Instructions to use VARabi/Sentence-ALDi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/Sentence-ALDi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VARabi/Sentence-ALDi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/Sentence-ALDi") model = AutoModelForSequenceClassification.from_pretrained("VARabi/Sentence-ALDi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from VARabi/Sentence-ALDi: direct link, hf CLI and curl.
- Browser
- Download file 627 Bytes
-
https://huggingface.co/VARabi/Sentence-ALDi/resolve/main/scheduler.pt
- Command line
-
hf download hf://VARabi/Sentence-ALDi/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/VARabi/Sentence-ALDi/resolve/main/scheduler.pt
627 Bytes
- Xet hash:
- f161bbdbf26bb6ce8c9b98bdc2030aacda14e45ae120ccd45d321bb051b02fb2
- Size of remote file:
- 627 Bytes
- SHA256:
- bf501303f049f96d13ee57ddac5410f72a079229358bfc0084c90a64ba044efe
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