Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
biencoder
text-classification
sentence-pair-classification
semantic-similarity
semantic-search
retrieval
reranking
Generated from Trainer
dataset_size:76349300
loss:ArcFaceInBatchLoss
Eval Results (legacy)
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:76349300 | |
| - loss:ArcFaceInBatchLoss | |
| base_model: Alibaba-NLP/gte-modernbert-base | |
| widget: | |
| - source_sentence: '"How much would I need to narrate a ""Let''s Play"" video in order | |
| to make money from it on YouTube?"' | |
| sentences: | |
| - How much money do people make from YouTube videos with 1 million views? | |
| - '"How much would I need to narrate a ""Let''s Play"" video in order to make money | |
| from it on YouTube?"' | |
| - '"Does the sentence, ""I expect to be disappointed,"" make sense?"' | |
| - source_sentence: '"I appreciate that.' | |
| sentences: | |
| - '"How is the Mariner rewarded in ""The Rime of the Ancient Mariner"" by Samuel | |
| Taylor Coleridge?"' | |
| - '"I appreciate that.' | |
| - I can appreciate that. | |
| - source_sentence: '"""It is very easy to defeat someone, but too hard to win some | |
| one"". What does the previous sentence mean?"' | |
| sentences: | |
| - '"How can you use the word ""visceral"" in a sentence?"' | |
| - '"""It is very easy to defeat someone, but too hard to win some one"". What does | |
| the previous sentence mean?"' | |
| - '"What does ""The loudest one in the room is the weakest one in the room."" Mean?"' | |
| - source_sentence: '" We condemn this raid which is in our view illegal and morally | |
| and politically unjustifiable , " London-based NCRI official Ali Safavi told Reuters | |
| by telephone .' | |
| sentences: | |
| - 'London-based NCRI official Ali Safavi told Reuters : " We condemn this raid , | |
| which is in our view illegal and morally and politically unjustifiable . "' | |
| - The social awkwardness is complicated by the fact that Marianne is a white girl | |
| living with a black family . | |
| - art's cause, this in my opinion | |
| - source_sentence: '"If you click ""like"" on an old post that someone made on your | |
| wall yet you''re no longer Facebook friends, will they still receive a notification?"' | |
| sentences: | |
| - '"Is there is any two wheeler having a gear box which has the feature ""automatic | |
| neutral"" when the engine is off while it is in gear?"' | |
| - '"If you click ""like"" on an old post that someone made on your wall yet you''re | |
| no longer Facebook friends, will they still receive a notification?"' | |
| - '"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would | |
| you be concerned?"' | |
| datasets: | |
| - redis/langcache-sentencepairs-v2 | |
| 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 | |
| - cosine_auc_precision_cache_hit_ratio | |
| - cosine_auc_similarity_distribution | |
| model-index: | |
| - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache | |
| results: | |
| - task: | |
| type: custom-information-retrieval | |
| name: Custom Information Retrieval | |
| dataset: | |
| name: test | |
| type: test | |
| metrics: | |
| - type: cosine_accuracy@1 | |
| value: 0.5955802603036876 | |
| name: Cosine Accuracy@1 | |
| - type: cosine_precision@1 | |
| value: 0.5955802603036876 | |
| name: Cosine Precision@1 | |
| - type: cosine_recall@1 | |
| value: 0.5780913232288468 | |
| name: Cosine Recall@1 | |
| - type: cosine_ndcg@10 | |
| value: 0.777639866271746 | |
| name: Cosine Ndcg@10 | |
| - type: cosine_mrr@1 | |
| value: 0.5955802603036876 | |
| name: Cosine Mrr@1 | |
| - type: cosine_map@100 | |
| value: 0.7275779687157514 | |
| name: Cosine Map@100 | |
| - type: cosine_auc_precision_cache_hit_ratio | |
| value: 0.3639683124583609 | |
| name: Cosine Auc Precision Cache Hit Ratio | |
| - type: cosine_auc_similarity_distribution | |
| value: 0.15401896350374616 | |
| name: Cosine Auc Similarity Distribution | |
| # 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-v2) 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-v2) | |
| - **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 = [ | |
| '"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"', | |
| '"If you click ""like"" on an old post that someone made on your wall yet you\'re no longer Facebook friends, will they still receive a notification?"', | |
| '"If your teenage son posted ""La commedia e finita"" on his Facebook wall, would you be concerned?"', | |
| ] | |
| embeddings = model.encode(sentences) | |
| print(embeddings.shape) | |
| # [3, 768] | |
| # Get the similarity scores for the embeddings | |
| similarities = model.similarity(embeddings, embeddings) | |
| print(similarities) | |
| # tensor([[1.0000, 1.0000, 0.6758], | |
| # [1.0000, 1.0000, 0.6758], | |
| # [0.6758, 0.6758, 1.0078]], 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 | |
| #### Custom Information Retrieval | |
| * Dataset: `test` | |
| * Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code> | |
| | Metric | Value | | |
| |:-------------------------------------|:-----------| | |
| | cosine_accuracy@1 | 0.5956 | | |
| | cosine_precision@1 | 0.5956 | | |
| | cosine_recall@1 | 0.5781 | | |
| | **cosine_ndcg@10** | **0.7776** | | |
| | cosine_mrr@1 | 0.5956 | | |
| | cosine_map@100 | 0.7276 | | |
| | cosine_auc_precision_cache_hit_ratio | 0.364 | | |
| | cosine_auc_similarity_distribution | 0.154 | | |
| <!-- | |
| ## 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-v2) | |
| * Size: 132,354 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: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> | | |
| * Samples: | |
| | anchor | positive | negative | | |
| |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| | |
| | <code> What high potential jobs are there other than computer science?</code> | <code> What high potential jobs are there other than computer science?</code> | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> | | |
| | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code> | | |
| | <code> water from the faucet is being drunk by a yellow dog</code> | <code>A yellow dog is drinking water from the faucet</code> | <code>Childlessness is low in Eastern European countries.</code> | | |
| * Loss: <code>losses.ArcFaceInBatchLoss</code> 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-v2) | |
| * Size: 132,354 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: 4 tokens</li><li>mean: 25.33 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.98 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 68 tokens</li></ul> | | |
| * Samples: | |
| | anchor | positive | negative | | |
| |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| | |
| | <code> What high potential jobs are there other than computer science?</code> | <code> What high potential jobs are there other than computer science?</code> | <code>Why IT or Computer Science jobs are being over rated than other Engineering jobs?</code> | | |
| | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code> Would India ever be able to develop a missile system like S300 or S400 missile?</code> | <code>Should India buy the Russian S400 air defence missile system?</code> | | |
| | <code> water from the faucet is being drunk by a yellow dog</code> | <code>A yellow dog is drinking water from the faucet</code> | <code>Childlessness is low in Eastern European countries.</code> | | |
| * Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters: | |
| ```json | |
| { | |
| "scale": 20.0, | |
| "similarity_fct": "cos_sim", | |
| "gather_across_devices": false | |
| } | |
| ``` | |
| ### Training Logs | |
| | Epoch | Step | test_cosine_ndcg@10 | | |
| |:-----:|:----:|:-------------------:| | |
| | -1 | -1 | 0.7776 | | |
| ### 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", | |
| } | |
| ``` | |
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