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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sparse-encoder |
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- sparse |
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- asymmetric |
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- inference-free |
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- splade |
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- generated_from_trainer |
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- dataset_size:9000 |
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- loss:SpladeLoss |
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- loss:SparseMultipleNegativesRankingLoss |
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- loss:FlopsLoss |
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- dataset_size:89000 |
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base_model: distilbert/distilbert-base-uncased |
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widget: |
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- text: Blank Neoprene Water Bottle Coolies (Variety Color 10 Pack) |
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- text: Dream Spa 3-way 8-Setting Rainfall Shower Head and Handheld Shower Combo (Chrome). |
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Use Luxury 7-inch Rain Showerhead or 7-Function Hand Shower for Ultimate Spa Experience! |
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- text: ¿Está disponible el nuevo iPhone 7 Plus? |
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- text: Naipo Back Massager Massage Chair Vibrating Car Seat Cushion for Back, Neck, |
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and Thigh with 8 Motor Vibrations 4 Modes 3 Speed Heating at Home Office Car |
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- text: Pizuna 400 Thread Count Cotton Fitted-Sheet Queen Size White 1pc, 100% Long |
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Staple Cotton Sateen Fitted Bed Sheet With All Around Elastic Deep Pocket Queen |
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Sheets Fit Up to 15Inch (White Fitted Sheet) |
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pipeline_tag: feature-extraction |
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library_name: sentence-transformers |
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metrics: |
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- dot_accuracy@1 |
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- dot_accuracy@3 |
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- dot_accuracy@5 |
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- dot_accuracy@10 |
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- dot_precision@1 |
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- dot_precision@3 |
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- dot_precision@5 |
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- dot_precision@10 |
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- dot_recall@1 |
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- dot_recall@3 |
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- dot_recall@5 |
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- dot_recall@10 |
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- dot_ndcg@10 |
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- dot_mrr@10 |
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- dot_map@100 |
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- query_active_dims |
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- query_sparsity_ratio |
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- corpus_active_dims |
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- corpus_sparsity_ratio |
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model-index: |
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- name: Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions |
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tuples |
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results: |
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- task: |
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type: sparse-information-retrieval |
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name: Sparse Information Retrieval |
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dataset: |
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name: NanoMSMARCO |
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type: NanoMSMARCO |
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metrics: |
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- type: dot_accuracy@1 |
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value: 0.3 |
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name: Dot Accuracy@1 |
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- type: dot_accuracy@3 |
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value: 0.58 |
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name: Dot Accuracy@3 |
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- type: dot_accuracy@5 |
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value: 0.66 |
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name: Dot Accuracy@5 |
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- type: dot_accuracy@10 |
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value: 0.76 |
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name: Dot Accuracy@10 |
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- type: dot_precision@1 |
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value: 0.3 |
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name: Dot Precision@1 |
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- type: dot_precision@3 |
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value: 0.19333333333333336 |
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name: Dot Precision@3 |
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- type: dot_precision@5 |
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value: 0.132 |
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name: Dot Precision@5 |
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- type: dot_precision@10 |
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value: 0.07600000000000001 |
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name: Dot Precision@10 |
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- type: dot_recall@1 |
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value: 0.3 |
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name: Dot Recall@1 |
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- type: dot_recall@3 |
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value: 0.58 |
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name: Dot Recall@3 |
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- type: dot_recall@5 |
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value: 0.66 |
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name: Dot Recall@5 |
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- type: dot_recall@10 |
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value: 0.76 |
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name: Dot Recall@10 |
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- type: dot_ndcg@10 |
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value: 0.5302210774188797 |
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name: Dot Ndcg@10 |
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- type: dot_mrr@10 |
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value: 0.45638095238095233 |
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name: Dot Mrr@10 |
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- type: dot_map@100 |
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value: 0.4675385567218492 |
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name: Dot Map@100 |
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- type: query_active_dims |
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value: 6.380000114440918 |
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name: Query Active Dims |
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- type: query_sparsity_ratio |
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value: 0.9997909704437966 |
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name: Query Sparsity Ratio |
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- type: corpus_active_dims |
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value: 813.6908569335938 |
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name: Corpus Active Dims |
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- type: corpus_sparsity_ratio |
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value: 0.9733408408055306 |
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name: Corpus Sparsity Ratio |
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--- |
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# Inference-free SPLADE distilbert-base-uncased trained on Natural-Questions tuples |
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This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. |
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## Model Details |
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### Model Description |
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- **Model Type:** Asymmetric Inference-free SPLADE Sparse Encoder |
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- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 30522 dimensions |
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- **Similarity Function:** Dot Product |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) |
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### Full Model Architecture |
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``` |
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SparseEncoder( |
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(0): Router( |
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(sub_modules): ModuleDict( |
