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
- Developed by: Thapelo Sindane, Vukosi Marivate
- 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-BY-SA
- 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
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Speeds, Sizes, Times [optional]
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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).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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