Instructions to use tum-nlp/bert-counterspeech-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tum-nlp/bert-counterspeech-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tum-nlp/bert-counterspeech-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tum-nlp/bert-counterspeech-classifier") model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/bert-counterspeech-classifier") - Notebooks
- Google Colab
- Kaggle
metadata
license: cc-by-4.0
language:
- en
Counter-Speech Classifier
The BERT-based counter-speech classifier is finetuned on the CONAN dataset for classifying whether a response is counter-speech, based on the counter-argument classifier ThinkCERCA/counterargument_hugging
Uses
The model is intended for classifying LM-generated dialogue responses, evaluating their validity as counter-speech.