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--- |
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license: mit |
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--- |
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# PaLe-MADLAD |
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The MADLAD-400 model fine-tuned to translate from Proper Karelian, Livvi, Ludian, and Veps to Russian and vice versa. We call this model **Pa**ragraph-**Le**vel as we trained it on paragraphs comprising multiple sentences. The model demonstrates the capacity to handle gender-neutral pronouns (presenting a major obstacle in translating from Finno-Ugric languages) and other discourse-level phenomena. |
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## Example Usage for Inference |
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```` |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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model = AutoModelForSeq2SeqLM.from_pretrained('tartuNLP/pale-madlad-mt') |
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tokenizer = AutoTokenizer.from_pretrained('tartuNLP/pale-madlad-mt') |
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# You need to explicitly prepend a target language tag to the input string in the format <2xx>, where xx stands for the language code. |
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# Language codes: 'krl' for Proper Karelian, 'lud' for Ludian, 'olo' for Livvi, 'vep' for Veps, 'ru' for Russian, 'en' for English. |
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text = '<2krl>' + 'Здравствуйте!' |
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inputs = tokenizer(text, return_tensors='pt').input_ids |
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outputs = model.generate(inputs) |
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tokenizer.decode(outputs[0], skip_special_tokens=True) |
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# Output: Terveh! |
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```` |
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Please cite the following paper if you use this model in your work: |
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``` |
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@inproceedings{ |
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pashchenko2024paragraphlevel, |
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title={Paragraph-Level Machine Translation for Low-Resource Finno-Ugric Languages}, |
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author={Dmytro Pashchenko and Lisa Yankovskaya and Mark Fishel}, |
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booktitle={The Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies}, |
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year={2024}, |
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url={https://openreview.net/forum?id=uTFJsQpNZk} |
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} |
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``` |
