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:
- 0250587ecf16529f2346662ff6d25397c6134ad7374dfd3d3d6e6623785be9b1
- Size of remote file:
- 651 MB
- SHA256:
- 5ddff6c9680382a79805f6943edcf72e4d4396132b043845acf1eef4b5874c46
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