Instructions to use cep-ter/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cep-ter/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cep-ter/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cep-ter/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("cep-ter/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0fd49c95d93361a51f2229b3347eaa711ec972adb3ae841dacaf61325cfac86
- Size of remote file:
- 3.58 kB
- SHA256:
- 73f43596a715a99e199fd5f76c06dadbb4697566fb942abfe8ef396099c795d0
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