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(query): Sequential( |
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(0): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=DistilBertTokenizerFast) |
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) |
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(document): Sequential( |
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(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'DistilBertForMaskedLM'}) |
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(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) |
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) |
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) |
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) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SparseEncoder |
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# Download from the 🤗 Hub |
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model = SparseEncoder("monkeypostulate/inference-free-splade-distilbert-base-uncased-nq") |
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# Run inference |
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queries = [ |
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"\u00bfHay una s\u00e1bana de algod\u00f3n ajustada disponible en tama\u00f1o queen?", |
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] |
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documents = [ |
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'Pizuna 400 Thread Count Cotton Fitted-Sheet Queen Size White 1pc, 100% Long Staple Cotton Sateen Fitted Bed Sheet With All Around Elastic Deep Pocket Queen Sheets Fit Up to 15Inch (White Fitted Sheet)', |
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'ArtSocket Shower Curtain Teal Rustic Shabby Country Chic Blue Curtains Wood Rose Home Bathroom Decor Polyester Fabric Waterproof 72 x 72 Inches Set with Hooks', |
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'AFARER Case Compatible with Samsung Galaxy S7 5.1 inch, Military Grade 12ft Drop Tested Protective Case with Kickstand,Military Armor Dual Layer Protective Cover - Blue', |
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] |
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query_embeddings = model.encode_query(queries) |
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document_embeddings = model.encode_document(documents) |
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print(query_embeddings.shape, document_embeddings.shape) |
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# [1, 30522] [3, 30522] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(query_embeddings, document_embeddings) |
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print(similarities) |
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# tensor([[13.2777, 7.2952, 2.9255]]) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Sparse Information Retrieval |
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* Dataset: `NanoMSMARCO` |
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* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) |
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| Metric | Value | |
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|:----------------------|:-----------| |
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| dot_accuracy@1 | 0.3 | |
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| dot_accuracy@3 | 0.58 | |
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| dot_accuracy@5 | 0.66 | |
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| dot_accuracy@10 | 0.76 | |
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| dot_precision@1 | 0.3 | |
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| dot_precision@3 | 0.1933 | |
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| dot_precision@5 | 0.132 | |
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| dot_precision@10 | 0.076 | |
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| dot_recall@1 | 0.3 | |
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| dot_recall@3 | 0.58 | |
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| dot_recall@5 | 0.66 | |
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| dot_recall@10 | 0.76 | |
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| **dot_ndcg@10** | **0.5302** | |
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| dot_mrr@10 | 0.4564 | |
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| dot_map@100 | 0.4675 | |
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| query_active_dims | 6.38 | |
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| query_sparsity_ratio | 0.9998 | |
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| corpus_active_dims | 813.6909 | |
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| corpus_sparsity_ratio | 0.9733 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 89,000 training samples |
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* Columns: <code>query</code> and <code>document</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | query | document | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 21.52 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.4 tokens</li><li>max: 93 tokens</li></ul> | |
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* Samples: |
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| query | document | |
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|:-------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>¿Hay una lámpara de colgar con batería disponible?</code> | <code>Farmhouse Plug in Pendant Light with On/Off Switch Wire Caged Hanging Pendant Lamp 16ft Cord</code> | |
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| <code>¿Hay leggings con bolsillos disponibles para mujeres?</code> | <code>IUGA High Waist Yoga Pants with Pockets, Tummy Control, Workout Pants for Women 4 Way Stretch Yoga Leggings with Pockets</code> | |
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| <code>¿Cuál es la tapa de oscuridad marrón disponible?</code> | <code>Thicken It 100% Scalp Coverage Hair Powder - DARK BROWN - Talc-Free .32 oz. Water Resistant Hair Loss Concealer. Naturally Thicker Than Hair Fibers & Spray Concealers</code> | |
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* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: |
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```json |
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{ |
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"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)", |
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"document_regularizer_weight": 0.003, |
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"query_regularizer_weight": 0 |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 1,000 evaluation samples |
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* Columns: <code>query</code> and <code>document</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | query | document | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 20.94 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 33.09 tokens</li><li>max: 79 tokens</li></ul> | |
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* Samples: |
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| query | document | |
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|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>¿Qué es un modelo anatómico del corazón?</code> | <code>Axis Scientific Heart Model, 2-Part Deluxe Life Size Human Heart Replica with 34 Anatomical Structures, Held Together with Magnets, Includes Mounted Display Base, Detailed Product Manual and Warranty</code> | |
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| <code>¿Hay un buscador de peces portátil disponible?</code> | <code>HawkEye Fishtrax 1C Fish Finder with HD Color Virtuview Display, Black/Red, 2" H x 1.6" W Screen Size</code> | |
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| <code>¿Hay un disfraz de Anna adulta de Frozen disponible para comprar?</code> | <code>Mitef Anime Cosplay Costume Princess Anna Fancy Dress with Shawl for Adult, L</code> | |
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* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: |
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```json |
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{ |
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"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)", |
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"document_regularizer_weight": 0.003, |
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"query_regularizer_weight": 0 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `learning_rate`: 2e-05 |
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- `warmup_ratio`: 0.1 |
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- `batch_sampler`: no_duplicates |
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- `router_mapping`: {'query': 'query', 'answer': 'document'} |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 3 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `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`: False |
|
|
- `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`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
- `router_mapping`: {'query': 'query', 'answer': 'document'} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | NanoMSMARCO_dot_ndcg@10 | |
|
|
|:------:|:----:|:-------------:|:-----------------------:| |
|
|
| 0.5747 | 200 | 3.33 | - | |
|
|
| 1.1494 | 400 | 2.755 | - | |
|
|
| -1 | -1 | - | 0.5302 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.9.6 |
|
|
- Sentence Transformers: 5.1.0 |
|
|
- Transformers: 4.55.0 |
|
|
- PyTorch: 2.8.0 |
|
|
- Accelerate: 1.10.0 |
|
|
- Datasets: 4.0.0 |
|
|
- Tokenizers: 0.21.4 |
|
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|
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## 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} |
|
|
} |
|
|
``` |
|
|
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