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--- |
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language: ro |
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license: apache-2.0 |
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tags: |
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- romanian |
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- seq2seq |
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- t5 |
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datasets: dumitrescustefan/diacritic |
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inference: true |
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--- |
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This is the fine-tuned [mt5-base-romanian](https://huggingface.co/dumitrescustefan/mt5-base-romanian) base model (**390M** parameters). |
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The model was fine-tuned on the [romanian diacritics dataset](https://huggingface.co/datasets/dumitrescustefan/diacritic) for 150k steps with a batch of size 8. The encoder sequence length is 256 and the decoder sequence length is also 256. It was trained with the following [scripts](https://github.com/iliemihai/t5x_diacritics). |
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### How to load the fine-tuned mt5x model |
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```python |
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from transformers import MT5ForConditionalGeneration, T5Tokenizer |
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model = MT5ForConditionalGeneration.from_pretrained('iliemihai/mt5-base-romanian-diacritics') |
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tokenizer = T5Tokenizer.from_pretrained('iliemihai/mt5-base-romanian-diacritics') |
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input_text = "A inceput sa ii taie un fir de par, iar fata sta in fata, tine camasa de in in mana si canta nota SI." |
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inputs = tokenizer(input_text, max_length=256, truncation=True, return_tensors="pt") |
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outputs = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]) |
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output = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(output) # this will print "A început să îi taie un fir de păr, iar fata stă în față, ține cămașa de in în mână și cântă nota SI" |
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``` |
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### Evaluation |
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Evaluation will be done soon [here]() |
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### Acknowledgements |
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We'd like to thank [TPU Research Cloud](https://sites.research.google/trc/about/) for providing the TPUv3 cores we used to train these models! |
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### Authors |
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Yours truly, |
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_[Stefan Dumitrescu](https://github.com/dumitrescustefan), [Mihai Ilie](https://github.com/iliemihai) and [Per Egil Kummervold](https://huggingface.co/north)_ |
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