Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:1451941
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
| 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:1047690 | |
| - loss:CoSENTLoss | |
| base_model: Alibaba-NLP/gte-modernbert-base | |
| widget: | |
| - source_sentence: That is evident from their failure , three times in a row , to | |
| get a big enough turnout to elect a president . | |
| sentences: | |
| - 'given a text, decide to which of a predefined set of classes it belongs. examples: | |
| language identification, genre classification, sentiment analysis, and spam detection' | |
| - Three times in a row , they failed to get a big _ enough turnout to elect a president | |
| . | |
| - He said the Government still did not know the real reason the original Saudi buyer | |
| pulled out on August 21 . | |
| - source_sentence: these use built-in and learned knowledge to make decisions and | |
| accomplish tasks that fulfill the intentions of the user. | |
| sentences: | |
| - It also features a 4.5 in back-lit LCD screen and memory expansion facilities | |
| . | |
| - '- set of interrelated components - collect, process, store and distribute info. | |
| - support decision-making, coordination, and control' | |
| - software programs that work without direct human intervention to carry out specific | |
| tasks for an individual user, business process, or software application -siri | |
| adapts to your preferences over time | |
| - source_sentence: any location in storage can be accessed at any moment in approximately | |
| the same amount of time. | |
| sentences: | |
| - your study can adopt the original model used by the cited theorist but you can | |
| modify different variables depending on your study of the whole theory | |
| - an access method that can access any storage location directly and in any order; | |
| primary storage devices and disk storage devices use random access... | |
| - Branson said that his preference would be to operate a fully commercial service | |
| on routes to New York , Barbados and Dubai . | |
| - source_sentence: United issued a statement saying it will " work professionally | |
| and cooperatively with all its unions . " | |
| sentences: | |
| - network that acts like the human brain; type of ai | |
| - a database system consists of one or more databases and a database management | |
| system (dbms). | |
| - Senior vice president Sara Fields said the airline " will work professionally | |
| and cooperatively with all our unions . " | |
| - source_sentence: A European Union spokesman said the Commission was consulting EU | |
| member states " with a view to taking appropriate action if necessary " on the | |
| matter . | |
| sentences: | |
| - Justice Minister Martin Cauchon and Prime Minister Jean Chretien both have said | |
| the government will introduce legislation to decriminalize possession of small | |
| amounts of pot . | |
| - Laos 's second most important export destination - said it was consulting EU member | |
| states ' ' with a view to taking appropriate action if necessary ' ' on the matter | |
| . | |
| - the form data assumes and the possible range of values that the attribute defined | |
| as that type of data may express 1. text 2. numerical | |
| datasets: | |
| - redis/langcache-sentencepairs-v1 | |
| pipeline_tag: sentence-similarity | |
| library_name: sentence-transformers | |
| metrics: | |
| - cosine_accuracy | |
| - cosine_accuracy_threshold | |
| - cosine_f1 | |
| - cosine_f1_threshold | |
| - cosine_precision | |
| - cosine_recall | |
| - cosine_ap | |
| - cosine_mcc | |
| model-index: | |
| - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache | |
| results: | |
| - task: | |
| type: binary-classification | |
| name: Binary Classification | |
| dataset: | |
| name: val | |
| type: val | |
| metrics: | |
| - type: cosine_accuracy | |
| value: 0.7638310529446758 | |
| name: Cosine Accuracy | |
| - type: cosine_accuracy_threshold | |
| value: 0.8640533685684204 | |
| name: Cosine Accuracy Threshold | |
| - type: cosine_f1 | |
| value: 0.6912742186395134 | |
| name: Cosine F1 | |
| - type: cosine_f1_threshold | |
| value: 0.825770378112793 | |
| name: Cosine F1 Threshold | |
| - type: cosine_precision | |
| value: 0.6289243437982501 | |
| name: Cosine Precision | |
| - type: cosine_recall | |
| value: 0.7673469387755102 | |
| name: Cosine Recall | |
| - type: cosine_ap | |
| value: 0.7353968345121902 | |
| name: Cosine Ap | |
| - type: cosine_mcc | |
| value: 0.4778469995044085 | |
| name: Cosine Mcc | |
| - task: | |
| type: binary-classification | |
| name: Binary Classification | |
| dataset: | |
| name: test | |
| type: test | |
| metrics: | |
| - type: cosine_accuracy | |
| value: 0.7037777526966672 | |
| name: Cosine Accuracy | |
| - type: cosine_accuracy_threshold | |
| value: 0.8524033427238464 | |
| name: Cosine Accuracy Threshold | |
| - type: cosine_f1 | |
| value: 0.7122170715871171 | |
| name: Cosine F1 | |
| - type: cosine_f1_threshold | |
| value: 0.8118724822998047 | |
| name: Cosine F1 Threshold | |
| - type: cosine_precision | |
| value: 0.5989283084033827 | |
| name: Cosine Precision | |
| - type: cosine_recall | |
| value: 0.8783612662942272 | |
| name: Cosine Recall | |
| - type: cosine_ap | |
| value: 0.6476665223951498 | |
| name: Cosine Ap | |
| - type: cosine_mcc | |
| value: 0.44182914870985407 | |
| name: Cosine Mcc | |
| --- | |
| # 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-v1) 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:** 8192 tokens | |
