Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use nahidcs/TokenizerTestingMTSUFall2024SoftwareEngineering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahidcs/TokenizerTestingMTSUFall2024SoftwareEngineering with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nahidcs/TokenizerTestingMTSUFall2024SoftwareEngineering") model = AutoModelForSeq2SeqLM.from_pretrained("nahidcs/TokenizerTestingMTSUFall2024SoftwareEngineering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96a998319d2151b16b2977093312b86d2022178d75a5f95f48d39eaec067de18
- Size of remote file:
- 242 MB
- SHA256:
- 881ecd3d2e20cfb1ddf96ac598ad90a610007dfa4ff04cfc024e4afd118bc686
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.