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dsfsi
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za-lid-bert

Text Classification
Transformers
Safetensors
bert
lid
Language Identification
African Languages
Inference Endpoints
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metadata
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

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

  • 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 presented in Lacoste et al. (2019).

  • 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

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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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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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