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Lelapa-X-NER (Multilingual)

Model Details

Basic information about the model: Review section 4.1 of the model cards paper.

OrganizationLelapa AI
ProductVulavula
Model date7 November 2023
FeatureASR
LangisiZulu, seSotho, Afrikaans, Sestwana, isiXhosa
DomainNews, Government
Model NameLelapa-X-NER (Multilingual)
Model version1.0.0
Model TypeFine-Tuned Proprietary Model

Information about training algorithms, parameters, fairness constraints or other applied approaches, and features: Proprietary Fine-tuning of a Base Model on Text Data

License: Proprietary

Contact: info@lelapa.ai

Intended use

Use cases that were envisioned during development: Review section 4.2 of the model cards paper.

Primary intended uses

Intended use is governed by the language and domain of the model. The model is intended to be used in the news and government domains for Named Entity Recognition in isiZulu, seSotho, Afrikaans, Sestwana and isiXhosa. The model is not suitable for domains different from News Articles and Government data and should be used with caution.

Primary intended users

The NER model can be used for :

  • News Aggregation
  • Research
  • Document Analysis
  • Government Document Analysis

Out-of-scope use cases

All languages and domains outside of NER Classification for isiZulu, seSotho, Afrikaans, Sestwana and isiXhosa.

Factors

Factors could include demographic or phenotypic groups, environmental conditions, technical attributes, or others listed in Section 4.3: Review section 4.3 of the model cards paper.

Relevant factors

Groups:

  • The annotators assigned and their level of understanding of the task is one of the relevant factors. There is no record of the demographic information about the annotators.

Environmental conditions, Instrumentation & Technical attributes:

  • The NER tags used are limited to Personal name (PER), Location (LOC), Organization (ORG), date \& time (DATE). and Other (O)

Evaluation factors

  • In our development setting (training and evaluation), we used the tags described above to determine model accuracy.

Metrics

The appropriate metrics to feature in a model card depend on the type of model that is being tested. For example, classification systems in which the primary output is a class label differ significantly from systems whose primary output is a score. In all cases, the reported metrics should be determined based on the model’s structure and intended use: Review section 4.4 of the model cards paper.

Model performance measures

The model is evaluated using F1 Score: the models’ performances are measured by automatic metric. As an automatic metric, F1 Score is an harmonic mean of the Precision and Recall. Precision tells us how well the model avoids mistakenly labeling something as a named entity when it’s not. Recall tells us how well the model captures all the actual named entities in the text. So a higher F1 score generally means the model is doing a good job of finding the right names and not mistakenly labeling other tokens Read more.

F1 score: Testing on NER test set in isiZulu and NCHLT (seSotho, Afrikaans, Sestwana, isiXhosa)

Decision thresholds

No decision thresholds have been specified

Evaluation data

All referenced datasets would ideally point to any set of documents that provide visibility into the source and composition of the dataset. Evaluation datasets should include datasets that are publicly available for third-party use. These could be existing datasets or new ones provided alongside the model card analyses to enable further benchmarking.

Review section 4.5 of the model cards paper.

Datasets

  • Publicly available isiZulu NER datasets in the News domain.
  • Publicly available seSotho, Afrikaans, Sestwana and isiXhosa dataset in Government domain.

Motivation

These datasets have been selected because they are open-source, high-quality, and cover the targeted languages . These help to capture interesting cultural and linguistic aspects that would be crucial in the development process for better performance.

Training data

Review section 4.6 of the model cards paper.

Refer to the datasheet provided

Quantitative analyses

Quantitative analyses should be disaggregated, that is, broken down by the chosen factors. Quantitative analyses should provide the results of evaluating the model according to the chosen metrics, providing confidence interval values when possible.

Review section 4.7 of the model cards paper.

Unitary results

LANGUAGESF1: PERSONF1: ORGANISATIONF1: LOCATIONF1: MISCF1 Avg
Afrikaans0.890.850.930.770.83
isiZulu0.960.850.920.86 (DATE)0.913
SeSotho0.900.840.850.80 (DATE)0.87
Unofficially Supported Languages
isiXhosa0.790.560.830.690.69
Northern Sotho0.830.800.890.770.81
Setswana0.680.600.820.710.71

Intersectional result

In progress

Ethical considerations

This section is intended to demonstrate the ethical considerations that went into model development, surfacing ethical challenges and solutions to stakeholders. The ethical analysis does not always lead to precise solutions, but the process of ethical contemplation is worthwhile to inform on responsible practices and next steps in future work: Review section 4.8 of the model cards paper.

All call center data is synthetic and so the model does not contain any personal information. More details in the datasheet.

Caveats and recommendations

This section should list additional concerns that were not covered in the previous sections.

Review section 4.9 of the model cards paper.

Additional caveats are outlined extensively in our Terms and Conditions.