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:
- 16d2710e3c0d7ffb5ba0a2e02e5c4349fd382c4425e5bfd2b71ba3f0c8d04e9c
- Size of remote file:
- 242 MB
- SHA256:
- 2d60ff5cd181eb55b63cfdfafb3d38b9a1f947339d0e21eaa39a3c5c050acb02
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