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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pjait/narrative_classifier") model = AutoModel.from_pretrained("pjait/narrative_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pjait/narrative_classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/pjait/narrative_classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://pjait/narrative_classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pjait/narrative_classifier/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 817f5eaac7c9fabc0a655d40f119cc40feb7e49d267b30a67ae29a1dd8c7137f
- Size of remote file:
- 5.3 kB
- SHA256:
- 7053bb9d7b515628085d2f02e66e1a12fc9a1fcf26ca67ff374b8418d16ffa6f
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