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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bde4a56ec9f18a8601a4368d188eaa5a80f29754e531c0b35e8ce406e241c177
- Size of remote file:
- 436 MB
- SHA256:
- b790ca7beec02481e956e9f0033e29e9f3de3416a1c2d1e49f5ec0c4cca554c2
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