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") - Notebooks
- Google Colab
- Kaggle
Add missing frame
Browse files- config.json +2 -1
config.json
CHANGED
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@@ -24,7 +24,8 @@
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"10": "cultural identity",
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"11": "public opinion",
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"12": "political",
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-
"13": "external regulation and reputation"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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| 24 |
"10": "cultural identity",
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| 25 |
"11": "public opinion",
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"12": "political",
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+
"13": "external regulation and reputation",
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"14": "other"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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