Instructions to use Davlan/mt5_base_eng_yor_mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/mt5_base_eng_yor_mt with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Davlan/mt5_base_eng_yor_mt") model = AutoModelForSeq2SeqLM.from_pretrained("Davlan/mt5_base_eng_yor_mt") - Notebooks
- Google Colab
- Kaggle
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README.md
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Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
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## Intended uses & limitations
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#### How to use
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You can use this model with Transformers *pipeline* for
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```python
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from transformers import MT5ForConditionalGeneration, T5Tokenizer
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Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
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## Intended uses & limitations
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#### How to use
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You can use this model with Transformers *pipeline* for MT.
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```python
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from transformers import MT5ForConditionalGeneration, T5Tokenizer
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