Instructions to use felixhoffmnn/GePart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use felixhoffmnn/GePart with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="felixhoffmnn/GePart")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("felixhoffmnn/GePart") model = AutoModelForSequenceClassification.from_pretrained("felixhoffmnn/GePart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - de | |
| metrics: | |
| - f1 | |
| pipeline_tag: text-classification | |
| license: gpl-3.0 | |
| # GePart: German Party Classification Model | |
| **GePart** is a German party classification model, being capable of predicting the most likely party to a given text. This repository is part of a student project at the [Baden-Württemberg Cooperative State University](https://www.dhbw.de/startseite) during the 5th and 6th semester of our bachelor's degree in computer science. | |
| For training the classification model, we used multiple sources (e.g., speeches, tweets, and party programs) with word embeddings like [FastText](https://fasttext.cc/) and transformer models like [BERT](https://arxiv.org/abs/1810.04805). | |
| ## Hosted Model | |
| The hosted model is based on a German version of the DistilBERT Model. | |
| ## License | |
| This project is licensed under [GNU GPL V3](https://github.com/felixhoffmnn/gepart/blob/main/LICENSE). |