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onyx-dot-app/
information-content-model

Name: information-content-model
Availability: InStock

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-mpnet-base-v2

finetune

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Onyx Information Content Classification using SetFit with Base sentence-transformers/paraphrase-mpnet-base-v2

The model is for use by the Onyx Enterprise Search system to identify whether a short text segment contains information that could be useful by itself to answer a RAG-type question.

It is based on the SetFit approach, using sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A trained LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

About Onyx

Website: Onyx
Repository: Open Source Gen-AI + Enterprise Search

Model Details

Core Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes
Language: English

SetFit Resources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Uses

Use for Inference

The model is for use by the Onyx Enterprise Search system.

To test it locally, first install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("onyx-dot-app/information-content-model")
# Run inference
preds = model("Paris is in France")
or:
pred_probability = model.predict_proba("Paris is in France")

Framework Versions

Python: 3.11.10
SetFit: 1.1.1
Sentence Transformers: 3.4.1
Transformers: 4.49.0
PyTorch: 2.6.0
Datasets: 3.3.2
Tokenizers: 0.21.0

Citation

BibTeX (SetFit Approach)

bibtex

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}