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InLegalBERT

Text Classification
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bert
Generated from Trainer
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End of training

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  1. README.md +33 -27
  2. model.safetensors +1 -1
  3. runs/Apr14_02-25-35_3e1d3a604afe/events.out.tfevents.1713061536.3e1d3a604afe.34.2 +2 -2
README.md CHANGED
@@ -1,8 +1,8 @@
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  ---
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  license: mit
 
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  tags:
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  - generated_from_trainer
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- base_model: law-ai/InLegalBERT
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  metrics:
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  - accuracy
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  - precision
@@ -19,21 +19,21 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.5527
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- - Accuracy: 0.7591
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- - Precision: 0.7598
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- - Recall: 0.7591
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- - Precision Macro: 0.6792
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- - Recall Macro: 0.6780
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- - Macro Fpr: 0.0228
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- - Weighted Fpr: 0.0222
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- - Weighted Specificity: 0.9703
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- - Macro Specificity: 0.9820
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- - Weighted Sensitivity: 0.7591
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- - Macro Sensitivity: 0.6780
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- - F1 Micro: 0.7591
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- - F1 Macro: 0.6756
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- - F1 Weighted: 0.7583
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  ## Model description
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@@ -58,22 +58,28 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 10
 
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
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- | 1.9079 | 1.0 | 643 | 1.2971 | 0.5732 | 0.5257 | 0.5732 | 0.3206 | 0.3555 | 0.0535 | 0.0505 | 0.9314 | 0.9670 | 0.5732 | 0.3555 | 0.5732 | 0.3189 | 0.5343 |
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- | 1.2081 | 2.0 | 1286 | 0.9146 | 0.7103 | 0.7163 | 0.7103 | 0.6091 | 0.5215 | 0.0287 | 0.0283 | 0.9651 | 0.9784 | 0.7103 | 0.5215 | 0.7103 | 0.5206 | 0.7070 |
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- | 0.9303 | 3.0 | 1929 | 0.8692 | 0.7405 | 0.7472 | 0.7405 | 0.6654 | 0.5940 | 0.0248 | 0.0244 | 0.9679 | 0.9806 | 0.7405 | 0.5940 | 0.7405 | 0.5993 | 0.7362 |
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- | 0.4996 | 4.0 | 2572 | 1.1656 | 0.7033 | 0.7270 | 0.7033 | 0.6366 | 0.6241 | 0.0297 | 0.0292 | 0.9651 | 0.9779 | 0.7033 | 0.6241 | 0.7033 | 0.6125 | 0.6959 |
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- | 0.3592 | 5.0 | 3215 | 1.0837 | 0.7459 | 0.7535 | 0.7459 | 0.6627 | 0.6131 | 0.0241 | 0.0238 | 0.9668 | 0.9808 | 0.7459 | 0.6131 | 0.7459 | 0.6261 | 0.7447 |
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- | 0.2809 | 6.0 | 3858 | 1.2175 | 0.7545 | 0.7607 | 0.7545 | 0.6758 | 0.6585 | 0.0232 | 0.0227 | 0.9695 | 0.9816 | 0.7545 | 0.6585 | 0.7545 | 0.6599 | 0.7531 |
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- | 0.1664 | 7.0 | 4501 | 1.3113 | 0.7637 | 0.7645 | 0.7637 | 0.6855 | 0.6886 | 0.0221 | 0.0216 | 0.9717 | 0.9824 | 0.7637 | 0.6886 | 0.7637 | 0.6841 | 0.7631 |
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- | 0.0733 | 8.0 | 5144 | 1.4751 | 0.7552 | 0.7610 | 0.7552 | 0.6835 | 0.6990 | 0.0231 | 0.0226 | 0.9697 | 0.9817 | 0.7552 | 0.6990 | 0.7552 | 0.6871 | 0.7566 |
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- | 0.0716 | 9.0 | 5787 | 1.5509 | 0.7637 | 0.7605 | 0.7637 | 0.7018 | 0.7035 | 0.0224 | 0.0216 | 0.9690 | 0.9822 | 0.7637 | 0.7035 | 0.7637 | 0.7006 | 0.7609 |
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- | 0.0286 | 10.0 | 6430 | 1.5527 | 0.7591 | 0.7598 | 0.7591 | 0.6792 | 0.6780 | 0.0228 | 0.0222 | 0.9703 | 0.9820 | 0.7591 | 0.6780 | 0.7591 | 0.6756 | 0.7583 |
 
 
 
 
 
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  ### Framework versions
 
1
  ---
2
  license: mit
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+ base_model: law-ai/InLegalBERT
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  tags:
5
  - generated_from_trainer
 
