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README.md
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When the sentences are longer and more practical, these ambiguities abound and affect all 3 analytics.
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## Test Data and Example Generation
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Each test example was generated using the following procedure:
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When the sentences are longer and more practical, these ambiguities abound and affect all 3 analytics.
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<details open>
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<summary>Punctuation Report</summary>
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```text
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label precision recall f1 support
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<NULL> (label_id: 0) 98.83 98.49 98.66 446496
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<ACRONYM> (label_id: 1) 74.15 94.26 83.01 697
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. (label_id: 2) 90.64 92.99 91.80 30002
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, (label_id: 3) 77.19 79.13 78.15 23321
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? (label_id: 4) 76.58 74.56 75.56 1022
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-------------------
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micro avg 97.21 97.21 97.21 501538
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macro avg 83.48 87.89 85.44 501538
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weighted avg 97.25 97.21 97.23 501538
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```
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</details>
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<details open>
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<summary>True-casing Report</summary>
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```text
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label precision recall f1 support
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LOWER (label_id: 0) 99.83 99.81 99.82 2020678
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UPPER (label_id: 1) 95.51 95.90 95.71 83873
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-------------------
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micro avg 99.66 99.66 99.66 2104551
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macro avg 97.67 97.86 97.76 2104551
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weighted avg 99.66 99.66 99.66 2104551
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```
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</details>
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<details open>
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<summary>True-casing report</summary>
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```text
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label precision recall f1 support
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NOSTOP (label_id: 0) 100.00 99.97 99.98 471608
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FULLSTOP (label_id: 1) 99.63 99.93 99.78 32923
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-------------------
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micro avg 99.97 99.97 99.97 504531
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macro avg 99.81 99.95 99.88 504531
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weighted avg 99.97 99.97 99.97 504531
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```
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</details>
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## Test Data and Example Generation
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Each test example was generated using the following procedure:
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