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:
- 3c417f556e9380bacc3c5ec4b52b2522eb1966e4792798eeb98629bd7935266e
- Size of remote file:
- 1.3 GB
- SHA256:
- 176d8f9198e9ba1890e73cb84cea6fd72029dde3aa3cfe20eb88e102fe423824
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