README.md

---
license: cc-by-sa-4.0
language:
- ts
- nr
- ve
- xh
- zu
- af
- en
- st
- ss
- nso
- tn
library_name: transformers
pipeline_tag: text-classification
datasets:
- dsfsi/vukuzenzele-monolingual
metrics:
- accuracy
tags:
- lid
- Language Identification
- African Languages
---

# 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](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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]

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

- **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

<!-- 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](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]

<!-- 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]

[More Information Needed]

## Model Card Contact

[More Information Needed]