safetensors arxiv:1910.09700 license:apache-2.0 region:us
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license: apache-2.0
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# MT5-es-to-quy

<!-- Provide a quick summary of what the model is/does. -->

This model was trained using synthetic data across several iterations of State-of-the-art Spanish to Quechua (Y) datasets.

## Model Details

### Model Description

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The model presents a Test Bleu: 2.0391 and Test chrF: 22.6785 which was tested into another dataset (contest).
Our validation results (Synthetic) were Bleu: 11.0126 and Validation chrF: 31.8344.

- **Developed by:**
  - Julio Santisteban Pablo
  - Ricardo Lazo Vasquez
<!-- - **Funded by [optional]:** [More Information Needed] -->
- **Shared by [optional]:** Julio Santisteban Pablo 
- **Model type:** Translate
- **Language(s) (NLP):** Spanish to Quechua (Y)
- **License:** Apache 2.0
- **FinetuFine-tunedmodel:** MT5

### Model Sources

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- **Repository:** Julio/mt5-es-to-quy
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## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

The overall goal is to improve translation from Spanish to Quechua(Y) and their related applications.

### Direct Use

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Improve translations from spanish to Quechua (Y).

### Downstream Use

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Create High-impact platforms who helps Quechua speakers.

### Out-of-Scope Use

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- Generate malicious content.
- A 100% trustable platform.

## Bias, Risks, and Limitations

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The overall bias in this model is that is a work in progress, which is not recommended to use this model for a critical scenario.

### Recommendations

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It is important to evaluate the model in your own data before use it blindly. If the end user thinks is necesary an additional training, it must be done in the same conditions over the expected productive data.

## How to Get Started with the Model

You can use it normally importing the model using MT5 as a base.

## Training Details

### Training Data

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The model was trained using syntetic data from different sources. The overall dataset will be published in the future and this notes will be updated accordingly.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

The model was trained over the syntetic dataset and tested in a contest dataset. (In the future, we will give more details).

#### Preprocessing

The model was preprocessed several times for each attempt of final model delivery. (In the future, we will give more details).


#### Training Hyperparameters

<!--- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

(In the future, we will give more details).

#### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

(In the future, we will give more details).

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

(In the future, we will give more details).

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

The main end user is the speakers of Quechua (Y) dialect.

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

We evaluate our model using BLEU and cHrF evaluation metrics.

### Results

The model presents a Test Bleu: 2.0391 and Test chrF: 22.6785 which was tested into another dataset (contest).
Our validation results (Synthetic) were Bleu: 11.0126 and Validation chrF: 31.8344.

#### Summary

This work was made by several researchers from universities from Peru.

(In the future, we will give more details).

## Model Examination

<!-- Relevant interpretability work for the model goes here -->

(In the future, we will give more details).

## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications

### Model Architecture and Objective

(In the future, we will give more details).

### Compute Infrastructure

(In the future, we will give more details).

#### Hardware

(In the future, we will give more details).

#### Software

The model was trained using PyTorch.

## Citation

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(In the future, we will give more details).
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## Glossary [optional]

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## More Information

Please feel free to mail to the main researcher Julio Santisteban to his email: `jsantisteban@ucsp.edu.pe`.

## Model Card Authors

Ricardo Lazo Vasquez 

## Model Card Contact

Please email us to:
- `jsantisteban@ucsp.edu.pe`
- CC: `ricardo.lazo@ucsp.edu.pe`