redis/langcache-embed-v3

Redis fine-tuned BiEncoder model for semantic caching on LangCache

This is a sentence-transformers model finetuned from answerdotai/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: answerdotai/ModernBERT-base
• 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': False, 'pooling_mode_mean_tokens': True, '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 = [
    "All For You was the third and last single of Kate Ryan 's third album `` Alive `` .",
    'All For You was the third and last single of the third album of Kate Ryan `` Alive `` .',
    'All For You was the third single of the third and last album `` Alive `` by Kate Ryan .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[0.9961, 0.9922, 0.9922],
#         [0.9922, 1.0000, 0.9961],
#         [0.9922, 0.9961, 1.0000]], dtype=torch.bfloat16)

Evaluation

Metrics

Information Retrieval

• Dataset: train
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.3778
cosine_precision@1	0.3778
cosine_recall@1	0.361
cosine_ndcg@10	0.5622
cosine_mrr@1	0.3778
cosine_map@100	0.5082

Training Details

Training Dataset

LangCache Sentence Pairs (all)

• Dataset: LangCache Sentence Pairs (all)
• Size: 109,885 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 27.27 tokens max: 49 tokens	min: 8 tokens mean: 27.27 tokens max: 48 tokens	min: 7 tokens mean: 26.47 tokens max: 61 tokens

• Samples:

  anchor	positive	negative
  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?
  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 .
  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 .

• Loss: MultipleNegativesRankingLoss 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: 109,885 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 8 tokens mean: 27.27 tokens max: 49 tokens	min: 8 tokens mean: 27.27 tokens max: 48 tokens	min: 7 tokens mean: 26.47 tokens max: 61 tokens

• Samples:

  anchor	positive	negative
  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?
  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 .
  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 .

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Logs

Epoch	Step	train_cosine_ndcg@10
-1	-1	0.5622

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",
}

MultipleNegativesRankingLoss

@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}
}