Text Classification
Transformers
PyTorch
German
roberta
public participation
text classification
transport
text-embeddings-inference
Instructions to use juliaromberg/roberta-base-wechsel-german_cimt-transport with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliaromberg/roberta-base-wechsel-german_cimt-transport with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="juliaromberg/roberta-base-wechsel-german_cimt-transport")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("juliaromberg/roberta-base-wechsel-german_cimt-transport") model = AutoModelForSequenceClassification.from_pretrained("juliaromberg/roberta-base-wechsel-german_cimt-transport", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Description
roberta-base-wechsel-german_cimt-transport is a fine-tuned RoBERTa model that is built to predict modes of transport.
Specifically, this model is a benjamin/roberta-base-wechsel-german that was fine-tuned on two datasets of public participation for urban planning.
Background
This work is based on research in the project CIMT, which investigates the chances and challenges of involving citizens in political decisions in the context of sustainable mobility transitions. (for more information, visit https://www.cimt-hhu.de/en/)
Details & Evaluation Results
tba
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("juliaromberg/roberta-base-wechsel-german_cimt-transport")
model = AutoModelForSequenceClassification.from_pretrained("juliaromberg/roberta-base-wechsel-german_cimt-transport")
Citation
tba
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