2nd LL
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README.md
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- Accuracy: 0.936
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## Model description
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More information needed
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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- Accuracy: 0.936
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## Model description
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This model is a simple Pytorch Custom Model that uses BERT to classify the emotions of a given text
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## Intended uses & limitations
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- It only supports English for now (am willing to add French next)
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- The input text has a limit in size, it can suit a mid-size paragraph easily but can't handle large documents (you can bypass this by dividing the document to paragraphs and make a weights summation)
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- The emotions it can recognize are limited (the 6 major emotions) so it can't detail to mixed psychological outcomes
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- Fine Tuning time : well we all know how BERT can be slow sometimes so i suggest for anyone who wants to develop over the idea to use DistelBERT for faster results
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## Training and evaluation data
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This dataset contains two key columns: 'text' and 'label'. The 'label' column represents six different emotion classes: sadness (0), joy (1), love (2), anger (3), fear (4), and surprise (5). Get ready to dive deep into the world of human emotions
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## Training procedure
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