Instructions to use aseifert/t5-base-jfleg-wi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aseifert/t5-base-jfleg-wi with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("aseifert/t5-base-jfleg-wi") model = AutoModelForSeq2SeqLM.from_pretrained("aseifert/t5-base-jfleg-wi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aseifert/t5-base-jfleg-wi: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/aseifert/t5-base-jfleg-wi/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aseifert/t5-base-jfleg-wi/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aseifert/t5-base-jfleg-wi/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- f2d1fa5e0e2ad414375ab4b269c7e7e00126e033f0ca6ed1d2edcadc4dac3179
- Size of remote file:
- 892 MB
- SHA256:
- 154275c8c83173a79d04ce1b70169bafe0069a57312d054c2cc3cb6a68829e49
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.