| | --- |
| | language: |
| | - 'no' |
| | --- |
| | ## Model Description |
| | This model builds upon the pre-trained NB-BERT model developed by Nasjonalbibloteket, which is designed to handle the nuances of the Norwegian language. The fine-tuning process tailored the model to understand and classify the sentiment of Norwegian governmental communications, particularly during the COVID-19 pandemic |
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| | ## Use cases |
| | Sentiment Analysis of Governmental Communication: |
| | Objective: Analyze and classify the sentiment of official governmental communications from Norwegian authorities. |
| | Example: Categorizing tweets from the Norwegian health department and government announcements to understand public sentiment trends. |
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| | ## Limitations |
| | The model is fine-tuned specifically for Norwegian |
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| | The model is only trained for government specific sentiment analysis |
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| | ## Training data |
| | Source: The datasets used include: |
| | Twitter data from the Norwegian Health Department (Folkehelseinstituttet) and the government (Rejeringen). |
| | A timeline dataset of the Norwegian government's handling of the COVID-19 pandemic. |
| | The labeling process utilized a combination of Chatgpt4 and manuel corrections, the fine-tuning is included |
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| | ## Credit |
| | This work builds upon the NB-BERT model. For more information on the original NB-BERT model, please refer to the Nasjonalbibloteket community’s publication: https://github.com/NBAiLab/notram |
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