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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:1056095 |
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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: In 2015 Adolf Hitler appeared in the kickstarter short movie `` |
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Kung Fury `` as Taccone ( A.K.A . |
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sentences: |
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- In 2015 , Adolf Hitler appeared in the Kickstarter - short film `` Kung Fury `` |
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as Taccone ( A.K.A . |
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- In 1795 , the only white residents were Dr. John Laidley and two brothers with |
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the surname Ainslie . |
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- The 125th University Match was played in March 2014 at the Rye Golf Club , Oxford |
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, East Sussex won the game 8.5 - 6.5 . |
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- source_sentence: From 1973 to 1974 , Aubrey toured with the Cambridge Theatre Company |
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as Diggory in `` She Stoops to Conquer `` and again as Aguecheek . |
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sentences: |
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- Oxide can be reduced to metallic samarium at higher temperatures by heating with |
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a reducing agent such as hydrogen or carbon monoxide . |
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- From 1973 to 1974 Aguecheek toured with the Cambridge Theatre Company as Diggory |
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in `` You Stoops to Conquer `` and again as Aubrey . |
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- The medals were presented by Barry Maister , IOC member , New Zealand and Sarah |
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Webb Gosling , Vice President of World Sailing . |
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- source_sentence: There is no official wall on the border , although there are sections |
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of fence near populated areas and continuous border crossings . |
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sentences: |
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- The 2014 -- 15 Boston Bruins season was the 91st season for the National Hockey |
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League franchise that was established on November 1 , 1924 . |
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- He was trained by the Inghams and owned by John Hawkes . |
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- There is no continuous wall on the border , although there are fence sections |
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near populated areas and official border crossings . |
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- source_sentence: Capital . `` The French established similar hill stations in Indochina |
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, such as Dalat built in 1921 . |
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sentences: |
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- Lubuk China is a small town in Alor Gajah District , Melaka , Malaysia . It is |
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situated near the border with Negeri Sembilan . |
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- The French established similar hill stations in Indochina , such as Dalat , built |
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in 1921 . |
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- John Potts ( or Pott ) was a doctor and colonial governor of Virginia in the Jamestown |
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settlement at Virginia Colony in the early 17th century . |
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- source_sentence: The band pursued `` signals `` in January 2012 in three weeks , |
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and drums were recorded in a day and a half . |
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sentences: |
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- It was repaired at the beginning of the 20th century and is listed as closed in |
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our records . |
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- The band tracked `` Signals `` in three weeks in January 2012 . Drums were recorded |
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in a day and a half . |
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- Contributors include actor Anton LaVey , Satanist Christopher Lee , serial killer |
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expert Clive Barker , author Karen Greenlee , and necrophile Robert Ressler . |
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datasets: |
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- aditeyabaral-redis/langcache-sentencepairs |
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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: 0.7629982153480072 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.8640064001083374 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.6907391673746814 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.8261547684669495 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.6290946608202218 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.7657770800627943 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.7350929175906749 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.47714361581572273 |
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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: 0.7035036519888425 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.8520700931549072 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.7118460123901542 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.8109649419784546 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.5979034259235814 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.8794413407821229 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.6473553527394227 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.4408784752892243 |
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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/aditeyabaral-redis/langcache-sentencepairs) 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/aditeyabaral-redis/langcache-sentencepairs) |
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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("aditeyabaral-redis/langcache-embed-v3") |
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# Run inference |
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sentences = [ |
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'The band pursued `` signals `` in January 2012 in three weeks , and drums were recorded in a day and a half .', |
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'The band tracked `` Signals `` in three weeks in January 2012 . Drums were recorded in a day and a half .', |
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'Contributors include actor Anton LaVey , Satanist Christopher Lee , serial killer expert Clive Barker , author Karen Greenlee , and necrophile Robert Ressler .', |
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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([[1.0000, 0.9599, 0.4944], |
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# [0.9599, 1.0000, 0.5097], |
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# [0.4944, 0.5097, 1.0000]]) |
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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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## 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 | 0.763 | 0.7035 | |
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| cosine_accuracy_threshold | 0.864 | 0.8521 | |
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| cosine_f1 | 0.6907 | 0.7118 | |
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| cosine_f1_threshold | 0.8262 | 0.811 | |
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| cosine_precision | 0.6291 | 0.5979 | |
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| cosine_recall | 0.7658 | 0.8794 | |
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| **cosine_ap** | **0.7351** | **0.6474** | |
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| cosine_mcc | 0.4771 | 0.4409 | |
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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/aditeyabaral-redis/langcache-sentencepairs) |
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* Size: 62,021 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: 8 tokens</li><li>mean: 27.46 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.36 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <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>1</code> | |
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| <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 Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> | |
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| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</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/aditeyabaral-redis/langcache-sentencepairs) |
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* Size: 62,021 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: 8 tokens</li><li>mean: 27.46 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 27.36 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | label | |
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|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <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>1</code> | |
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| <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 Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> | |
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| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</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 | 0.7351 | 0.6474 | |
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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: 4.55.0 |
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- PyTorch: 2.8.0+cu128 |
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- Accelerate: 1.10.0 |
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- Datasets: 4.0.0 |
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- Tokenizers: 0.21.4 |
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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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