Zenomis/mt5-italian-to-sardinian

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

Model Card for Model ID

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

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 Italian to the Sardinian language, 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]

<!-- Provide the basic links for the model. -->

• —Repository: [More Information Needed]
• —Paper [optional]: [More Information Needed]
• —Demo [optional]: [More Information Needed]

Uses

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

Here’s a minimal example to perform translation:

from transformers import pipeline


model_id = "Zenomis/mt5-italian-sardinian"

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

italian_text = "L'autonomia è un principio fondamentale della democrazia."

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

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[More Information Needed]

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

Training Procedure

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

Preprocessing [optional]

[More Information Needed]

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 -->

Speeds, Sizes, Times [optional]

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

[More Information Needed]

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. -->

[More Information Needed]

Factors

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

[More Information Needed]

Metrics

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

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

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

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

• —Hardware Type: [More Information Needed]
• —Hours used: [More Information Needed]
• —Cloud Provider: [More Information Needed]
• —Compute Region: [More Information Needed]
• —Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

Simone Pinna: simone.pinnaz82@gmail.com

Model Card Contact

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

Simone Pinna – simone.pinnaz82@gmail.com