Instructions to use AMR-KELEG/Sentence-ALDi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMR-KELEG/Sentence-ALDi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AMR-KELEG/Sentence-ALDi")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AMR-KELEG/Sentence-ALDi") model = AutoModelForSequenceClassification.from_pretrained("AMR-KELEG/Sentence-ALDi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e20e9af8b0f3311ce4f360a366fc976c279fdfa8297b46ff17306fca85bc09c
- Size of remote file:
- 651 MB
- SHA256:
- aba1e23ea7f0ba7f424f3c69b922b42409b699cc42ebee9a33b5dcdc51c6c6e5
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