Instructions to use Drigoro/deberta_mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/deberta_mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Drigoro/deberta_mini")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Drigoro/deberta_mini") model = AutoModelForSequenceClassification.from_pretrained("Drigoro/deberta_mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Drigoro/deberta_mini: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Drigoro/deberta_mini/resolve/main/training_args.bin
- Command line
-
hf download hf://Drigoro/deberta_mini/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Drigoro/deberta_mini/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 5340a9ec2cd924df278b2d329407a60a2efcc09f2d177528494535ba9ae95866
- Size of remote file:
- 5.11 kB
- SHA256:
- fc17dbc2479655021e43ea3238c2a091d376fdafdd55f0aa4d12a99ef0ce693a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.