
# Spanish to Quechua translator

This model is a finetuned version of the [t5-small](https://huggingface.co/t5-small).

## Model description

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.

## Intended uses & limitations

A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences.

### How to use

You can import this model as follows:

```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> model_name = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua'
>>> model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```

To translate you can do:

```python
>>> sentence = "Entonces dijo"
>>> input = tokenizer(sentence, return_tensors="pt")
>>> output = model.generate(input["input_ids"], max_length=40, num_beams=4, early_stopping=True)
>>> print('Original Sentence: {} \nTranslated sentence: {}'.format(sentence, tokenizer.decode(output[0])))
```

### Limitations and bias

Actually this model only can translate to Quechua of Ayacucho.

## Training data

For train this model we use [Spanish to Quechua dataset](https://huggingface.co/datasets/hackathon-pln-es/spanish-to-quechua)

## Evaluation results

We obtained the following metrics during the training process:

- eval_bleu = 2.9691
- eval_loss = 1.2064628601074219

## Team members

- [Sara Benel](https://huggingface.co/sbenel)
- [Jose Vílchez](https://huggingface.co/JCarlos)

{"language": ["es", "qu"], "license": "apache-2.0", "tags": ["quechua", "translation", "spanish"], "metrics": ["bleu", "sacrebleu"], "widget": [{"text": "Dios ama a los hombres"}, {"text": "A pesar de todo, soy feliz"}, {"text": "\u00bfQu\u00e9 har\u00e1n all\u00ed?"}, {"text": "Debes aprender a respetar"}]}
transformers pytorch safetensors t5 text2text-generation quechua translation spanish es qu license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #quechua #translation #spanish #es #qu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# Spanish to Quechua translator

This model is a finetuned version of the t5-small.

## Model description

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.

## Intended uses & limitations

A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences.

### How to use

You can import this model as follows:



To translate you can do:



### Limitations and bias

Actually this model only can translate to Quechua of Ayacucho.

## Training data

For train this model we use Spanish to Quechua dataset

## Evaluation results

We obtained the following metrics during the training process:

- eval_bleu = 2.9691
- eval_loss = 1.2064628601074219

## Team members

- Sara Benel
- Jose Vílchez

# Spanish to Quechua translator

This model is a finetuned version of the t5-small. ## Model description

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. ## Intended uses & limitations

A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences. ### How to use

You can import this model as follows:



To translate you can do: ### Limitations and bias

Actually this model only can translate to Quechua of Ayacucho. ## Training data

For train this model we use Spanish to Quechua dataset ## Evaluation results

We obtained the following metrics during the training process:

- eval_bleu = 2.9691
- eval_loss = 1.2064628601074219 ## Team members

- Sara Benel
- Jose Vílchez
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #quechua #translation #spanish #es #qu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # Spanish to Quechua translator

This model is a finetuned version of the t5-small. ## Model description

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. ## Intended uses & limitations

A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences. ### How to use

You can import this model as follows:



To translate you can do: ### Limitations and bias

Actually this model only can translate to Quechua of Ayacucho. ## Training data

For train this model we use Spanish to Quechua dataset ## Evaluation results

We obtained the following metrics during the training process:

- eval_bleu = 2.9691
- eval_loss = 1.2064628601074219 ## Team members

- Sara Benel
- Jose Vílchez
passage: TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #quechua #translation #spanish #es #qu #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# Spanish to Quechua translator

This model is a finetuned version of the t5-small.## Model description

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.## Intended uses & limitations

A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences.### How to use

You can import this model as follows:



To translate you can do:### Limitations and bias

Actually this model only can translate to Quechua of Ayacucho.## Training data

For train this model we use Spanish to Quechua dataset## Evaluation results

We obtained the following metrics during the training process:

- eval_bleu = 2.9691
- eval_loss = 1.2064628601074219## Team members

- Sara Benel
- Jose Vílchez