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")# pip install -U transformers accelerate # 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
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Download README.md from eventdata-utd/conflibert-binary-classification: direct link, hf CLI and curl.
- Browser
- Download file 680 Bytes
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https://huggingface.co/eventdata-utd/conflibert-binary-classification/resolve/main/README.md
- Command line
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hf download hf://eventdata-utd/conflibert-binary-classification/README.md
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curl -L -o README.md https://huggingface.co/eventdata-utd/conflibert-binary-classification/resolve/main/README.md
680 Bytes
metadata
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
- Paper : ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence
- Demo : Colab Notebook