| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - biencoder |
| - sentence-transformers |
| - text-classification |
| - sentence-pair-classification |
| - semantic-similarity |
| - semantic-search |
| - retrieval |
| - reranking |
| - generated_from_trainer |
| - dataset_size:13667 |
| - loss:ArcFaceInBatchLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: It was mobilized in December 2014 from elements of the dissolved |
| 51st Mechanized Brigade and newly formed units . |
| sentences: |
| - This North-South route falls entirely in the Belgian territory and runs together |
| with the Belgian roads N31 and A17 . |
| - It was mobilized in December 2014 from elements of the disbanded 51st Mechanized |
| Brigade and newly formed units . |
| - All windows are double wood , hung up with a single light . |
| - source_sentence: It is located at Ellison Bay , in the town of Liberty Grove , Wisconsin |
| . |
| sentences: |
| - It is located in Ellison Bay , in the town of Liberty Grove , Wisconsin . |
| - It is located in Liberty Grove , Wisconsin , in the town of Ellison Bay . |
| - 'The Hadejia River ( Hausa : `` kogin Haɗeja `` ) is a river in northern Nigeria |
| and is a tributary of the Yobe River ( Komadugu Yobe ) .' |
| - source_sentence: Both long and short vowels can be nasalized ( differentiation between |
| `` acces `` and `` Ä cces `` below ) , but long nasal vowels are more common . |
| sentences: |
| - Both long and short vowels can be nasalized ( the distinction between `` acces |
| `` and `` ącces `` below ) , but long nasal vowels are more common . |
| - Wilson was a member of the Senate from 1844 to 1846 and 1850 to 1852 . From 1851 |
| to 1852 he was the Massachusetts State Senate 's President . |
| - Both long vowels can be nasalized ( the distinction between `` acces `` and `` |
| ącces `` below ) , but long and short nasal vowels are more common . |
| - source_sentence: At that time , on June 22 , 1754 , Edward Bentham married Bentham |
| Elizabeth Bates ( d . 1790 ) from Hampshire in the nearby county of Alton . |
| sentences: |
| - The Department of Criminal Justice developed the first certificate program in |
| forensic science in North Carolina and sponsors a summer comparative studies program |
| based in Europe . |
| - At that time , on June 22 , 1754 , Edward Bentham married Bentham Elizabeth Bates |
| ( d . 1790 ) from Hampshire in the nearby county of Alton . |
| - It was at this time , on 22 June 1754 , that Edward Bentham married Elizabeth |
| Bates ( d 1790 ) from Alton in the nearby county of Hampshire . |
| - source_sentence: In 1973 Michels ' apos broke ; Barcelona the world transfer record |
| to bring Cruyff to Catalonia . |
| sentences: |
| - In 1973 , Cruyff 'Barcelona broke the world transfer record to bring Michels to |
| Catalonia . |
| - Amalric then marched to Cairo , where Shawar offered Amalric two million pieces |
| of gold . |
| - In 1973 Michels ' apos broke ; Barcelona the world transfer record to bring Cruyff |
| to Catalonia . |
| datasets: |
| - redis/langcache-sentencepairs-v2 |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_precision@1 |
| - cosine_recall@1 |
| - cosine_ndcg@10 |
| - cosine_mrr@1 |
| - cosine_map@100 |
| - cosine_auc_precision_cache_hit_ratio |
| - cosine_auc_similarity_distribution |
| model-index: |
| - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache |
| results: |
| - task: |
| type: custom-information-retrieval |
| name: Custom Information Retrieval |
| dataset: |
| name: test |
| type: test |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.5767756724811061 |
| name: Cosine Accuracy@1 |
| - type: cosine_precision@1 |
| value: 0.5767756724811061 |
| name: Cosine Precision@1 |
| - type: cosine_recall@1 |
| value: 0.5587801563902068 |
| name: Cosine Recall@1 |
| - type: cosine_ndcg@10 |
| value: 0.765320607860921 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@1 |
| value: 0.5767756724811061 |
| name: Cosine Mrr@1 |
| - type: cosine_map@100 |
| value: 0.7130569949974509 |
| name: Cosine Map@100 |
| - type: cosine_auc_precision_cache_hit_ratio |
| value: 0.33372951540341317 |
| name: Cosine Auc Precision Cache Hit Ratio |
| - type: cosine_auc_similarity_distribution |
| value: 0.1529248551010913 |
| name: Cosine Auc Similarity Distribution |
| --- |
| |
| # Redis fine-tuned BiEncoder model for semantic caching on LangCache |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for sentence pair similarity. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
| - **Maximum Sequence Length:** 100 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) |
| - **Language:** en |
| - **License:** apache-2.0 |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'BertModel'}) |
| (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## 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 SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("redis/langcache-embed-experimental") |
| # Run inference |
| sentences = [ |
| "In 1973 Michels ' apos broke ; Barcelona the world transfer record to bring Cruyff to Catalonia .", |
| "In 1973 Michels ' apos broke ; Barcelona the world transfer record to bring Cruyff to Catalonia .", |
| "In 1973 , Cruyff 'Barcelona broke the world transfer record to bring Michels to Catalonia .", |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 1.0000, 0.9219], |
| # [1.0000, 1.0000, 0.9219], |
| # [0.9219, 0.9219, 1.0078]], dtype=torch.bfloat16) |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Custom Information Retrieval |
|
|
| * Dataset: `test` |
| * Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code> |
| |
| | Metric | Value | |
| |:-------------------------------------|:-----------| |
| | cosine_accuracy@1 | 0.5768 | |
| | cosine_precision@1 | 0.5768 | |
| | cosine_recall@1 | 0.5588 | |
| | **cosine_ndcg@10** | **0.7653** | |
| | cosine_mrr@1 | 0.5768 | |
| | cosine_map@100 | 0.7131 | |
| | cosine_auc_precision_cache_hit_ratio | 0.3337 | |
| | cosine_auc_similarity_distribution | 0.1529 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### LangCache Sentence Pairs (all) |
| |
| * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) |
| * Size: 6,780 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.27 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.25 tokens</li><li>max: 47 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>This marine species occurs in the eastern Indian Ocean and before the Maldives and New Caledonia .</code> | |
| | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>Both young people burn with love really , for both , but without being able to say it to himself , admitting him always .</code> | |
| | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | |
| * Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### LangCache Sentence Pairs (all) |
| |
| * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) |
| * Size: 6,780 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.27 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.25 tokens</li><li>max: 47 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>This marine species occurs in the eastern Indian Ocean and before the Maldives and New Caledonia .</code> | |
| | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>Both young people burn with love really , for both , but without being able to say it to himself , admitting him always .</code> | |
| | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | |
| * Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "gather_across_devices": false |
| } |
| ``` |
| |
| ### Training Logs |
| | Epoch | Step | test_cosine_ndcg@10 | |
| |:-----:|:----:|:-------------------:| |
| | -1 | -1 | 0.7653 | |
| |
| |
| ### Framework Versions |
| - Python: 3.12.3 |
| - Sentence Transformers: 5.1.0 |
| - Transformers: 4.56.0 |
| - PyTorch: 2.8.0+cu128 |
| - Accelerate: 1.10.1 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.22.0 |
| |
| ## 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", |
| } |
| ``` |
| |
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| ## Glossary |
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| ## Model Card Authors |
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