| --- |
| 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:9233417 |
| - loss:ArcFaceInBatchLoss |
| base_model: Alibaba-NLP/gte-modernbert-base |
| widget: |
| - source_sentence: Hayley Vaughan portrayed Ripa on the ABC daytime soap opera , `` |
| All My Children `` , between 1990 and 2002 . |
| sentences: |
| - Traxxpad is a music application for Sony 's PlayStation Portable published by |
| Definitive Studios and developed by Eidos Interactive . |
| - Between 1990 and 2002 , Hayley Vaughan Ripa portrayed in the ABC soap opera `` |
| All My Children `` . |
| - Between 1990 and 2002 , Ripa Hayley portrayed Vaughan in the ABC soap opera `` |
| All My Children `` . |
| - source_sentence: Olivella monilifera is a species of dwarf sea snail , small gastropod |
| mollusk in the family Olivellidae , the marine olives . |
| sentences: |
| - Olivella monilifera is a species of the dwarf - sea snail , small gastropod mollusk |
| in the Olivellidae family , the marine olives . |
| - He was cut by the Browns after being signed by the Bills in 2013 . He was later |
| released . |
| - Olivella monilifera is a kind of sea snail , marine gastropod mollusk in the Olivellidae |
| family , the dwarf olives . |
| - source_sentence: Hayashi said that Mackey `` is a sort of `` of the original model |
| for Tenchi . |
| sentences: |
| - In the summer of 2009 , Ellick shot a documentary about Malala Yousafzai . |
| - Hayashi said that Mackey is `` sort of `` the original model for Tenchi . |
| - Mackey said that Hayashi is `` sort of `` the original model for Tenchi . |
| - source_sentence: Much of the film was shot on location in Los Angeles and in nearby |
| Burbank and Glendale . |
| sentences: |
| - Much of the film was shot on location in Los Angeles and in nearby Burbank and |
| Glendale . |
| - Much of the film was shot on site in Burbank and Glendale and in the nearby Los |
| Angeles . |
| - Traxxpad is a music application for the Sony PlayStation Portable developed by |
| the Definitive Studios and published by Eidos Interactive . |
| - source_sentence: According to him , the earth is the carrier of his artistic work |
| , which is only integrated into the creative process by minimal changes . |
| sentences: |
| - National players are Bold players . |
| - According to him , earth is the carrier of his artistic work being integrated |
| into the creative process only by minimal changes . |
| - According to him , earth is the carrier of his creative work being integrated |
| into the artistic process only by minimal changes . |
| 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: 0.5861241448475948 |
| name: Cosine Accuracy@1 |
| - type: cosine_precision@1 |
| value: 0.5861241448475948 |
| name: Cosine Precision@1 |
| - type: cosine_recall@1 |
| value: 0.5679885764966713 |
| name: Cosine Recall@1 |
| - type: cosine_ndcg@10 |
| value: 0.7729838064849864 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@1 |
| value: 0.5861241448475948 |
| name: Cosine Mrr@1 |
| - type: cosine_map@100 |
| value: 0.7216697804426214 |
| name: Cosine Map@100 |
| - type: cosine_auc_precision_cache_hit_ratio |
| value: 0.34889374678008206 |
| name: Cosine Auc Precision Cache Hit Ratio |
| - type: cosine_auc_similarity_distribution |
| value: 0.15252445696542008 |
| 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 [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 --> |
| - **Maximum Sequence Length:** 100 tokens |
| - **Output Dimensionality:** 768 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': 'ModernBertModel'}) |
| (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| ) |
| ``` |
|
|
| ## 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 = [ |
| 'According to him , the earth is the carrier of his artistic work , which is only integrated into the creative process by minimal changes .', |
| 'According to him , earth is the carrier of his artistic work being integrated into the creative process only by minimal changes .', |
| 'According to him , earth is the carrier of his creative work being integrated into the artistic process only by minimal changes .', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.9961, 0.9922], |
| # [0.9961, 1.0000, 0.9961], |
| # [0.9922, 0.9961, 0.9961]], 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 | 0.5861 | |
| | cosine_precision@1 | 0.5861 | |
| | cosine_recall@1 | 0.568 | |
| | **cosine_ndcg@10** | **0.773** | |
| | cosine_mrr@1 | 0.5861 | |
| | cosine_map@100 | 0.7217 | |
| | cosine_auc_precision_cache_hit_ratio | 0.3489 | |
| | cosine_auc_similarity_distribution | 0.1525 | |
| |
| <!-- |
| ## 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: 126,938 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: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.54 tokens</li><li>max: 61 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>how can I get financial freedom as soon as possible?</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>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</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: 126,938 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: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.54 tokens</li><li>max: 61 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>how can I get financial freedom as soon as possible?</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>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</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 | 0.7730 | |
| |
| |
| ### 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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| *Clearly define terms in order to be accessible across audiences.* |
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| ## Model Card Authors |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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