Instructions to use FiveC/EDA_RD-Bahnar-Vie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FiveC/EDA_RD-Bahnar-Vie with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FiveC/EDA_RD-Bahnar-Vie") model = AutoModelForSeq2SeqLM.from_pretrained("FiveC/EDA_RD-Bahnar-Vie") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09ba09353ced28bae2a31c79cbb96d98c3c6af5de317847ae94906ac8a6f1190
- Size of remote file:
- 5.97 kB
- SHA256:
- 15d3fe8a7d953af6365b36cf6dd768a550c98bed176279152ef4a206acb55453
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.