redis/langcache-embed-v3-experimental

Redis fine-tuned BiEncoder model for semantic caching on LangCache

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-modernbert-base on the LangCache Sentence Pairs (all) 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 <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 -->
• —Maximum Sequence Length: 100 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —LangCache Sentence Pairs (all)
• —Language: en
• —License: apache-2.0

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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)

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Evaluation

Metrics

Custom Information Retrieval

• —Dataset: test
• —Evaluated with <code>ir_evaluator.CustomInformationRetrievalEvaluator</code>

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Training Details

Training Dataset

LangCache Sentence Pairs (all)

• —Dataset: LangCache Sentence Pairs (all)
• —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:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Evaluation Dataset

LangCache Sentence Pairs (all)

• —Dataset: LangCache Sentence Pairs (all)
• —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:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Training Logs

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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