Instructions to use eventdata-utd/conflibert-binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eventdata-utd/conflibert-binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eventdata-utd/conflibert-binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eventdata-utd/conflibert-binary-classification") model = AutoModelForSequenceClassification.from_pretrained("eventdata-utd/conflibert-binary-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: gpl-3.0 | |
| # Model Card for Model ID | |
| Conflibert-binary-classification is built upon the foundational Conflibert model. Through rigorous fine-tuning, this enhanced model demonstrates superior | |
| capabilities in classifying between conflict and non-conflict events. | |
| - **Finetuned from model :** [eventdata-utd/ConfliBERT-scr-uncased](https://huggingface.co/eventdata-utd/ConfliBERT-scr-uncased) | |
| - **Paper :** [ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence](https://aclanthology.org/2022.naacl-main.400.pdf) | |
| - **Demo :** [Colab Notebook](https://colab.research.google.com/drive/1asD_z6RplGVAiFUMZN6-kr7jZXGXhLgr#scrollTo=MrIFOrH2nEmN) | |