| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - cross-encoder |
| - sentence-transformers |
| - text-classification |
| - sentence-pair-classification |
| - semantic-similarity |
| - semantic-search |
| - retrieval |
| - reranking |
| - generated_from_trainer |
| - dataset_size:1452533 |
| - loss:MultipleNegativesRankingLoss |
| base_model: Alibaba-NLP/gte-reranker-modernbert-base |
| datasets: |
| - redis/langcache-sentencepairs-v1 |
| pipeline_tag: text-ranking |
| library_name: sentence-transformers |
| --- |
| |
| # Redis fine-tuned CrossEncoder model for semantic caching on LangCache |
|
|
| This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for sentence pair classification. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Cross Encoder |
| - **Base model:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c --> |
| - **Maximum Sequence Length:** 8192 tokens |
| - **Number of Output Labels:** 1 label |
| - **Training Dataset:** |
| - [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) |
| - **Language:** en |
| - **License:** apache-2.0 |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) |
|
|
| ## 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 CrossEncoder |
| |
| # Download from the 🤗 Hub |
| model = CrossEncoder("redis/langcache-reranker-v1-softmnrl-triplet") |
| # Get scores for pairs of texts |
| pairs = [ |
| [' What high potential jobs are there other than computer science?', ' What high potential jobs are there other than computer science?'], |
| [' Would India ever be able to develop a missile system like S300 or S400 missile?', ' Would India ever be able to develop a missile system like S300 or S400 missile?'], |
| [' water from the faucet is being drunk by a yellow dog', 'A yellow dog is drinking water from the faucet'], |
| [' water from the faucet is being drunk by a yellow dog', 'The yellow dog is drinking water from a bottle'], |
| ['! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``', '! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``'], |
| ] |
| scores = model.predict(pairs) |
| print(scores.shape) |
| # (5,) |
| |
| # Or rank different texts based on similarity to a single text |
| ranks = model.rank( |
| ' What high potential jobs are there other than computer science?', |
| [ |
| ' What high potential jobs are there other than computer science?', |
| ' Would India ever be able to develop a missile system like S300 or S400 missile?', |
| 'A yellow dog is drinking water from the faucet', |
| 'The yellow dog is drinking water from a bottle', |
| '! colspan = `` 14 `` `` Players who appeared for Colchester who left during the season ``', |
| ] |
| ) |
| # [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...] |
| ``` |
|
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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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| <!-- |
| ### 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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| <!-- |
| ### 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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| <!-- |
| ## 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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| <!-- |
| ### Recommendations |
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| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
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| ## Training Details |
|
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| ### Training Dataset |
|
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| #### LangCache Sentence Pairs (subsets=['all'], train+val=True) |
|
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| * Dataset: [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) |
| * Size: 1,452,533 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative_1 | |
| |:--------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 24 characters</li><li>mean: 114.25 characters</li><li>max: 268 characters</li></ul> | <ul><li>min: 19 characters</li><li>mean: 114.1 characters</li><li>max: 226 characters</li></ul> | <ul><li>min: 4 characters</li><li>mean: 93.04 characters</li><li>max: 234 characters</li></ul> | |
| * Samples: |
| | anchor | positive | negative_1 | |
| |:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------| |
| | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code> | <code> Any Canadian teachers (B.Ed. holders) teaching in U.S. schools?</code> | <code>Are there many Canadians living and working illegally in the United States?</code> | |
| | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code> Are there any underlying psychological tricks/tactics that are used when designing the lines for rides at amusement parks?</code> | <code>Is there any tricks for straight lines mcqs?</code> | |
| | <code> Can I pay with a debit card on PayPal?</code> | <code> Can I pay with a debit card on PayPal?</code> | <code>Can you transfer PayPal funds onto a debit card/credit card?</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "num_negatives": 1, |
| "activation_fn": "torch.nn.modules.activation.Sigmoid" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
|
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| #### LangCache Sentence Pairs (split=test) |
|
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| * Dataset: [LangCache Sentence Pairs (split=test)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v1) |
| * Size: 110,066 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative_1</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative_1 | |
| |:--------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 3 characters</li><li>mean: 97.95 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 3 characters</li><li>mean: 97.03 characters</li><li>max: 314 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 74.49 characters</li><li>max: 295 characters</li></ul> | |
| * Samples: |
| | anchor | positive | negative_1 | |
| |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
| | <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>Do you get more homework in 9th grade than 8th?</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#multiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "num_negatives": 1, |
| "activation_fn": "torch.nn.modules.activation.Sigmoid" |
| } |
| ``` |
| |
| ### 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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| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
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