Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:1451941
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - biencoder | |
| - sentence-transformers | |
| - text-classification | |
| - sentence-pair-classification | |
| - semantic-similarity | |
| - semantic-search | |
| - retrieval | |
| - reranking | |
| - generated_from_trainer | |
| - dataset_size:1451941 | |
| - loss:MultipleNegativesRankingLoss | |
| base_model: Alibaba-NLP/gte-modernbert-base | |
| widget: | |
| - source_sentence: Gocharya ji authored Krishna Cahrit Manas in the poetic form describing | |
| about the full life of Lord Krishna ( from birth to Nirvana ) . | |
| sentences: | |
| - 'Q: Can I buy coverage for prescription drugs right away?' | |
| - Krishna Cahrit Manas in poetic form , describing the full life of Lord Krishna | |
| ( from birth to nirvana ) , wrote Gocharya ji . | |
| - Baron played actress Violet Carson who portrayed Ena Sharples in the soap . | |
| - source_sentence: The Kilkenny line only reached Maryborough in 1867 . | |
| sentences: | |
| - It was also known formerly as ' Crotto ' . | |
| - The line from Maryborough only reached Kilkenny in 1867 . | |
| - The line from Kilkenny only reached Maryborough in 1867 . | |
| - source_sentence: Tokelau International Netball Team represents Tokelau in the national | |
| netball . | |
| sentences: | |
| - Ernest Dewey Albinson ( 1898 in Minneapolis , Minnesota - 1971 in Mexico ) was | |
| an American artist . | |
| - The Tokelau national netball team represents Tokelau in international netball | |
| . | |
| - The Tokelau international netball team represents Tokelau in national netball | |
| . | |
| - source_sentence: The real number is called the `` imaginary part `` of the real | |
| number ; the real number is called the `` complex part `` of . | |
| sentences: | |
| - The school board consists of Robbie Sanders , Bryan Richards , Linda Fullingim | |
| , Lori Lambert , & Kelly Teague . | |
| - Which web design company has the best templates? | |
| - The real number is called the `` imaginary part `` of the real number , the real | |
| number of `` complex part `` of . | |
| - source_sentence: All For You was the third and last single of Kate Ryan 's third | |
| album `` Alive `` . | |
| sentences: | |
| - According to John Keay , he was `` country bred `` ( born and educated in India | |
| ) . | |
| - All For You was the third single of the third and last album `` Alive `` by Kate | |
| Ryan . | |
| - All For You was the third and last single of the third album of Kate Ryan `` Alive | |
| `` . | |
| datasets: | |
| - redis/langcache-sentencepairs-v1 | |
| pipeline_tag: sentence-similarity | |
| library_name: sentence-transformers | |
| metrics: | |
| - cosine_accuracy@1 | |
| - cosine_precision@1 | |
| - cosine_recall@1 | |
| - cosine_ndcg@10 | |
| - cosine_mrr@1 | |
| - cosine_map@100 | |
| model-index: | |
| - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache | |
| results: | |
| - task: | |
| type: information-retrieval | |
| name: Information Retrieval | |
| dataset: | |
| name: train | |
| type: train | |
| metrics: | |
| - type: cosine_accuracy@1 | |
| value: 0.5579129681749296 | |
| name: Cosine Accuracy@1 | |
| - type: cosine_precision@1 | |
| value: 0.5579129681749296 | |
| name: Cosine Precision@1 | |
| - type: cosine_recall@1 | |
| value: 0.5359784831006956 | |
| name: Cosine Recall@1 | |
| - type: cosine_ndcg@10 | |
| value: 0.7522148521266401 | |
| name: Cosine Ndcg@10 | |
| - type: cosine_mrr@1 | |
| value: 0.5579129681749296 | |
| name: Cosine Mrr@1 | |
| - type: cosine_map@100 | |
| value: 0.6974638651409195 | |
| name: Cosine Map@100 | |
| --- | |
| # Redis fine-tuned BiEncoder model for semantic caching on LangCache | |
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for sentence pair similarity. | |
| ## Model Details | |
| ### Model Description | |
| - **Model Type:** Sentence Transformer | |
| - **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 --> | |
| - **Maximum Sequence Length:** 100 tokens | |
| - **Output Dimensionality:** 768 dimensions | |
| - **Similarity Function:** Cosine Similarity | |
| - **Training Dataset:** | |
| - [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| - **Language:** en | |
| - **License:** apache-2.0 | |
| ### Model Sources | |
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) | |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) | |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) | |
| ### Full Model Architecture | |
| ``` | |
| SentenceTransformer( | |
| (0): Transformer({'max_seq_length': 100, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) | |
| (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) | |
| ) | |
| ``` | |
| ## Usage | |
| ### Direct Usage (Sentence Transformers) | |
| First install the Sentence Transformers library: | |
| ```bash | |
| pip install -U sentence-transformers | |
| ``` | |
| Then you can load this model and run inference. | |
| ```python | |
| from sentence_transformers import SentenceTransformer | |
| # Download from the 🤗 Hub | |
| model = SentenceTransformer("redis/langcache-embed-v3") | |
