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Token Classification
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lilt
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End of training

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Files changed (5) hide show
  1. README.md +22 -22
  2. logs/events.out.tfevents.1695903227.Benjamins-MacBook-Pro.local.21816.0 +2 -2
  3. preprocessor_config.json +1 -1
  4. pytorch_model.bin +1 -1
  5. tokenizer.json +16 -2
README.md CHANGED
@@ -14,14 +14,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0292
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- - Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10}
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- - Header: {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17}
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- - Question: {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17}
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- - Overall Precision: 0.0556
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- - Overall Recall: 0.0455
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- - Overall F1: 0.0500
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- - Overall Accuracy: 0.6578
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  ## Model description
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@@ -50,20 +50,20 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.1533 | 3.92 | 200 | 0.0173 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0 | 0.0 | 0.0 | 0.5989 |
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- | 0.0443 | 7.84 | 400 | 0.0137 | {'precision': 0.09090909090909091, 'recall': 0.1, 'f1': 0.09523809523809525, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0164 | 0.0227 | 0.0190 | 0.6203 |
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- | 0.0194 | 11.76 | 600 | 0.0162 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.2222222222222222, 'recall': 0.11764705882352941, 'f1': 0.15384615384615383, 'number': 17} | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | 0.1154 | 0.0682 | 0.0857 | 0.6417 |
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- | 0.0089 | 15.69 | 800 | 0.0186 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.06666666666666667, 'recall': 0.058823529411764705, 'f1': 0.0625, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0278 | 0.0227 | 0.0250 | 0.6684 |
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- | 0.0036 | 19.61 | 1000 | 0.0264 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17} | 0.0556 | 0.0455 | 0.0500 | 0.6578 |
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- | 0.0031 | 23.53 | 1200 | 0.0200 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.1111111111111111, 'recall': 0.11764705882352941, 'f1': 0.11428571428571428, 'number': 17} | {'precision': 0.05263157894736842, 'recall': 0.058823529411764705, 'f1': 0.05555555555555555, 'number': 17} | 0.0732 | 0.0682 | 0.0706 | 0.6791 |
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- | 0.0015 | 27.45 | 1400 | 0.0222 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.05, 'recall': 0.058823529411764705, 'f1': 0.05405405405405405, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0196 | 0.0227 | 0.0211 | 0.6631 |
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- | 0.0011 | 31.37 | 1600 | 0.0249 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.045454545454545456, 'recall': 0.058823529411764705, 'f1': 0.05128205128205128, 'number': 17} | 0.0408 | 0.0455 | 0.0430 | 0.6845 |
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- | 0.0006 | 35.29 | 1800 | 0.0243 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17} | 0.0213 | 0.0227 | 0.0220 | 0.6952 |
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- | 0.0004 | 39.22 | 2000 | 0.0290 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | 0.0488 | 0.0455 | 0.0471 | 0.6578 |
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- | 0.0002 | 43.14 | 2200 | 0.0288 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07142857142857142, 'recall': 0.058823529411764705, 'f1': 0.06451612903225808, 'number': 17} | {'precision': 0.05555555555555555, 'recall': 0.058823529411764705, 'f1': 0.05714285714285714, 'number': 17} | 0.0488 | 0.0455 | 0.0471 | 0.6578 |
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- | 0.0002 | 47.06 | 2400 | 0.0292 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.07692307692307693, 'recall': 0.058823529411764705, 'f1': 0.06666666666666667, 'number': 17} | {'precision': 0.0625, 'recall': 0.058823529411764705, 'f1': 0.06060606060606061, 'number': 17} | 0.0556 | 0.0455 | 0.0500 | 0.6578 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0582
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+ - Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
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+ - Header: {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13}
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+ - Question: {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13}
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+ - Overall Precision: 0.0682
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+ - Overall Recall: 0.0882
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+ - Overall F1: 0.0769
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+ - Overall Accuracy: 0.6434
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.1677 | 3.08 | 200 | 0.0239 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | 0.0 | 0.0 | 0.0 | 0.7295 |
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+ | 0.0578 | 6.15 | 400 | 0.0251 | {'precision': 0.4, 'recall': 0.25, 'f1': 0.3076923076923077, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | 0.1333 | 0.0588 | 0.0816 | 0.7295 |
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+ | 0.0275 | 9.23 | 600 | 0.0291 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05555555555555555, 'recall': 0.07692307692307693, 'f1': 0.06451612903225808, 'number': 13} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | 0.0526 | 0.0588 | 0.0556 | 0.7008 |
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+ | 0.0124 | 12.31 | 800 | 0.0401 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0625, 'recall': 0.07692307692307693, 'f1': 0.06896551724137931, 'number': 13} | 0.0303 | 0.0294 | 0.0299 | 0.6352 |
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+ | 0.0086 | 15.38 | 1000 | 0.0416 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | 0.0513 | 0.0588 | 0.0548 | 0.6311 |
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+ | 0.0045 | 18.46 | 1200 | 0.0447 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | 0.0 | 0.0 | 0.0 | 0.6639 |
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+ | 0.0027 | 21.54 | 1400 | 0.0467 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05, 'recall': 0.07692307692307693, 'f1': 0.060606060606060615, 'number': 13} | {'precision': 0.09523809523809523, 'recall': 0.15384615384615385, 'f1': 0.11764705882352941, 'number': 13} | 0.0667 | 0.0882 | 0.0759 | 0.6639 |
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+ | 0.0013 | 24.62 | 1600 | 0.0494 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.045454545454545456, 'recall': 0.07692307692307693, 'f1': 0.05714285714285715, 'number': 13} | {'precision': 0.08695652173913043, 'recall': 0.15384615384615385, 'f1': 0.1111111111111111, 'number': 13} | 0.0612 | 0.0882 | 0.0723 | 0.6434 |
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+ | 0.0009 | 27.69 | 1800 | 0.0559 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.045454545454545456, 'recall': 0.07692307692307693, 'f1': 0.05714285714285715, 'number': 13} | {'precision': 0.08695652173913043, 'recall': 0.15384615384615385, 'f1': 0.1111111111111111, 'number': 13} | 0.06 | 0.0882 | 0.0714 | 0.6475 |
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+ | 0.0006 | 30.77 | 2000 | 0.0522 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0625, 'recall': 0.07692307692307693, 'f1': 0.06896551724137931, 'number': 13} | {'precision': 0.05555555555555555, 'recall': 0.07692307692307693, 'f1': 0.06451612903225808, 'number': 13} | 0.0526 | 0.0588 | 0.0556 | 0.6393 |
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+ | 0.0004 | 33.85 | 2200 | 0.0557 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13} | 0.0682 | 0.0882 | 0.0769 | 0.6516 |
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+ | 0.0005 | 36.92 | 2400 | 0.0582 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13} | 0.0682 | 0.0882 | 0.0769 | 0.6434 |
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  ### Framework versions
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