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--- |
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language: |
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- en |
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license: mit |
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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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- splade |
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- generated_from_trainer |
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- dataset_size:496123 |
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- loss:SpladeLoss |
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- loss:SparseMultipleNegativesRankingLoss |
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- loss:FlopsLoss |
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base_model: prajjwal1/bert-medium |
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widget: |
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- text: What is the name, background and ethnicity of the actress who plays Raj’s |
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sister Priya on “The Big Bang Theory”? —Charles Dix, Stewartsville, Mo. Aarti |
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Mann, 36, a first-generation Indian American, was born in Connecticut and raised |
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in Pennsylvania, and plays Priya Koothrappali on “The Big Bang Theory.”. Of landing |
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the role as Raj’s sister, she says, “It is like winning the opportunity to go |
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to the acting Olympics. |
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- text: 'Resolved Question: Severe pain in right side of hip radiating down leg and |
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into foot. It hurts to stand, walk, sit or lie down. I''ve had it for several |
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weeks & have used heat, ice, muscle rub-ons & patches.' |
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- text: 'The Antarctic Treaty. The 12 nations listed in the preamble (below) signed |
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the Antarctic Treaty on 1 December 1959 at Washington, D.C. The Treaty entered |
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into force on 23 June 1961; the 12 signatories became the original 12 consultative |
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nations.nother 21 nations have acceded to the Antarctic Treaty: Austria, Belarus, |
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Canada, Colombia, Cuba, Democratic Peoples Republic of Korea, Denmark, Estonia, |
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Greece, Guatemala, Hungary, Malaysia, Monaco, Pakistan, Papua New Guinea, Portugal, |
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Romania, Slovak Republic, Switzerland, Turkey, and Venezuela.' |
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- text: Orlando, Florida, USA — Sunrise, Sunset, and Daylength, May 2017. May 2017 |
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— Sun in Orlando. |
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- text: Line baking dish ... to also cover roast). Place roast ... the roast. Place |
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in preheated 300 degree oven for 2 1/2 to 3 hours. About 50 minutes per pound.rim |
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all excess fat from roast. Place potatoes ... Crockery Pot on top of potatoes |
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and onions. Cover and cook on low setting for 10 to 12 hours (high 5 to 6). |
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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: SPLADE-BERT-Medium |
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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: Unknown |
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type: unknown |
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metrics: |
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- type: dot_accuracy@1 |
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value: 0.4716 |
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name: Dot Accuracy@1 |
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- type: dot_accuracy@3 |
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value: 0.7802 |
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name: Dot Accuracy@3 |
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- type: dot_accuracy@5 |
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value: 0.8684 |
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name: Dot Accuracy@5 |
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- type: dot_accuracy@10 |
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value: 0.9396 |
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name: Dot Accuracy@10 |
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- type: dot_precision@1 |
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value: 0.4716 |
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name: Dot Precision@1 |
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- type: dot_precision@3 |
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value: 0.26713333333333333 |
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name: Dot Precision@3 |
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- type: dot_precision@5 |
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value: 0.18059999999999998 |
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name: Dot Precision@5 |
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- type: dot_precision@10 |
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value: 0.09851999999999998 |
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name: Dot Precision@10 |
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- type: dot_recall@1 |
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value: 0.4563333333333333 |
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name: Dot Recall@1 |
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- type: dot_recall@3 |
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value: 0.7666333333333334 |
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name: Dot Recall@3 |
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- type: dot_recall@5 |
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value: 0.8592166666666667 |
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name: Dot Recall@5 |
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- type: dot_recall@10 |
