Instructions to use VARabi/Sentence-ALDi-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/Sentence-ALDi-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VARabi/Sentence-ALDi-30")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/Sentence-ALDi-30") model = AutoModelForSequenceClassification.from_pretrained("VARabi/Sentence-ALDi-30", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from VARabi/Sentence-ALDi-30: direct link, hf CLI and curl.
- Browser
- Download file 651 MB
-
https://huggingface.co/VARabi/Sentence-ALDi-30/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://VARabi/Sentence-ALDi-30/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VARabi/Sentence-ALDi-30/resolve/main/pytorch_model.bin
651 MB
- Xet hash:
- 411c944f11462302cfcfbf126a3db1a3287704505965a69038f2e83cfe312313
- Size of remote file:
- 651 MB
- SHA256:
- 74c33172ebafc6e4f0738bcc72ea243b280dc6e4e4c27a73d310ae1e6530df60
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