 1.7857 | | 0.0296 | 600 | 1.0238 | | 0.0394 | 800 | 0.8982 | | 0.0493 | 1000 | 0.8221 | | 0.0592 | 1200 | 0.7875 | | 0.0690 | 1400 | 0.7405 | | 0.0789 | 1600 | 0.7131 | | 0.0887 | 1800 | 0.6761 | | 0.0986 | 2000 | 0.6732 | | 0.1085 | 2200 | 0.6397 | | 0.1183 | 2400 | 0.6303 | | 0.1282 | 2600 | 0.6021 | | 0.1380 | 2800 | 0.5794 | | 0.1479 | 3000 | 0.5552 | | 0.1578 | 3200 | 0.5638 | | 0.1676 | 3400 | 0.5633 | | 0.1775 | 3600 | 0.5550 | | 0.1873 | 3800 | 0.5473 | | 0.1972 | 4000 | 0.5281 | | 0.2070 | 4200 | 0.5189 | | 0.2169 | 4400 | 0.5282 | | 0.2268 | 4600 | 0.5080 | | 0.2366 | 4800 | 0.5046 | | 0.2465 | 5000 | 0.5111 | | 0.2563 | 5200 | 0.4957 | | 0.2662 | 5400 | 0.4932 | | 0.2761 | 5600 | 0.5045 | | 0.2859 | 5800 | 0.5019 | | 0.2958 | 6000 | 0.4789 | | 0.3056 | 6200 | 0.4866 | | 0.3155 | 6400 | 0.4810 | | 0.3254 | 6600 | 0.4891 | | 0.3352 | 6800 | 0.4800 | | 0.3451 | 7000 | 0.4659 | | 0.3549 | 7200 | 0.4612 | | 0.3648 | 7400 | 0.4644 | | 0.3747 | 7600 | 0.4364 | | 0.3845 | 7800 | 0.4672 | | 0.3944 | 8000 | 0.4546 | | 0.4042 | 8200 | 0.4631 | | 0.4141 | 8400 | 0.4565 | | 0.4240 | 8600 | 0.4242 | | 0.4338 | 8800 | 0.4425 | | 0.4437 | 9000 | 0.4597 | | 0.4535 | 9200 | 0.4292 | | 0.4634 | 9400 | 0.4406 | | 0.4733 | 9600 | 0.4331 | | 0.4831 | 9800 | 0.4342 | | 0.4930 | 10000 | 0.4483 | | 0.5028 | 10200 | 0.4254 | | 0.5127 | 10400 | 0.4182 | | 0.5226 | 10600 | 0.4173 | | 0.5324 | 10800 | 0.4120 | | 0.5423 | 11000 | 0.4197 | | 0.5521 | 11200 | 0.4029 | | 0.5620 | 11400 | 0.4016 | | 0.5719 | 11600 | 0.4159 | | 0.5817 | 11800 | 0.4033 | | 0.5916 | 12000 | 0.4250 | | 0.6014 | 12200 | 0.4013 | | 0.6113 | 12400 | 0.4234 | | 0.6211 | 12600 | 0.4103 | | 0.6310 | 12800 | 0.4046 | | 0.6409 | 13000 | 0.4123 | | 0.6507 | 13200 | 0.4074 | | 0.6606 | 13400 | 0.4098 | | 0.6704 | 13600 | 0.4192 | | 0.6803 | 13800 | 0.3887 | | 0.6902 | 14000 | 0.4017 | | 0.7000 | 14200 | 0.3956 | | 0.7099 | 14400 | 0.3971 | | 0.7197 | 14600 | 0.3964 | | 0.7296 | 14800 | 0.3921 | | 0.7395 | 15000 | 0.3947 | | 0.7493 | 15200 | 0.3795 | | 0.7592 | 15400 | 0.3813 | | 0.7690 | 15600 | 0.3931 | | 0.7789 | 15800 | 0.3792 | | 0.7888 | 16000 | 0.3878 | | 0.7986 | 16200 | 0.3703 | | 0.8085 | 16400 | 0.3836 | | 0.8183 | 16600 | 0.3871 | | 0.8282 | 16800 | 0.3751 | | 0.8381 | 17000 | 0.4005 | | 0.8479 | 17200 | 0.3887 | | 0.8578 | 17400 | 0.3873 | | 0.8676 | 17600 | 0.3811 | | 0.8775 | 17800 | 0.3841 | | 0.8874 | 18000 | 0.3841 | | 0.8972 | 18200 | 0.3701 | | 0.9071 | 18400 | 0.3913 | | 0.9169 | 18600 | 0.3848 | | 0.9268 | 18800 | 0.3885 | | 0.9367 | 19000 | 0.3648 | | 0.9465 | 19200 | 0.3842 | | 0.9564 | 19400 | 0.3989 | | 0.9662 | 19600 | 0.3930 | | 0.9761 | 19800 | 0.3944 | | 0.9860 | 20000 | 0.3712 | | 0.9958 | 20200 | 0.3762 |
### Training Time - **Training**: 48.5 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} } ```