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:
- 7c03282db298d36614e9e1d01e3c7bf9c4dfbb2e387165d4904063b9e6fd7983
- Size of remote file:
- 3.45 kB
- SHA256:
- db3f9c859b89ac5b2db9b88fc8df45a1b17441770c9fd6e9575cda204bc2be71
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