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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e5fbda0cb8ded9399f99589f4799a2248911dfe5a86c43f520cbce1bd941f50
- Size of remote file:
- 4.86 kB
- SHA256:
- 74f8dbfe2cade7352c9fe3b9786deaa156afd57667af2103b209017bf3439318
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