Instructions to use muibk/t5_emea_20k_en-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muibk/t5_emea_20k_en-de with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("muibk/t5_emea_20k_en-de") model = AutoModelForSeq2SeqLM.from_pretrained("muibk/t5_emea_20k_en-de") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17b54a557b10191d8cc9cf2d09d4c5fb731b2c23549546787c6ffd5822275b80
- Size of remote file:
- 3.64 kB
- SHA256:
- ce9952df8b11cdcc7a928e36e94b4b1188381c4d4450be3fdc89895074ecbb5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.