Text Classification
Transformers
PyTorch
English
roberta
classification
framing
MediaFrames
argument classification
multilabel
RoBERTa-base
Instructions to use pheinisch/MediaFrame-Roberta-recall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pheinisch/MediaFrame-Roberta-recall with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pheinisch/MediaFrame-Roberta-recall")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pheinisch/MediaFrame-Roberta-recall") model = AutoModelForSequenceClassification.from_pretrained("pheinisch/MediaFrame-Roberta-recall", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1476dbfacf3d16391dd1b44936e58857eb52cd10018140c0d4e5c3234616e8ea
- Size of remote file:
- 499 MB
- SHA256:
- 8c8736419534bdecfdec81afb481e789c3ca5ee40d027e85f8400541cb20e941
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