Instructions to use fdzaitana/t5-normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fdzaitana/t5-normalization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("fdzaitana/t5-normalization") model = AutoModelForSeq2SeqLM.from_pretrained("fdzaitana/t5-normalization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 00caf935e94fd9230be85552028ec253cb399ac0a9c64527dff7a99be30de831
- Size of remote file:
- 5.5 kB
- SHA256:
- 0cf5dfe75207e37147f26b01ad38bee46ad008c6989e0f088583dcfa4465817a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.