6
  metrics:
7
  - accuracy
8
  - precision
 
19
 
20
  This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on an unknown dataset.
21
  It achieves the following results on the evaluation set:
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+ - Loss: 1.5914
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+ - Accuracy: 0.8296
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+ - Precision: 0.8293
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+ - Recall: 0.8296
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+ - Precision Macro: 0.7959
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+ - Recall Macro: 0.8029
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+ - Macro Fpr: 0.0150
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+ - Weighted Fpr: 0.0145
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+ - Weighted Specificity: 0.9774
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+ - Macro Specificity: 0.9871
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+ - Weighted Sensitivity: 0.8296
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+ - Macro Sensitivity: 0.8029
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+ - F1 Micro: 0.8296
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+ - F1 Macro: 0.7954
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+ - F1 Weighted: 0.8283
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 15
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+ - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
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+ | 1.065 | 1.0 | 643 | 0.6395 | 0.7994 | 0.7818 | 0.7994 | 0.6194 | 0.6308 | 0.0185 | 0.0176 | 0.9714 | 0.9847 | 0.7994 | 0.6308 | 0.7994 | 0.6029 | 0.7804 |
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+ | 0.5866 | 2.0 | 1286 | 0.6907 | 0.8187 | 0.8199 | 0.8187 | 0.7285 | 0.7366 | 0.0161 | 0.0156 | 0.9765 | 0.9864 | 0.8187 | 0.7366 | 0.8187 | 0.7276 | 0.8152 |
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+ | 0.4622 | 3.0 | 1929 | 0.8056 | 0.8180 | 0.8137 | 0.8180 | 0.7227 | 0.7376 | 0.0162 | 0.0156 | 0.9764 | 0.9863 | 0.8180 | 0.7376 | 0.8180 | 0.7283 | 0.8150 |
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+ | 0.2398 | 4.0 | 2572 | 0.9310 | 0.8172 | 0.8235 | 0.8172 | 0.7661 | 0.7425 | 0.0161 | 0.0157 | 0.9762 | 0.9862 | 0.8172 | 0.7425 | 0.8172 | 0.7407 | 0.8161 |
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+ | 0.1611 | 5.0 | 3215 | 1.0763 | 0.8304 | 0.8363 | 0.8304 | 0.8174 | 0.7918 | 0.0148 | 0.0144 | 0.9784 | 0.9873 | 0.8304 | 0.7918 | 0.8304 | 0.7986 | 0.8304 |
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+ | 0.1055 | 6.0 | 3858 | 1.1377 | 0.8257 | 0.8275 | 0.8257 | 0.8039 | 0.7810 | 0.0154 | 0.0149 | 0.9775 | 0.9869 | 0.8257 | 0.7810 | 0.8257 | 0.7863 | 0.8246 |
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+ | 0.0463 | 7.0 | 4501 | 1.3215 | 0.8071 | 0.8111 | 0.8071 | 0.7692 | 0.7689 | 0.0172 | 0.0168 | 0.9761 | 0.9856 | 0.8071 | 0.7689 | 0.8071 | 0.7661 | 0.8078 |
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+ | 0.031 | 8.0 | 5144 | 1.3483 | 0.8203 | 0.8170 | 0.8203 | 0.7773 | 0.7727 | 0.0161 | 0.0154 | 0.9751 | 0.9864 | 0.8203 | 0.7727 | 0.8203 | 0.7690 | 0.8175 |
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+ | 0.0202 | 9.0 | 5787 | 1.3730 | 0.8280 | 0.8263 | 0.8280 | 0.7818 | 0.7803 | 0.0152 | 0.0146 | 0.9779 | 0.9871 | 0.8280 | 0.7803 | 0.8280 | 0.7753 | 0.8256 |
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+ | 0.0133 | 10.0 | 6430 | 1.5407 | 0.8164 | 0.8163 | 0.8164 | 0.7688 | 0.7779 | 0.0165 | 0.0158 | 0.9751 | 0.9861 | 0.8164 | 0.7779 | 0.8164 | 0.7655 | 0.8135 |
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+ | 0.0051 | 11.0 | 7073 | 1.5235 | 0.8226 | 0.8265 | 0.8226 | 0.7900 | 0.7680 | 0.0156 | 0.0152 | 0.9769 | 0.9866 | 0.8226 | 0.7680 | 0.8226 | 0.7744 | 0.8234 |
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+ | 0.0027 | 12.0 | 7716 | 1.5643 | 0.8265 | 0.8259 | 0.8265 | 0.7805 | 0.7841 | 0.0154 | 0.0148 | 0.9772 | 0.9869 | 0.8265 | 0.7841 | 0.8265 | 0.7775 | 0.8245 |
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+ | 0.002 | 13.0 | 8359 | 1.5516 | 0.8280 | 0.8273 | 0.8280 | 0.7882 | 0.7902 | 0.0152 | 0.0146 | 0.9779 | 0.9871 | 0.8280 | 0.7902 | 0.8280 | 0.7860 | 0.8262 |
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+ | 0.0015 | 14.0 | 9002 | 1.5835 | 0.8273 | 0.8268 | 0.8273 | 0.7943 | 0.8022 | 0.0153 | 0.0147 | 0.9773 | 0.9870 | 0.8273 | 0.8022 | 0.8273 | 0.7943 | 0.8259 |
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+ | 0.0007 | 15.0 | 9645 | 1.5914 | 0.8296 | 0.8293 | 0.8296 | 0.7959 | 0.8029 | 0.0150 | 0.0145 | 0.9774 | 0.9871 | 0.8296 | 0.8029 | 0.8296 | 0.7954 | 0.8283 |
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  ### Framework versions
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