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cvapict/
yhi-message-topic-all-MiniLM-L12-v2

Name: yhi-message-topic-all-MiniLM-L12-v2
Availability: InStock

sentence-transformers

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cvapict/yhi-message-type-v2-all-MiniLM-L12-v2

{'accuracy': 0.8269230769230769}

This is a SetFit model that can be used for text 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.

Usage

To use this model for inference, first install the SetFit library:

python -m pip install setfit

You can then run inference as follows:

python

from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("cvapict/yhi-message-type-v2-all-MiniLM-L12-v2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])

BibTeX entry and citation info

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}
}

cvapict/yhi-message-topic-all-MiniLM-L12-v2

cvapict/yhi-message-type-v2-all-MiniLM-L12-v2

Usage

BibTeX entry and citation info

cvapict/
yhi-message-topic-all-MiniLM-L12-v2