Sam4rano/nollysenti_classifier

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This is a sentiment classification model fine-tuned on the NollySenti dataset provided by David Adelani for test. It is designed to classify the sentiment of movie reviews in multiple Nigerian languages and English

Model Details

Model Description

This model is a fine-tuned version of the bert-base-multilingual-cased model for sentiment classification. It was trained on the NollySenti dataset, which contains movie reviews in English, Hausa, Igbo, Nigerian Pidgin, and Yoruba, labeled as either positive or negative sentiment.

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• Developed by: [Samuel Oyerinde]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [Sam4rano]
• Model type: [BertForSequenceClassification]
• Language(s) (NLP): [English, Hausa, Igbo, Nigerian Pidgin (pcm), Yoruba (yo)]
• License: [More Information Needed]
• Finetuned from model [optional]: [bert-base-multilingual-cased]

Model Sources [optional]

• Repository: [https://huggingface.co/Sam4rano/nollysenti_classifier]
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• Demo [optional]: [More Information Needed]

Uses

Direct Use

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Out-of-Scope Use

[This model is specifically trained for movie review sentiment analysis. It may not perform well on text from other domains or in languages it was not trained on. Using it for sensitive applications without further evaluation and fine-tuning is not recommended.]

Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Evaluation

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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