Instructions to use pcuenq/trainer-chapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pcuenq/trainer-chapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pcuenq/trainer-chapter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pcuenq/trainer-chapter") model = AutoModelForSequenceClassification.from_pretrained("pcuenq/trainer-chapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc85694ffbb892c30d0bd4840614d5ef665a762c5169095b08915b6ef7edc0e3
- Size of remote file:
- 268 MB
- SHA256:
- f054b594a64128c51479f2a6e33a8bd33d18808406fd770efa60c30bc3b8d55b
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