Zenomis/mt5-sardinian-to-italian

This is the first open-source fine-tuned model for machine translation from Sardinian to Italian, developed by Simone Pinna.

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

The first fine-tuned machine translation model capable of translating from the Sardinian language to Italian, based on the mT5-small architecture. It was trained on a specially developed Sardinian–Italian parallel corpus, with the aim of promoting research and use of the Sardinian language in NLP contexts.

• Developed by: Simone Pinna
• License: cc-by-nc-4.0
• Finetuned from model [optional]: google/mt5-small

Model Sources [optional]

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• Demo [optional]: [More Information Needed]

Uses

You can use this model directly via Hugging Face transformers pipeline.
It translates sentences from Sardinian to Italian.

Here’s a minimal example to perform translation:

from transformers import pipeline


model_id = "Zenomis/mt5-sardinian-to-italian"

translator = pipeline(
    task="translation",
    model=model_id,
    tokenizer=model_id,
    framework="pt"
)


sardu_text = "Su autonomìsmu est cussu fenomenu in politica in ube una comunidade pùnnat a siche picare prus potere."

# Translation
result = translator(sardu_text, max_length=200)
print("Output (Italiano):", result[0]["translation_text"])

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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Model Card Authors [optional]

Simone Pinna: [email protected]

Model Card Contact

For questions, collaboration proposals, or commercial use requests, please contact:

Simone Pinna – [email protected]