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README.md
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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The model is a result of fine-tuning Mistral-7B-v0.1 on a down stream task, in low resourced setting. It is able to translate English sentences to Zulu and Xhosa
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## Model Details
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## Uses
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The model can be used to translate Engslih to Zulu and Xhosa. With further improvement it can be used to translate domain specific infromation from English to Zulu and Xhosa,
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thus it can be used to
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be used in the Education industry to teach core subjects in native South African langauges thus can improve pupils' performance in
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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You can download the model, dsfsi/OMT-LR-Mistral7b, and prompt it to translate English sentences to Zulu and Xhosa sentences.
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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[
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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Khoboko, P. W., Marivate, V., & Sefara, J. (2025). Optimizing translation for low-resource languages: Efficient fine-tuning with custom prompt engineering in large language models. Machine Learning with Applications, 20, 100649.
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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The model is a result of fine-tuning Mistral-7B-v0.1 on a down stream task, in low resourced setting. It is able to translate English sentences to Zulu and Xhosa sentences.
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## Model Details
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## Uses
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The model can be used to translate Engslih to Zulu and Xhosa. With further improvement it can be used to translate domain specific infromation from English to Zulu and Xhosa,
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thus it can be used to translate research information that was written in English, in the agriculture industry, to small scale farmers that speak Zulu and Xhosa. Further, it can
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be used in the Education industry to teach core subjects in native South African langauges thus can improve pupils' performance in these subjects.
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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You can download the model, dsfsi/OMT-LR-Mistral7b, and prompt it to translate English sentences to Zulu and Xhosa sentences.
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### Out-of-Scope Use
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#### Preprocessing [optional]
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Look at the repo to find out how the dataset clean up and preparation code in python.
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#### Training Hyperparameters
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```
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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- [nwu-ctext/autshumato](https://huggingface.co/datasets/nwu-ctext/autshumato)
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- [Helsinki-NLP/opus-100](https://huggingface.co/datasets/Helsinki-NLP/opus-100)
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- [WMT22](https://huggingface.co/datasets/wmt22)
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- [gsarti/flores_101](https://huggingface.co/datasets/gsarti/flores_101)
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#### Metrics
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Khoboko, P. W., Marivate, V., & Sefara, J. (2025). Optimizing translation for low-resource languages: Efficient fine-tuning with custom prompt engineering in large language models. Machine Learning with Applications, 20, 100649.
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## Model Card Authors [optional]
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