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--- |
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license: mit |
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language: |
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- cs |
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base_model: |
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- nlptown/bert-base-multilingual-uncased-sentiment |
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pipeline_tag: text-classification |
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--- |
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# Sentiment Analysis Model |
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### This model is used in our transcription service, where the audio is first transcribed and then analysed via this model. |
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The model expects a sentence and return a number from 1 to 5 where 1 is the most negative sentiment and 5 is the most positive one. There is a parsing present that checks the confidence and if it is below 0.7, it checks for the second most probable result, averages them and uses math.ceil for optimistic behavior. |
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The model is trained on BERT (nlptown/bert-base-multilingual-uncased-sentiment), which has an MIT license, and distilled llm results. |
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This model was trained for 20 epochs where the result is: |
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| | Precision | Recall | F1-score | Support | |
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|--------------|-----------|--------|----------|---------| |
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| **Class 1** | 0.95 | 0.88 | 0.92 | 43 | |
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| **Class 2** | 0.78 | 0.86 | 0.82 | 37 | |
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| **Class 3** | 0.80 | 0.72 | 0.76 | 39 | |
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| **Class 4** | 0.79 | 0.88 | 0.83 | 66 | |
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| **Class 5** | 0.85 | 0.78 | 0.81 | 45 | |
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| **Accuracy** | | | 0.83 | 230 | |
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| **Macro avg**| 0.84 | 0.82 | 0.83 | 230 | |
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| **Weighted avg** | 0.83 | 0.83 | 0.83 | 230 | |
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## sentiment_model_6: |
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| Version | Changelog | |
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|---------|------------------------------------------------------| |
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| **1.0** | initial training | |
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| **1.1** | fine-tuning time and datetime to a neutral sentiment | |
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| **1.2** | fine-tuning numbers to a neutral sentiment | |
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