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@@ -21,7 +21,7 @@ base_model:
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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 sentrences.
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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 get 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 the core 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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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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-
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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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-
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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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- [More Information Needed]
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  #### Training Hyperparameters
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  )
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  ```
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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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-
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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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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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-
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- #### Factors
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-
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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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@@ -259,15 +242,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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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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-
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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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  ```
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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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