Text Classification
Transformers
Safetensors
English
roberta
multi-label-classification
disinformation
narrative-detection
propaganda
media-analysis
text-embeddings-inference
Instructions to use pjait/narrative_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pjait/narrative_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pjait/narrative_classifier")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pjait/narrative_classifier") model = AutoModel.from_pretrained("pjait/narrative_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 817f5eaac7c9fabc0a655d40f119cc40feb7e49d267b30a67ae29a1dd8c7137f
- Size of remote file:
- 5.3 kB
- SHA256:
- 7053bb9d7b515628085d2f02e66e1a12fc9a1fcf26ca67ff374b8418d16ffa6f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.