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README.md
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- text: 'You can email me at [email protected] or call directly on 9999988888. The point of contact would be my manager Avisekh Raj'
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example_title: Email 3
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|Class| Precision | Recall | f1 |
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| 1 | 0.98 | 1.00 | 0.91 |
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| 2 | 0.95 | 0.89 | 0.92 |
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| 3 | 0.8 | 0.88 | 0.84 |
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| macro-avg | 0.93 | 0.94 | 0.94 |
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- text: 'You can email me at [email protected] or call directly on 9999988888. The point of contact would be my manager Avisekh Raj'
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example_title: Email 3
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---
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Overview:
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The Model is fine-tuned for 3 class + "0" class.
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The Dataset is custom annotated and contains 400 texts and the model was trained on the split of 0.76, 0.12, and 0.12.
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The validation classification report is as follows:
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|Class| Precision | Recall | f1 |
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| 1 | 0.98 | 1.00 | 0.91 |
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| 2 | 0.95 | 0.89 | 0.92 |
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| 3 | 0.8 | 0.88 | 0.84 |
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| macro-avg | 0.93 | 0.94 | 0.94 |
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The test classification report is as follows:
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|Class| Precision | Recall | f1 |
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| 0 | 1.00 | 1.00 | 1.00 |
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| 1 | 0.98 | 1.00 | 0.99 |
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| 2 | 0.66 | 0.97 | 0.79 |
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| 3 | 0.84 | 0.78 | 0.81 |
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| macro-avg | 0.87 | 0.94 | 0.90 |
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Possible future direction:
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1. Clean data to a good enough format as much as possible.
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2. Increase the data as much as possible. (Make sure to have data that is seen in real-usecase.)
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