Instructions to use KpRT/task-t2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KpRT/task-t2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KpRT/task-t2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KpRT/task-t2") model = AutoModelForTokenClassification.from_pretrained("KpRT/task-t2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d2c65e95907057d9b85445edddee24f0113c82bfd002b380cbf65b316bb9dd4
- Size of remote file:
- 5.11 kB
- SHA256:
- ce0b04498ef91344b0c9158285150e49f7f9a26def6e43ef0bc6e966e6c02cd2
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