Instructions to use KpRT/task-t3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KpRT/task-t3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KpRT/task-t3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KpRT/task-t3") model = AutoModelForTokenClassification.from_pretrained("KpRT/task-t3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77fb3e7dcf114e89c132e50b2d913f361c970fc3fcfed5ee641b3c5a7e3fa4e3
- Size of remote file:
- 436 MB
- SHA256:
- b869479c9ef0a8a6aa23e9b5a936bffb8d4003cad0468619410630c615e0ccf8
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