Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use syssec-utd/py39-pylingual-v1-statement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py39-pylingual-v1-statement with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py39-pylingual-v1-statement") model = AutoModelForSeq2SeqLM.from_pretrained("syssec-utd/py39-pylingual-v1-statement") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59025157050e3dac432d83fc211be0b41cc9ab09be3069f91bc8752d54bcdc84
- Size of remote file:
- 5.62 kB
- SHA256:
- 7caf7a11af9ddce63ab301838354eee4077a7cedabe7a948ea09de06f3017ba7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.