Instructions to use AMR-KELEG/ALDi-Token-DI-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMR-KELEG/ALDi-Token-DI-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AMR-KELEG/ALDi-Token-DI-30")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AMR-KELEG/ALDi-Token-DI-30") model = AutoModelForTokenClassification.from_pretrained("AMR-KELEG/ALDi-Token-DI-30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b9c5c74b26bed85120fdf7eeaafecadfb2716610b9ba92bffa8d415373ed8f1
- Size of remote file:
- 649 MB
- SHA256:
- b2085cc27542cc01f15c19ebc71d43d1f8f45bf197e79d0577205025461795a7
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