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 | |
| 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 = [ | |
| 'The weather is lovely today.', | |
| "It's so sunny outside!", | |
| 'He drove to the stadium.', | |
| ] | |
| embeddings = model.encode(sentences) | |
| print(embeddings.shape) | |
| # [3, 768] | |
| # Get the similarity scores for the embeddings | |
| similarities = model.similarity(embeddings, embeddings) | |
| print(similarities) | |
| # tensor([[0.9922, 0.7891, 0.4629], | |
| # [0.7891, 1.0000, 0.5117], | |
| # [0.4629, 0.5117, 1.0000]], dtype=torch.bfloat16) | |
| ``` | |
| <!-- | |
| ### Direct Usage (Transformers) | |
| <details><summary>Click to see the direct usage in Transformers</summary> | |
| </details> | |
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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>texts</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | texts | | |
| |:--------|:--------------------------------------------------------------------------------------| | |
| | type | list | | |
| | details | <ul><li>min: 3 elements</li><li>mean: 3.50 elements</li><li>max: 4 elements</li></ul> | | |
| * Samples: | |
| | texts | | |
| |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | |
| | <code>['The newer Punts are still very much in existence today and race in the same fleets as the older boats .', 'The newer punts are still very much in existence today and run in the same fleets as the older boats .', '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 .', 'The newer Punts are still very much in existence today and race in the same fleets as the older boats .', '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 .', 'Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .', '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>texts</code> | |
| * Approximate statistics based on the first 1000 samples: | |
| | | texts | | |
| |:--------|:--------------------------------------------------------------------------------------| | |
| | type | list | | |
| | details | <ul><li>min: 3 elements</li><li>mean: 3.50 elements</li><li>max: 4 elements</li></ul> | | |
| * Samples: | |
| | texts | | |
| |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | |
| | <code>['The newer Punts are still very much in existence today and race in the same fleets as the older boats .', 'The newer punts are still very much in existence today and run in the same fleets as the older boats .', '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 .', 'The newer Punts are still very much in existence today and race in the same fleets as the older boats .', '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 .', 'Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .', '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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