Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use syssec-utd/py310-pylingual-v1-statement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syssec-utd/py310-pylingual-v1-statement with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("syssec-utd/py310-pylingual-v1-statement") model = AutoModelForSeq2SeqLM.from_pretrained("syssec-utd/py310-pylingual-v1-statement") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5690185b5a894132fd3e6f30c0e244dbf136c55a35e95147bab3adf7195a01bf
- Size of remote file:
- 5.62 kB
- SHA256:
- 7eb05d0d3d1b5c66accdbff4eb687ed2d078182c23fb46db193bbf48e9b3034e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.