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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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- biencoder |
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- sentence-transformers |
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- text-classification |
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- sentence-pair-classification |
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- semantic-similarity |
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- semantic-search |
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- retrieval |
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- reranking |
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- generated_from_trainer |
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- dataset_size:1047690 |
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- loss:CoSENTLoss |
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base_model: Alibaba-NLP/gte-modernbert-base |
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widget: |
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- source_sentence: That is evident from their failure , three times in a row , to |
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get a big enough turnout to elect a president . |
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sentences: |
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- 'given a text, decide to which of a predefined set of classes it belongs. examples: |
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language identification, genre classification, sentiment analysis, and spam detection' |
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- Three times in a row , they failed to get a big _ enough turnout to elect a president |
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. |
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- He said the Government still did not know the real reason the original Saudi buyer |
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pulled out on August 21 . |
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- source_sentence: these use built-in and learned knowledge to make decisions and |
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accomplish tasks that fulfill the intentions of the user. |
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sentences: |
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- It also features a 4.5 in back-lit LCD screen and memory expansion facilities |
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. |
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- '- set of interrelated components - collect, process, store and distribute info. |
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- support decision-making, coordination, and control' |
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- software programs that work without direct human intervention to carry out specific |
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tasks for an individual user, business process, or software application -siri |
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adapts to your preferences over time |
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- source_sentence: any location in storage can be accessed at any moment in approximately |
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the same amount of time. |
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sentences: |
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- your study can adopt the original model used by the cited theorist but you can |
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modify different variables depending on your study of the whole theory |
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- an access method that can access any storage location directly and in any order; |
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primary storage devices and disk storage devices use random access... |
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- Branson said that his preference would be to operate a fully commercial service |
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on routes to New York , Barbados and Dubai . |
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- source_sentence: United issued a statement saying it will " work professionally |
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and cooperatively with all its unions . " |
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sentences: |
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- network that acts like the human brain; type of ai |
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- a database system consists of one or more databases and a database management |
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system (dbms). |
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- Senior vice president Sara Fields said the airline " will work professionally |
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and cooperatively with all our unions . " |
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- source_sentence: A European Union spokesman said the Commission was consulting EU |
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member states " with a view to taking appropriate action if necessary " on the |
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matter . |
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sentences: |
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- Justice Minister Martin Cauchon and Prime Minister Jean Chretien both have said |
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the government will introduce legislation to decriminalize possession of small |
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amounts of pot . |
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- Laos 's second most important export destination - said it was consulting EU member |
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states ' ' with a view to taking appropriate action if necessary ' ' on the matter |
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. |
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- the form data assumes and the possible range of values that the attribute defined |
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as that type of data may express 1. text 2. numerical |
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datasets: |
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- redis/langcache-sentencepairs-v1 |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy |
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- cosine_accuracy_threshold |
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- cosine_f1 |
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- cosine_f1_threshold |
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- cosine_precision |
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- cosine_recall |
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- cosine_ap |
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- cosine_mcc |
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model-index: |
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- name: Redis fine-tuned BiEncoder model for semantic caching on LangCache |
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results: |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: val |
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type: val |
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metrics: |
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- type: cosine_accuracy |
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value: 0.7638310529446758 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.8640533685684204 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.6912742186395134 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.825770378112793 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.6289243437982501 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.7673469387755102 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.7353968345121902 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.4778469995044085 |
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name: Cosine Mcc |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: test |
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type: test |
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metrics: |
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- type: cosine_accuracy |
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value: 0.7037777526966672 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.8524033427238464 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.7122170715871171 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.8118724822998047 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.5989283084033827 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.8783612662942272 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.6476665223951498 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.44182914870985407 |
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name: Cosine Mcc |
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--- |
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# Redis fine-tuned BiEncoder model for semantic caching on LangCache |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) |
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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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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) |
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(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}) |
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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 SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("redis/langcache-embed-v3") |
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# Run inference |
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sentences = [ |
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'A European Union spokesman said the Commission was consulting EU member states " with a view to taking appropriate action if necessary " on the matter .', |
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"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 .", |
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'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', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0078, 0.8789, 0.4961], |
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# [0.8789, 1.0000, 0.4648], |
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# [0.4961, 0.4648, 1.0078]], dtype=torch.bfloat16) |
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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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<!-- |
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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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## Evaluation |
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### Metrics |
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#### Binary Classification |
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* Datasets: `val` and `test` |
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
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| Metric | val | test | |
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|:--------------------------|:-----------|:-----------| |
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| cosine_accuracy | 0.7638 | 0.7038 | |
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| cosine_accuracy_threshold | 0.8641 | 0.8524 | |
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| cosine_f1 | 0.6913 | 0.7122 | |
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| cosine_f1_threshold | 0.8258 | 0.8119 | |
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| cosine_precision | 0.6289 | 0.5989 | |
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| cosine_recall | 0.7673 | 0.8784 | |
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| **cosine_ap** | **0.7354** | **0.6477** | |
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| cosine_mcc | 0.4778 | 0.4418 | |
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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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#### LangCache Sentence Pairs (all) |
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* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) |
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* Size: 8,405 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| 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> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### LangCache Sentence Pairs (all) |
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* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) |
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* Size: 8,405 evaluation samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | label | |
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|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| 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> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "pairwise_cos_sim" |
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} |
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``` |
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### Training Logs |
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| Epoch | Step | val_cosine_ap | test_cosine_ap | |
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|:-----:|:----:|:-------------:|:--------------:| |
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| -1 | -1 | 0.7354 | 0.6477 | |
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### Framework Versions |
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- Python: 3.12.3 |
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- Sentence Transformers: 5.1.0 |
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- Transformers: 4.56.0 |
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- PyTorch: 2.8.0+cu128 |
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- Accelerate: 1.10.1 |
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- Datasets: 4.0.0 |
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- Tokenizers: 0.22.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### CoSENTLoss |
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```bibtex |
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@online{kexuefm-8847, |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
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author={Su Jianlin}, |
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year={2022}, |
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month={Jan}, |
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url={https://kexue.fm/archives/8847}, |
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} |
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``` |
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