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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e34dfd826fddb9705938f71e08244934cdd00aba64893175147a91ab15b55ae5
- Size of remote file:
- 436 MB
- SHA256:
- 8a2de3bdafb2b5f62b5f9b456df86643d623575937419b722482cfe6f86c8c34
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