Instructions to use evankomp/learn2therm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evankomp/learn2therm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="evankomp/learn2therm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("evankomp/learn2therm") model = AutoModelForSequenceClassification.from_pretrained("evankomp/learn2therm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15c5f882f90e60b537d84360984448d73be2340989ecf81e4305435c80135654
- Size of remote file:
- 1.68 GB
- SHA256:
- 5d3fc01495b4a1c96f761903de347d2d352ccaf264ad7e9fcaf9f03f3b3a4b57
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