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 training_args.bin from Mathissimo/alberta_base: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/Mathissimo/alberta_base/resolve/main/training_args.bin
- Command line
-
hf download hf://Mathissimo/alberta_base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Mathissimo/alberta_base/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 4ba73d33ba56cbe5687721cbe0c9dac3c49433d48442dfbcedff75d25af06dd8
- Size of remote file:
- 3.31 kB
- SHA256:
- a8cf3420fec0bc18fb6979afe9f792bca333b9ce8545352fc755393a20acf4a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.