| - **Output Dimensionality:** 768 dimensions | |
| - **Similarity Function:** Cosine Similarity | |
| - **Training Dataset:** | |
| - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| - **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': 8192, '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-v3") | |
| # Run inference | |
| sentences = [ | |
| 'A European Union spokesman said the Commission was consulting EU member states " with a view to taking appropriate action if necessary " on the matter .', | |
| "Laos 's second most important export destination - said it was consulting EU member states ' ' with a view to taking appropriate action if necessary ' ' on the matter .", | |
| 'the form data assumes and the possible range of values that the attribute defined as that type of data may express 1. text 2. numerical', | |
| ] | |
| embeddings = model.encode(sentences) | |
| print(embeddings.shape) | |
| # [3, 768] | |
| # Get the similarity scores for the embeddings | |
| similarities = model.similarity(embeddings, embeddings) | |
| print(similarities) | |
| # tensor([[1.0078, 0.8789, 0.4961], | |
| # [0.8789, 1.0000, 0.4648], | |
| # [0.4961, 0.4648, 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 | |
| #### Binary Classification | |
| * Datasets: `val` and `test` | |
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | |
| | Metric | val | test | | |
| |:--------------------------|:-----------|:-----------| | |
| | cosine_accuracy | 0.7638 | 0.7038 | | |
| | cosine_accuracy_threshold | 0.8641 | 0.8524 | | |
| | cosine_f1 | 0.6913 | 0.7122 | | |
| | cosine_f1_threshold | 0.8258 | 0.8119 | | |
| | cosine_precision | 0.6289 | 0.5989 | | |
| | cosine_recall | 0.7673 | 0.8784 | | |
| | **cosine_ap** | **0.7354** | **0.6477** | | |
| | cosine_mcc | 0.4778 | 0.4418 | | |
| <!-- | |
| ## 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-v1) | |
| * Size: 8,405 training samples | |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | sentence1 | sentence2 | label | | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | |
| | type | string | string | int | | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 24.89 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.3 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> | | |
| * Samples: | |
| | sentence1 | sentence2 | label | | |
| |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | |
| | <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code> | <code>" The foodservice pie business does not fit our long-term growth strategy .</code> | <code>1</code> | | |
| | <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code> | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code> | <code>0</code> | | |
| | <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> | | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: | |
| ```json | |
| { | |
| "scale": 20.0, | |
| "similarity_fct": "pairwise_cos_sim" | |
| } | |
| ``` | |
| ### Evaluation Dataset | |
| #### LangCache Sentence Pairs (all) | |
| * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| * Size: 8,405 evaluation samples | |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | sentence1 | sentence2 | label | | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| | |
| | type | string | string | int | | |
| | details | <ul><li>min: 6 tokens</li><li>mean: 24.89 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 24.3 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~45.80%</li><li>1: ~54.20%</li></ul> | | |
| * Samples: | |
| | sentence1 | sentence2 | label | | |
| |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | |
| | <code>He said the foodservice pie business doesn 't fit the company 's long-term growth strategy .</code> | <code>" The foodservice pie business does not fit our long-term growth strategy .</code> | <code>1</code> | | |
| | <code>Magnarelli said Racicot hated the Iraqi regime and looked forward to using his long years of training in the war .</code> | <code>His wife said he was " 100 percent behind George Bush " and looked forward to using his years of training in the war .</code> | <code>0</code> | | |
| | <code>The dollar was at 116.92 yen against the yen , flat on the session , and at 1.2891 against the Swiss franc , also flat .</code> | <code>The dollar was at 116.78 yen JPY = , virtually flat on the session , and at 1.2871 against the Swiss franc CHF = , down 0.1 percent .</code> | <code>0</code> | | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: | |
| ```json | |
| { | |
| "scale": 20.0, | |
| "similarity_fct": "pairwise_cos_sim" | |
| } | |
| ``` | |
| ### Training Logs | |
| | Epoch | Step | val_cosine_ap | test_cosine_ap | | |
| |:-----:|:----:|:-------------:|:--------------:| | |
| | -1 | -1 | 0.7354 | 0.6477 | | |
| ### 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", | |
| } | |
| ``` | |
| #### CoSENTLoss | |
| ```bibtex | |
| @online{kexuefm-8847, | |
| title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, | |
| author={Su Jianlin}, | |
| year={2022}, | |
| month={Jan}, | |
| url={https://kexue.fm/archives/8847}, | |
| } | |
| ``` | |
| <!-- | |
| ## Glossary | |
| *Clearly define terms in order to be accessible across audiences.* | |
| --> | |
| <!-- | |
| ## Model Card Authors | |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* | |
| --> | |
| <!-- | |
| ## Model Card Contact | |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* | |
| --> |