| # Run inference | |
| sentences = [ | |
| "All For You was the third and last single of Kate Ryan 's third album `` Alive `` .", | |
| 'All For You was the third and last single of the third album of Kate Ryan `` Alive `` .', | |
| 'All For You was the third single of the third and last album `` Alive `` by Kate Ryan .', | |
| ] | |
| embeddings = model.encode(sentences) | |
| print(embeddings.shape) | |
| # [3, 768] | |
| # Get the similarity scores for the embeddings | |
| similarities = model.similarity(embeddings, embeddings) | |
| print(similarities) | |
| # tensor([[0.9961, 0.9922, 0.9961], | |
| # [0.9922, 1.0000, 0.9922], | |
| # [0.9961, 0.9922, 1.0078]], dtype=torch.bfloat16) | |
| ``` | |
| <!-- | |
| ### Direct Usage (Transformers) | |
| <details><summary>Click to see the direct usage in Transformers</summary> | |
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| <!-- | |
| ### Downstream Usage (Sentence Transformers) | |
| You can finetune this model on your own dataset. | |
| <details><summary>Click to expand</summary> | |
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| <!-- | |
| ### Out-of-Scope Use | |
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* | |
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| ## Evaluation | |
| ### Metrics | |
| #### Information Retrieval | |
| * Dataset: `train` | |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | |
| | Metric | Value | | |
| |:-------------------|:-----------| | |
| | cosine_accuracy@1 | 0.5579 | | |
| | cosine_precision@1 | 0.5579 | | |
| | cosine_recall@1 | 0.536 | | |
| | **cosine_ndcg@10** | **0.7522** | | |
| | cosine_mrr@1 | 0.5579 | | |
| | cosine_map@100 | 0.6975 | | |
| <!-- | |
| ## Bias, Risks and Limitations | |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* | |
| --> | |
| <!-- | |
| ### Recommendations | |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* | |
| --> | |
| ## Training Details | |
| ### Training Dataset | |
| #### LangCache Sentence Pairs (all) | |
| * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| * Size: 109,885 training samples | |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | anchor | positive | negative | | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | |
| | type | string | string | string | | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.47 tokens</li><li>max: 61 tokens</li></ul> | | |
| * Samples: | |
| | anchor | positive | negative | | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------| | |
| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>how can I get financial freedom as soon as possible?</code> | | |
| | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code> | | |
| | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: | |
| ```json | |
| { | |
| "scale": 20.0, | |
| "similarity_fct": "cos_sim", | |
| "gather_across_devices": false | |
| } | |
| ``` | |
| ### Evaluation Dataset | |
| #### LangCache Sentence Pairs (all) | |
| * Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/redis/langcache-sentencepairs-v1) | |
| * Size: 109,885 evaluation samples | |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | anchor | positive | negative | | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | |
| | type | string | string | string | | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 27.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.47 tokens</li><li>max: 61 tokens</li></ul> | | |
| * Samples: | |
| | anchor | positive | negative | | |
| |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------| | |
| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>how can I get financial freedom as soon as possible?</code> | | |
| | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code> | | |
| | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: | |
| ```json | |
| { | |
| "scale": 20.0, | |
| "similarity_fct": "cos_sim", | |
| "gather_across_devices": false | |
| } | |
| ``` | |
| ### Training Logs | |
| | Epoch | Step | train_cosine_ndcg@10 | | |
| |:-----:|:----:|:--------------------:| | |
| | -1 | -1 | 0.7522 | | |
| ### Framework Versions | |
| - Python: 3.12.3 | |
| - Sentence Transformers: 5.1.0 | |
| - Transformers: 4.56.0 | |
| - PyTorch: 2.8.0+cu128 | |
| - Accelerate: 1.10.1 | |
| - Datasets: 4.0.0 | |
| - Tokenizers: 0.22.0 | |
| ## Citation | |
| ### BibTeX | |
| #### Sentence Transformers | |
| ```bibtex | |
| @inproceedings{reimers-2019-sentence-bert, | |
| title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", | |
| author = "Reimers, Nils and Gurevych, Iryna", | |
| booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", | |
| month = "11", | |
| year = "2019", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://arxiv.org/abs/1908.10084", | |
| } | |
| ``` | |
| #### MultipleNegativesRankingLoss | |
| ```bibtex | |
| @misc{henderson2017efficient, | |
| title={Efficient Natural Language Response Suggestion for Smart Reply}, | |
| author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, | |
| year={2017}, | |
| eprint={1705.00652}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |
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