Instructions to use spencerh/rightpartisan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencerh/rightpartisan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencerh/rightpartisan")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("spencerh/rightpartisan") model = AutoModelForSequenceClassification.from_pretrained("spencerh/rightpartisan", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Text classifier using DistilBERT to determine Partisanship
This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/centerpartisan)
label_0 refers to "other" while label_1 refers to "right" (right as in right-leaning).
This was trained with 40,000 articles.
Best Practices
This model was optimized for 512 token-length text. Any text below 150 tokens will result in inaccurate results.
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