centre-for-humanities-computing/stormtrooper
Zero/few shot learning components for scikit-learn pipelines with LLMs and transformers.
This tool helps developers quickly classify text into categories without needing extensive labeled datasets. You input a list of text examples and a set of predefined categories, and it outputs which category each text belongs to. This is ideal for Python developers building applications that require text categorization, especially when training data is scarce.
No commits in the last 6 months.
Use this if you are a Python developer and need to add text classification capabilities to your application, but you don't have many (or any) labeled examples for training.
Not ideal if you are looking for a no-code solution or if your primary goal is to train a classification model from scratch with a large, custom dataset.
Stars
19
Forks
2
Language
Python
License
MIT
Category
Last pushed
Nov 21, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/centre-for-humanities-computing/stormtrooper"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lonePatient/Bert-Multi-Label-Text-Classification
This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text...
mim-solutions/bert_for_longer_texts
BERT classification model for processing texts longer than 512 tokens. Text is first divided...
OctoberChang/X-Transformer
X-Transformer: Taming Pretrained Transformers for eXtreme Multi-label Text Classification
QData/LaMP
ECML 2019: Graph Neural Networks for Multi-Label Classification
illiterate/BertClassifier
基于PyTorch的BERT中文文本分类模型（BERT Chinese text classification model implemented by PyTorch）