Text Classification
Transformers
PyTorch
Safetensors
Tswana
roberta
iptc
Inference Endpoints
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@@ -32,16 +32,18 @@ This is a News Categorisation model for Setswana.
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  ### News Categories
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- 0: arts_culture_entertainment_and_media
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- 1: crime_law_and_justice
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- 2: disaster_accident_and_emergency_incident
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- 3: economy_business_and_finance
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- 4: education
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- 5: environment
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- 6: health
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- 7: politics
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- 8: religion_and_belief
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- 9: society
 
 
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  ### Model Performance
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@@ -51,8 +53,8 @@ Performance of models on Daily News Dikgang dataset
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  |-----------------------------|--------------------------------------|-------------------|
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  | Logistic Regression + TFIDF | 60.1 | 56.2 |
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  | NCHLT TSN RoBERTa | 64.7 | 60.3 |
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- | PuoBERTa | 63.8 | 62.9 |
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- | PuoBERTaJW300 | extbf{66.2} | **65.4**
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  ### Usage
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  ### News Categories
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+ We use the IPTC news codes [https://iptc.org/standards/newscodes/](https://iptc.org/standards/newscodes/)
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+
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+ 0. arts_culture_entertainment_and_media (Botsweretshi, setso, boitapoloso le bobegakgang)
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+ 1. crime_law_and_justice (Bosenyi, molao le bosiamisi)
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+ 2. disaster_accident_and_emergency_incident (Masetlapelo, kotsi le tiragalo ya maemo a tshoganyetso)
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+ 3. economy_business_and_finance (Ikonomi, tsa kgwebo le tsa ditšhelete)
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+ 4. education (Thuto)
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+ 5. environment (Tikologo)
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+ 6. health (Boitekanelo)
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+ 7. politics (Dipolotiki)
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+ 8. religion_and_belief (Bodumedi le tumelo)
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+ 9. society (Setšhaba)
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  ### Model Performance
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  |-----------------------------|--------------------------------------|-------------------|
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  | Logistic Regression + TFIDF | 60.1 | 56.2 |
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  | NCHLT TSN RoBERTa | 64.7 | 60.3 |
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+ | PuoBERTa | **63.8** | **62.9** |
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+ | PuoBERTaJW300 | 66.2 | *65.4* |
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  ### Usage
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