Instructions to use shiontendon/error_diagnostic_model_mPyT5_label with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shiontendon/error_diagnostic_model_mPyT5_label with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shiontendon/error_diagnostic_model_mPyT5_label") model = AutoModelForSeq2SeqLM.from_pretrained("shiontendon/error_diagnostic_model_mPyT5_label", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from shiontendon/error_diagnostic_model_mPyT5_label: direct link, hf CLI and curl.
- Browser
- Download file 4.35 MB
-
https://huggingface.co/shiontendon/error_diagnostic_model_mPyT5_label/resolve/main/spiece.model
- Command line
-
hf download hf://shiontendon/error_diagnostic_model_mPyT5_label/spiece.model
-
curl -L -o spiece.model https://huggingface.co/shiontendon/error_diagnostic_model_mPyT5_label/resolve/main/spiece.model
4.35 MB
- Xet hash:
- 3eefe3c97072d0bffd456a982ab457048e752bd5a34e5523d18c42a0ee2e515c
- Size of remote file:
- 4.35 MB
- SHA256:
- 2fea3b4cc1b5c049581e7612bc7a9c1dc0b50bdc170751df757eacc24cdc082a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.