Instructions to use rajammanabrolu/t5_supervised_en_de_wmt16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajammanabrolu/t5_supervised_en_de_wmt16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rajammanabrolu/t5_supervised_en_de_wmt16") model = AutoModelForSeq2SeqLM.from_pretrained("rajammanabrolu/t5_supervised_en_de_wmt16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bf92838b0b379b4e65e0eac209f06c8cf53c257f2a875484c57f6a73d999751
- Size of remote file:
- 892 MB
- SHA256:
- 6a50766f5961cfcd5964dec04dc8f80a9459f8ad4028dc694b511882e7cf1823
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.