Instructions to use everyl12/stance_class_mod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use everyl12/stance_class_mod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="everyl12/stance_class_mod")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("everyl12/stance_class_mod") model = AutoModelForSequenceClassification.from_pretrained("everyl12/stance_class_mod", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fde3c7a56056b82b274ff21f1912799a3daa3353c1461c2410fce298c9dcd18d
- Size of remote file:
- 540 MB
- SHA256:
- 93b07bd089350e022b366379c8b5cd4ca98f7ffd2b0ca78c8baa3d49fc7df12d
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