Instructions to use ninagroot/thesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ninagroot/thesis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ninagroot/thesis", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ninagroot/thesis", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("ninagroot/thesis", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 665acbf5b21823e0d48007765df3e4efa780123c3281c595d46a715efee3d9a5
- Size of remote file:
- 17.5 MB
- SHA256:
- 80e1394480fe8cab6850fe31965cda4ed62c4b2f2820f44613419d964b8e47a5
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