langcache-reranker-v1-mnrl

Redis fine-tuned CrossEncoder model for semantic caching on LangCache

This is a Cross Encoder model finetuned from Alibaba-NLP/gte-reranker-modernbert-base on the LangCache Sentence Pairs (subsets=['all'], train+val=True) dataset using the sentence-transformers 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 <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c -->
• Maximum Sequence Length: 8192 tokens
• Number of Output Labels: 1 label
• Training Dataset:
  • LangCache Sentence Pairs (subsets=['all'], train+val=True)
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

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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Training Details

Training Dataset

LangCache Sentence Pairs (subsets=['all'], train+val=True)

• Dataset: LangCache Sentence Pairs (subsets=['all'], train+val=True)
• 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> with these parameters:

  {
      "scale": 20.0,
      "num_negatives": 1,
      "activation_fn": "torch.nn.modules.activation.Sigmoid"
  }
  

Evaluation Dataset

LangCache Sentence Pairs (split=test)

• Dataset: LangCache Sentence Pairs (split=test)
• 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> with these parameters:

  {
      "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

@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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