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Redis fine-tuned BiEncoder model for semantic caching on LangCache

Name: langcache-embed-v3-experimental
Rating: 2.5 (54 reviews)

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
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.

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)

Evaluation

Metrics

Custom Information Retrieval

Dataset: test
Evaluated with ir_evaluator.CustomInformationRetrievalEvaluator

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

Training Details

Training Dataset

LangCache Sentence Pairs (all)

Dataset: LangCache Sentence Pairs (all)
Size: 132,354 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 4 tokens
mean: 25.33 tokens
max: 100 tokens
min: 4 tokens
mean: 24.98 tokens
max: 100 tokens
min: 5 tokens
mean: 19.06 tokens
max: 68 tokens

Samples:

anchor	positive	negative
`What high potential jobs are there other than computer science?`	`What high potential jobs are there other than computer science?`	`Why IT or Computer Science jobs are being over rated than other Engineering jobs?`
`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?`	`Should India buy the Russian S400 air defence missile system?`
`water from the faucet is being drunk by a yellow dog`	`A yellow dog is drinking water from the faucet`	`Childlessness is low in Eastern European countries.`

Loss: losses.ArcFaceInBatchLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Evaluation Dataset

LangCache Sentence Pairs (all)

Dataset: LangCache Sentence Pairs (all)
Size: 132,354 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 4 tokens
mean: 25.33 tokens
max: 100 tokens
min: 4 tokens
mean: 24.98 tokens
max: 100 tokens
min: 5 tokens
mean: 19.06 tokens
max: 68 tokens

Samples:

anchor	positive	negative
`What high potential jobs are there other than computer science?`	`What high potential jobs are there other than computer science?`	`Why IT or Computer Science jobs are being over rated than other Engineering jobs?`
`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?`	`Should India buy the Russian S400 air defence missile system?`
`water from the faucet is being drunk by a yellow dog`	`A yellow dog is drinking water from the faucet`	`Childlessness is low in Eastern European countries.`

Loss: losses.ArcFaceInBatchLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

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",
}

	anchor	positive	negative
type	string	string	string
details	min: 4 tokens mean: 25.33 tokens max: 100 tokens	min: 4 tokens mean: 24.98 tokens max: 100 tokens	min: 5 tokens mean: 19.06 tokens max: 68 tokens

redis/langcache-embed-v3-experimental

Redis fine-tuned BiEncoder model for semantic caching on LangCache

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Custom Information Retrieval

Training Details

Training Dataset

LangCache Sentence Pairs (all)

Evaluation Dataset

LangCache Sentence Pairs (all)

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

redis/
langcache-embed-v3-experimental