langcache-embed-v3 / README.md

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metadata

language:
  - en
license: apache-2.0
tags:
  - biencoder
  - sentence-transformers
  - text-classification
  - sentence-pair-classification
  - semantic-similarity
  - semantic-search
  - retrieval
  - reranking
  - generated_from_trainer
  - dataset_size:76349300
  - loss:ArcFaceInBatchLoss
base_model: Alibaba-NLP/gte-modernbert-base
widget:
  - source_sentence: >-
      "How much would I need to narrate a ""Let's Play"" video in order to make
      money from it on YouTube?"
    sentences:
      - How much money do people make from YouTube videos with 1 million views?
      - >-
        "How much would I need to narrate a ""Let's Play"" video in order to
        make money from it on YouTube?"
      - '"Does the sentence, ""I expect to be disappointed,"" make sense?"'
  - source_sentence: '"I appreciate that.'
    sentences:
      - >-
        "How is the Mariner rewarded in ""The Rime of the Ancient Mariner"" by
        Samuel Taylor Coleridge?"
      - '"I appreciate that.'
      - I can appreciate that.
  - source_sentence: >-
      """It is very easy to defeat someone, but too hard to win some one"". What
      does the previous sentence mean?"
    sentences:
      - '"How can you use the word ""visceral"" in a sentence?"'
      - >-
        """It is very easy to defeat someone, but too hard to win some one"".
        What does the previous sentence mean?"
      - >-
        "What does ""The loudest one in the room is the weakest one in the
        room."" Mean?"
  - source_sentence: >-
      " We condemn this raid which is in our view illegal and morally and
      politically unjustifiable , " London-based NCRI official Ali Safavi told
      Reuters by telephone .
    sentences:
      - >-
        London-based NCRI official Ali Safavi told Reuters : " We condemn this
        raid , which is in our view illegal and morally and politically
        unjustifiable . "
      - >-
        The social awkwardness is complicated by the fact that Marianne is a
        white girl living with a black family .
      - art's cause, this in my opinion
  - source_sentence: >-
      "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?"
    sentences:
      - >-
        "Is there is any two wheeler having a gear box which has the feature
        ""automatic neutral"" when the engine is off while it is in gear?"
      - >-
        "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?"
datasets:
  - redis/langcache-sentencepairs-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_precision@1
  - cosine_recall@1
  - cosine_ndcg@10
  - cosine_mrr@1
  - cosine_map@100
  - cosine_auc_precision_cache_hit_ratio
  - cosine_auc_similarity_distribution
model-index:
  - name: Redis fine-tuned BiEncoder model for semantic caching on LangCache
    results:
      - task:
          type: custom-information-retrieval
          name: Custom Information Retrieval
        dataset:
          name: test
          type: test
        metrics:
          - type: cosine_accuracy@1
            value: 0.5955802603036876
            name: Cosine Accuracy@1
          - type: cosine_precision@1
            value: 0.5955802603036876
            name: Cosine Precision@1
          - type: cosine_recall@1
            value: 0.5780913232288468
            name: Cosine Recall@1
          - type: cosine_ndcg@10
            value: 0.777639866271746
            name: Cosine Ndcg@10
          - type: cosine_mrr@1
            value: 0.5955802603036876
            name: Cosine Mrr@1
          - type: cosine_map@100
            value: 0.7275779687157514
            name: Cosine Map@100
          - type: cosine_auc_precision_cache_hit_ratio
            value: 0.3639683124583609
            name: Cosine Auc Precision Cache Hit Ratio
          - type: cosine_auc_similarity_distribution
            value: 0.15401896350374616
            name: Cosine Auc Similarity Distribution

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
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)

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:

{
    "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: 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:

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

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

@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",
}