Instructions to use AMR-KELEG/Sentence-ALDi-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMR-KELEG/Sentence-ALDi-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AMR-KELEG/Sentence-ALDi-30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AMR-KELEG/Sentence-ALDi-30") model = AutoModelForSequenceClassification.from_pretrained("AMR-KELEG/Sentence-ALDi-30") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2dbb5388fc026c48dc183e91bd78974538e922759d5a7f52502fe46697782f19
- Size of remote file:
- 1.3 GB
- SHA256:
- 3847e2f389185d6971249f6bdff66a3978df873f00fbd2e25b38339afe6e1728
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