Instructions to use Mathissimo/alberta_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathissimo/alberta_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mathissimo/alberta_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mathissimo/alberta_base") model = AutoModelForSequenceClassification.from_pretrained("Mathissimo/alberta_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mathissimo/alberta_base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Mathissimo/alberta_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mathissimo/alberta_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mathissimo/alberta_base/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 72e7ea6ce48d7d0852e0ec97191e8de6809b11c650a25e89c9624f4a396b6da3
- Size of remote file:
- 499 MB
- SHA256:
- 13b57674f9ad782fb3fa1ada216e56f648f341618c50c9ede620e321c9b191aa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.