Instructions to use borjagomez/alberta_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use borjagomez/alberta_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="borjagomez/alberta_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("borjagomez/alberta_base") model = AutoModelForSequenceClassification.from_pretrained("borjagomez/alberta_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from borjagomez/alberta_base: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/borjagomez/alberta_base/resolve/main/training_args.bin
- Command line
-
hf download hf://borjagomez/alberta_base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/borjagomez/alberta_base/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- f847c7e6aed58e94519d9bf376775cde5091bc432c2234ee05d38b73c2540540
- Size of remote file:
- 3.31 kB
- SHA256:
- 45c929a8d78c9337d709bb7aab45e2831584634fceca259dd181bb779f57293e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.