Instructions to use AMR-KELEG/Sentence-ALDi-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMR-KELEG/Sentence-ALDi-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AMR-KELEG/Sentence-ALDi-50")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AMR-KELEG/Sentence-ALDi-50") model = AutoModelForSequenceClassification.from_pretrained("AMR-KELEG/Sentence-ALDi-50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49b96aa9e7f3cc2488eb7f9752f94314fb1e5fcb6620462df5f2c5f99f135d26
- Size of remote file:
- 1.3 GB
- SHA256:
- 54c8f56c27e48fce875735c93c58c1a7d55cc84f80fba6896d1fba3c658e0944
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