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value: 0.9338666666666667 |
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name: Dot Recall@10 |
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- type: dot_ndcg@10 |
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value: 0.7088774640922301 |
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name: Dot Ndcg@10 |
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- type: dot_mrr@10 |
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value: 0.6397524603174632 |
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name: Dot Mrr@10 |
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- type: dot_map@100 |
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value: 0.6359976077086615 |
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name: Dot Map@100 |
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- type: query_active_dims |
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value: 23.28499984741211 |
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name: Query Active Dims |
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- type: query_sparsity_ratio |
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value: 0.9992371076650478 |
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name: Query Sparsity Ratio |
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- type: corpus_active_dims |
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value: 175.6306999586799 |
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name: Corpus Active Dims |
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- type: corpus_sparsity_ratio |
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value: 0.9942457669891004 |
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name: Corpus Sparsity Ratio |
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--- |
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# SPLADE-BERT-Medium |
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This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [prajjwal1/bert-medium](https://huggingface.co/prajjwal1/bert-medium) 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:** SPLADE Sparse Encoder |
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- **Base model:** [prajjwal1/bert-medium](https://huggingface.co/prajjwal1/bert-medium) <!-- at revision ce27ec2944bd32b66ed837edb9c77eb7301b8ecc --> |
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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:** mit |
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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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'}) |
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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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## 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("yosefw/SPLADE-BERT-Medium-BS384") |
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# Run inference |
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queries = [ |
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"how long to bake arm roast", |
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] |
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documents = [ |
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'Line baking dish ... to also cover roast). Place roast ... the roast. Place in preheated 300 degree oven for 2 1/2 to 3 hours. About 50 minutes per pound.rim all excess fat from roast. Place potatoes ... Crockery Pot on top of potatoes and onions. Cover and cook on low setting for 10 to 12 hours (high 5 to 6).', |
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'Considerations. The total time it takes to cook an arm roast depends on its size. A 3- to 4-lb. chuck roast takes 5 to 6 hours on high and 10 to 12 hours on low.Chuck roasts usually contain enough marbled fat to cook without water, but most Crock-Pot roast recipes call for a little liquid.Most importantly, resist the temptation to lift the lid while your roast is cooking. 3- to 4-lb. chuck roast takes 5 to 6 hours on high and 10 to 12 hours on low. Chuck roasts usually contain enough marbled fat to cook without water, but most Crock-Pot roast recipes call for a little liquid. Most importantly, resist the temptation to lift the lid while your roast is cooking.', |
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'Set your Crock Pot on high to reach a simmer point of 209 degrees F in 3 to 4 hours, or low to reach the same cooking temperature in 7 to 8 hours. The total time it takes to cook an arm roast depends on its size. A 3- to 4-lb. chuck roast takes 5 to 6 hours on high and 10 to 12 hours on low.Chuck roasts usually contain enough marbled fat to cook without water, but most Crock-Pot roast recipes call for a little liquid.Most importantly, resist the temptation to lift the lid while your roast is cooking. 3- to 4-lb. chuck roast takes 5 to 6 hours on high and 10 to 12 hours on low. Chuck roasts usually contain enough marbled fat to cook without water, but most Crock-Pot roast recipes call for a little liquid. Most importantly, resist the temptation to lift the lid while your roast is cooking.', |
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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([[16.1861, 15.3382, 15.6794]]) |
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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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* 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 | 0.4716 | |
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| dot_accuracy@3 | 0.7802 | |
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| dot_accuracy@5 | 0.8684 | |
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| dot_accuracy@10 | 0.9396 | |
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| dot_precision@1 | 0.4716 | |
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| dot_precision@3 | 0.2671 | |
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| dot_precision@5 | 0.1806 | |
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| dot_precision@10 | 0.0985 | |
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| dot_recall@1 | 0.4563 | |
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| dot_recall@3 | 0.7666 | |
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| dot_recall@5 | 0.8592 | |
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| dot_recall@10 | 0.9339 | |
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| **dot_ndcg@10** | **0.7089** | |
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| dot_mrr@10 | 0.6398 | |
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| dot_map@100 | 0.636 | |
