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:
- c48ab210a26907b4a320e73a7236a7dc2c4b93236ef104b9670e6134b0dc5d18
- Size of remote file:
- 268 MB
- SHA256:
- 1c1a83d489deef40225f4d88b1c496290a3079deb90385d8ba29652355cd6c8c
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