Instructions to use gArthur98/Distilbert-For-Capstone_WD_0.07 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gArthur98/Distilbert-For-Capstone_WD_0.07 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gArthur98/Distilbert-For-Capstone_WD_0.07")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gArthur98/Distilbert-For-Capstone_WD_0.07") model = AutoModelForSequenceClassification.from_pretrained("gArthur98/Distilbert-For-Capstone_WD_0.07", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f3d1625837a7f78f0c11930094ce298f12faf304589e6db5ee867a39d81889e
- Size of remote file:
- 268 MB
- SHA256:
- 4d5980f0880df3fda9e2147ed24f7b11652499fff796000a570b0fb127fd1ebb
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