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| query_active_dims | 23.285 | |
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| query_sparsity_ratio | 0.9992 | |
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| corpus_active_dims | 175.6307 | |
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| corpus_sparsity_ratio | 0.9942 | |
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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: 496,123 training samples |
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* Columns: <code>query</code>, <code>positive</code>, <code>negative_1</code>, and <code>negative_2</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | query | positive | negative_1 | negative_2 | |
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|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | string | string | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 8.87 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 81.23 tokens</li><li>max: 259 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 79.21 tokens</li><li>max: 197 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 77.89 tokens</li><li>max: 207 tokens</li></ul> | |
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* Samples: |
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| query | positive | negative_1 | negative_2 | |
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|:------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>heart specialists in ridgeland ms</code> | <code>Dr. George Reynolds Jr, MD is a cardiology specialist in Ridgeland, MS and has been practicing for 35 years. He graduated from Vanderbilt University School Of Medicine in 1977 and specializes in cardiology and internal medicine.</code> | <code>Dr. James Kramer is a Internist in Ridgeland, MS. Find Dr. Kramer's phone number, address and more.</code> | <code>Dr. James Kramer is an internist in Ridgeland, Mississippi. He received his medical degree from Loma Linda University School of Medicine and has been in practice for more than 20 years. Dr. James Kramer's Details</code> | |
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| <code>does baytril otic require a prescription</code> | <code>Baytril Otic Ear Drops-Enrofloxacin/Silver Sulfadiazine-Prices & Information. A prescription is required for this item. A prescription is required for this item. Brand medication is not available at this time.</code> | <code>RX required for this item. Click here for our full Prescription Policy and Form. Baytril Otic (enrofloxacin/silver sulfadiazine) Emulsion from Bayer is the first fluoroquinolone approved by the Food and Drug Administration for the topical treatment of canine otitis externa.</code> | <code>Product Details. Baytril Otic is a highly effective treatment prescribed by many veterinarians when your pet has an ear infection caused by susceptible bacteria or fungus. Baytril Otic is: a liquid emulsion that is used topically directly in the ear or on the skin in order to treat susceptible bacterial and yeast infections.</code> | |
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| <code>what is on a gyro</code> | <code>Report Abuse. Gyros or gyro (giros) (pronounced /ˈjɪəroʊ/ or /ˈdʒaɪroʊ/, Greek: γύρος turn) is a Greek dish consisting of meat (typically lamb and/or beef), tomato, onion, and tzatziki sauce, and is served with pita bread. Chicken and pork meat can be used too.</code> | <code>A gyroscope (from Ancient Greek γῦρος gûros, circle and σκοπέω skopéō, to look) is a spinning wheel or disc in which the axis of rotation is free to assume any orientation by itself. When rotating, the orientation of this axis is unaffected by tilting or rotation of the mounting, according to the conservation of angular momentum.</code> | <code>Diagram of a gyro wheel. Reaction arrows about the output axis (blue) correspond to forces applied about the input axis (green), and vice versa. A gyroscope is a wheel mounted in two or three gimbals, which are a pivoted supports that allow the rotation of the wheel about a single axis.</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": 0.005 |
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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`: epoch |
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- `per_device_train_batch_size`: 48 |
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- `per_device_eval_batch_size`: 48 |
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- `gradient_accumulation_steps`: 8 |
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- `learning_rate`: 8e-05 |
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- `num_train_epochs`: 8 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.025 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `push_to_hub`: True |
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- `batch_sampler`: no_duplicates |
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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`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 48 |
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- `per_device_eval_batch_size`: 48 |
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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`: 8 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 8e-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`: 8 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.025 |
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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`: True |
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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`: True |
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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 |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: True |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: None |
|
|
- `hub_always_push`: False |
|
|
- `hub_revision`: None |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `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`: {} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | dot_ndcg@10 | |
|
|
|:-----:|:----:|:-------------:|:-----------:| |
|
|
| 1.0 | 1292 | 42.0325 | 0.7155 | |
|
|
| 2.0 | 2584 | 1.1261 | 0.7216 | |
|
|
| 3.0 | 3876 | 1.049 | 0.7214 | |
|
|
| 4.0 | 5168 | 0.9631 | 0.7188 | |
|
|
| 5.0 | 6460 | 0.8725 | 0.7120 | |
|
|
| -1 | -1 | - | 0.7089 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.11 |
|
|
- Sentence Transformers: 5.1.0 |
|
|
- Transformers: 4.55.4 |
|
|
- PyTorch: 2.8.0+cu126 |
|
|
- Accelerate: 1.10.1 |
|
|
- Datasets: 4.0.0 |
|
|
- Tokenizers: 0.21.4 |
|
|
|
|
|
## 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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