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--- |
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language: |
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- es |
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- qu |
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tags: |
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- quechua |
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- translation |
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- spanish |
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license: apache-2.0 |
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metrics: |
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- bleu |
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- sacrebleu |
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widget: |
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- text: "Dios ama a los hombres" |
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- text: "A pesar de todo, soy feliz" |
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- text: "¿Qué harán allí?" |
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- text: "Debes aprender a respetar" |
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--- |
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# Spanish to Quechua translator |
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This model is a finetuned version of the [t5-small](https://huggingface.co/t5-small). |
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## Model description |
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t5-small-finetuned-spanish-to-quechua has trained for 46 epochs with 102 747 sentences, the validation was performed with 12 844 sentences and 12 843 sentences were used for the test. |
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## Intended uses & limitations |
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A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences. |
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### How to use |
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You can import this model as follows: |
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```python |
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>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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>>> model_name = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua' |
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>>> model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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>>> tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
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To translate you can do: |
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```python |
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>>> sentence = "Entonces dijo" |
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>>> input = tokenizer(sentence, return_tensors="pt") |
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>>> output = model.generate(input["input_ids"], max_length=40, num_beams=4, early_stopping=True) |
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>>> print('Original Sentence: {} \nTranslated sentence: {}'.format(sentence, tokenizer.decode(output[0]))) |
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``` |
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### Limitations and bias |
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Actually this model only can translate to Quechua of Ayacucho. |
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## Training data |
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For train this model we use [Spanish to Quechua dataset](https://huggingface.co/datasets/hackathon-pln-es/spanish-to-quechua) |
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## Evaluation results |
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We obtained the following metrics during the training process: |
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- eval_bleu = 2.9691 |
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- eval_loss = 1.2064628601074219 |
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## Team |
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- [Sara Benel](https://huggingface.co/sbenel) |
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- [Jose Vílchez](https://huggingface.co/JCarlos) |
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