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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:13667 |
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- loss:ArcFaceInBatchLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: It was mobilized in December 2014 from elements of the dissolved |
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51st Mechanized Brigade and newly formed units . |
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sentences: |
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- This North-South route falls entirely in the Belgian territory and runs together |
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with the Belgian roads N31 and A17 . |
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- It was mobilized in December 2014 from elements of the disbanded 51st Mechanized |
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Brigade and newly formed units . |
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- All windows are double wood , hung up with a single light . |
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- source_sentence: It is located at Ellison Bay , in the town of Liberty Grove , Wisconsin |
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. |
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sentences: |
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- It is located in Ellison Bay , in the town of Liberty Grove , Wisconsin . |
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- It is located in Liberty Grove , Wisconsin , in the town of Ellison Bay . |
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- 'The Hadejia River ( Hausa : `` kogin Haɗeja `` ) is a river in northern Nigeria |
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and is a tributary of the Yobe River ( Komadugu Yobe ) .' |
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- source_sentence: Both long and short vowels can be nasalized ( differentiation between |
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`` acces `` and `` Ä cces `` below ) , but long nasal vowels are more common . |
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sentences: |
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- Both long and short vowels can be nasalized ( the distinction between `` acces |
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`` and `` ącces `` below ) , but long nasal vowels are more common . |
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- Wilson was a member of the Senate from 1844 to 1846 and 1850 to 1852 . From 1851 |
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to 1852 he was the Massachusetts State Senate 's President . |
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- Both long vowels can be nasalized ( the distinction between `` acces `` and `` |
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ącces `` below ) , but long and short nasal vowels are more common . |
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- source_sentence: At that time , on June 22 , 1754 , Edward Bentham married Bentham |
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Elizabeth Bates ( d . 1790 ) from Hampshire in the nearby county of Alton . |
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sentences: |
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- The Department of Criminal Justice developed the first certificate program in |
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forensic science in North Carolina and sponsors a summer comparative studies program |
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based in Europe . |
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- At that time , on June 22 , 1754 , Edward Bentham married Bentham Elizabeth Bates |
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( d . 1790 ) from Hampshire in the nearby county of Alton . |
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- It was at this time , on 22 June 1754 , that Edward Bentham married Elizabeth |
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Bates ( d 1790 ) from Alton in the nearby county of Hampshire . |
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- source_sentence: In 1973 Michels ' apos broke ; Barcelona the world transfer record |
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to bring Cruyff to Catalonia . |
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sentences: |
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- In 1973 , Cruyff 'Barcelona broke the world transfer record to bring Michels to |
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Catalonia . |
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- Amalric then marched to Cairo , where Shawar offered Amalric two million pieces |
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of gold . |
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- In 1973 Michels ' apos broke ; Barcelona the world transfer record to bring Cruyff |
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to Catalonia . |
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datasets: |
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- redis/langcache-sentencepairs-v2 |
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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@1 |
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- cosine_precision@1 |
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- cosine_recall@1 |
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- cosine_ndcg@10 |
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- cosine_mrr@1 |
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- cosine_map@100 |
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- cosine_auc_precision_cache_hit_ratio |
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- cosine_auc_similarity_distribution |
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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: custom-information-retrieval |
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name: Custom Information Retrieval |
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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@1 |
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value: 0.5763458576596583 |
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name: Cosine Accuracy@1 |
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- type: cosine_precision@1 |
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value: 0.5763458576596583 |
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name: Cosine Precision@1 |
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- type: cosine_recall@1 |
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value: 0.5583264629675676 |
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name: Cosine Recall@1 |
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- type: cosine_ndcg@10 |
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value: 0.7650954794467948 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@1 |
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value: 0.5763458576596583 |
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name: Cosine Mrr@1 |
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- type: cosine_map@100 |
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value: 0.7127722828012101 |
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name: Cosine Map@100 |
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- type: cosine_auc_precision_cache_hit_ratio |
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value: 0.333338757469584 |
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name: Cosine Auc Precision Cache Hit Ratio |
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- type: cosine_auc_similarity_distribution |
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value: 0.1528271968812688 |
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name: Cosine Auc Similarity Distribution |
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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 [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v2) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for sentence pair similarity. |
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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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> |
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- **Maximum Sequence Length:** 100 tokens |
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- **Output Dimensionality:** 384 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-v2) |
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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': 100, 'do_lower_case': False, 'architecture': 'BertModel'}) |
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(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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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-experimental") |
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# Run inference |
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sentences = [ |
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"In 1973 Michels ' apos broke ; Barcelona the world transfer record to bring Cruyff to Catalonia .", |
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"In 1973 Michels ' apos broke ; Barcelona the world transfer record to bring Cruyff to Catalonia .", |
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"In 1973 , Cruyff 'Barcelona broke the world transfer record to bring Michels to Catalonia .", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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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, 1.0000, 0.9219], |
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# [1.0000, 1.0000, 0.9219], |
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# [0.9219, 0.9219, 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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</details> |
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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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#### Custom Information Retrieval |
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* Dataset: `test` |
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* Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code> |
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| Metric | Value | |
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|:-------------------------------------|:-----------| |
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| cosine_accuracy@1 | 0.5763 | |
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| cosine_precision@1 | 0.5763 | |
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| cosine_recall@1 | 0.5583 | |
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| **cosine_ndcg@10** | **0.7651** | |
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| cosine_mrr@1 | 0.5763 | |
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| cosine_map@100 | 0.7128 | |
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| cosine_auc_precision_cache_hit_ratio | 0.3333 | |
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| cosine_auc_similarity_distribution | 0.1528 | |
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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-v2) |
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* Size: 6,780 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.27 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.25 tokens</li><li>max: 47 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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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>This marine species occurs in the eastern Indian Ocean and before the Maldives and New Caledonia .</code> | |
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| <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>Both young people burn with love really , for both , but without being able to say it to himself , admitting him always .</code> | |
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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 , , 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> | |
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* Loss: <code>losses.ArcFaceInBatchLoss</code> 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": "cos_sim", |
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"gather_across_devices": false |
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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-v2) |
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* Size: 6,780 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.27 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.25 tokens</li><li>max: 47 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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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>This marine species occurs in the eastern Indian Ocean and before the Maldives and New Caledonia .</code> | |
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| <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>Both young people burn with love really , for both , but without being able to say it to himself , admitting him always .</code> | |
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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 , , 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> | |
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* Loss: <code>losses.ArcFaceInBatchLoss</code> 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": "cos_sim", |
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"gather_across_devices": false |
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} |
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``` |
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### Training Logs |
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| Epoch | Step | test_cosine_ndcg@10 | |
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|:-----:|:----:|:-------------------:| |
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| -1 | -1 | 0.7651 | |
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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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