Instructions to use roval15/umt5-small_translation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roval15/umt5-small_translation_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("roval15/umt5-small_translation_model") model = AutoModelForSeq2SeqLM.from_pretrained("roval15/umt5-small_translation_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0954149240d6d69935f3c2f1b6e25c5c2dce7871fc21caf7ca13417060fce75a
- Size of remote file:
- 16.8 MB
- SHA256:
- 59d699373e2ddd26d522131571e6c8a1b63ad76d847c15fee88b38940d2a5ce5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.