Modelpublic

redis/langcache-embed-experimental

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

0likes210downloads

Model Card

Redis fine-tuned BiEncoder model for semantic caching on LangCache

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the LangCache Sentence Pairs (all) dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for sentence pair similarity.

Model Details

Model Description

—Model Type: Sentence Transformer
—Base model: sentence-transformers/all-MiniLM-L6-v2
—Maximum Sequence Length: 100 tokens
—Output Dimensionality: 384 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': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

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-experimental")
# Run inference
sentences = [
    'According to him , the earth is the carrier of his artistic work , which is only integrated into the creative process by minimal changes .',
    'According to him , earth is the carrier of his artistic work being integrated into the creative process only by minimal changes .',
    'According to him , earth is the carrier of his creative work being integrated into the artistic process only by minimal changes .',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9844, 0.9844],
#         [0.9844, 1.0000, 1.0000],
#         [0.9844, 1.0000, 1.0078]], dtype=torch.bfloat16)

<!--

Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details> -->

<!--

Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details> -->

<!--

Out-of-Scope Use

List how the model may foreseeably be misused and address what users ought not to do with the model. -->

Evaluation

Metrics

Custom Information Retrieval

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

Metric	Value
cosine_accuracy@1	0.5768
cosine_precision@1	0.5768
cosine_recall@1	0.5588
cosine_ndcg@10	0.7653
cosine_mrr@1	0.5768
cosine_map@100	0.7131
cosineaucprecisioncachehit_ratio	0.3337
cosineaucsimilarity_distribution	0.1529

<!--

Bias, Risks and Limitations

What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -->

<!--

Recommendations

What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -->

Training Details

Training Dataset

LangCache Sentence Pairs (all)

—Dataset: LangCache Sentence Pairs (all)
—Size: 126,938 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: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 25.69 tokens</li><li>max: 65 tokens</li></ul> |
—Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>how can I get financial freedom as soon as possible?</code> | | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code> | | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> |
—Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters:

json

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

Evaluation Dataset

LangCache Sentence Pairs (all)

—Dataset: LangCache Sentence Pairs (all)
—Size: 126,938 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: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.28 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 25.69 tokens</li><li>max: 65 tokens</li></ul> |
—Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------| | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>how can I get financial freedom as soon as possible?</code> | | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The older Punts are still very much in existence today and race in the same fleets as the newer boats .</code> | | <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley , , was located at Turner Valley Bar N Ranch Airport , southwest of Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> |
—Loss: <code>losses.ArcFaceInBatchLoss</code> with these parameters:

json

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

Training Logs

Epoch	Step	test_cosine_ndcg@10
-1	-1	0.7653

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",
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->