README.md

metadata

license: apache-2.0
tags:
  - setfit
  - sentence-transformers
  - text-classification
pipeline_tag: text-classification
library_name: setfit

avenuegp/setfit-vertical-classification

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:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. 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==0.7.0

You can then run inference as follows:

from setfit import SetFitModel
import huggingface_hub
import torch
huggingface_hub.login(token='<HF_TOKEN>')

# Instatiate Model. 
model = SetFitModel.from_pretrained("avenuegp/setfit-vertical-classification-gpu")

# Run Inference
with torch.no_grad():
  predictions = model(list(df['description']))
  
# saving it back to the dataframe
df['vertical_prediction'] = predictions.tolist()

BibTeX entry and citation info

@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}
}