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:
- 812900e87e770c68f4aced6c1d719db69bdaa7be077d88083630a90ac820fb02
- Size of remote file:
- 4.66 kB
- SHA256:
- aed20cbacc104b964c6e4ebeda4168e1d4ba2a6ace24dc0434a8869a00b1f114
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