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:
- c7d6fc991a1c9faae60f34a5a5b5229d19422fa80f42ccf48ea54037b71d99f9
- Size of remote file:
- 1.3 GB
- SHA256:
- bb7b05699b568108461a3e8d6d9e24788a0edb5976994b84e1311d25c6baa6fc
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