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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a260dc1db86b516103c19021fd1f92ab06cf02c748bc130e2917f84852c2d9fe
- Size of remote file:
- 651 MB
- SHA256:
- 13e32e53df3a892158a15319f4cde205002cc6f0ca783bcf74bf078a1c3b0681
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