Instructions to use Ashmal/Code-t5-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ashmal/Code-t5-finetuned with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ashmal/Code-t5-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("Ashmal/Code-t5-finetuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ccf5ac957aeb13c45d0b87201564b2f8110397e282e84ad5ba301abb51e70d57
- Size of remote file:
- 2.95 GB
- SHA256:
- acc7d4e51901c96dac8959b781a56e0c60f22a09ee6809a869b384e16adb7bdb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.