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Update README.md

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@@ -65,4 +65,20 @@ outputs = predict(path, sampling_rate)
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  {'Emotion': 'fear', 'Score': '0.4%'},
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  {'Emotion': 'happiness', 'Score': '0.7%'},
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  {'Emotion': 'sadness', 'Score': '0.5%'}]
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- ~~~
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  {'Emotion': 'fear', 'Score': '0.4%'},
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  {'Emotion': 'happiness', 'Score': '0.7%'},
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  {'Emotion': 'sadness', 'Score': '0.5%'}]
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+ ~~~
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+
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+ ## Evaluation
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+ The following tables summarize the scores obtained by model overall and per each class.
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+
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+
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+ | Emotions | precision | recall | f1-score | accuracy |
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+ |-----------|-----------|--------|----------|----------|
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+ | anger | 0.82 | 1.00 | 0.81 | |
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+ | disgust | 0.85 | 0.96 | 0.85 | |
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+ | fear | 0.78 | 0.88 | 0.80 | |
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+ | happiness | 0.84 | 0.71 | 0.78 | |
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+ | sadness | 0.86 | 1.00 | 0.79 | |
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+ | | | | Overall | 0.806 |
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+
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+
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+ ##