--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description - **Developed by:** Thapelo Sindane - **Shared by [optional]:** DSFSI - **Model type:** BERT - **Language(s) (NLP):** Sepedi (nso), Sesotho(sot), Setswana(tsn), Xitsonga(tso), Isindebele(nr), Tshivenda(ven), IsiXhosa(xho), IsiZulu(zul), IsiSwati(ssw), Afrikaans(af), and English(en) - **License:** CC - **Finetuned from model [optional]:** N/A ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses Models must be used for language identification of the South African languages identified above ### Direct Use LID for low-resourced languages ### Downstream Use [optional] Language data filtering and identification [More Information Needed] ### Out-of-Scope Use Language detection in code-switched data. [More Information Needed] ## Bias, Risks, and Limitations Requires GPU to run fast [More Information Needed] ### Recommendations Do not use for sensitive tasks. Model at an infant stage. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data The source data used to train the model came from the paper 'Preparing Vuk...' referenced below: * Lastrucci, R., Dzingirai, I., Rajab, J., Madodonga, A., Shingange, M., Njini, D. and Marivate, V., 2023. Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora. arXiv preprint arXiv:2303.03750. Number of sentences in datasets: 'nso': 5007, 'tsn': 4851, 'sot': 5075, 'xho': 5219, 'zul': 5103, 'nbl': 5600, 'ssw': 5210, 'ven': 5119, 'tso': 5193, 'af': 5252, 'eng': 5552 Train Test split: Train: 70% of minimum, 15% of minimum size, Dev: remaining sample ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact 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). - **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] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]