Instructions to use ViktorDo/DistilBERT-POWO_Lifecycle_Scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViktorDo/DistilBERT-POWO_Lifecycle_Scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViktorDo/DistilBERT-POWO_Lifecycle_Scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViktorDo/DistilBERT-POWO_Lifecycle_Scratch") model = AutoModelForSequenceClassification.from_pretrained("ViktorDo/DistilBERT-POWO_Lifecycle_Scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4275db413775fc1468dcba9fb533d12d680cd30b67195511b395b71f0ac9f03
- Size of remote file:
- 267 MB
- SHA256:
- 1ee13e0b2f7a216aae8a6f4ddba6f3dedbc15455258c4934af5c667a745a7d63
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