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legal-bert-base-uncased

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End of training

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  1. README.md +27 -26
  2. model.safetensors +1 -1
  3. runs/Apr12_19-33-47_85119520632e/events.out.tfevents.1712951052.85119520632e.34.0 +2 -2
README.md CHANGED
@@ -1,8 +1,8 @@
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  ---
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  license: cc-by-sa-4.0
 
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  tags:
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  - generated_from_trainer
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- base_model: nlpaueb/legal-bert-base-uncased
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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 [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2259
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- - Accuracy: 0.2455
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- - Precision: 0.0603
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- - Recall: 0.2455
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- - Precision Macro: 0.0164
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- - Recall Macro: 0.0667
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- - Macro Fpr: 0.0667
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- - Weighted Fpr: 0.1800
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- - Weighted Specificity: 0.7545
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- - Macro Specificity: 0.9333
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- - Weighted Sensitivity: 0.2455
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- - Macro Sensitivity: 0.0667
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- - F1 Micro: 0.2455
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- - F1 Macro: 0.0263
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- - F1 Weighted: 0.0968
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  ## Model description
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@@ -59,21 +59,22 @@ The following hyperparameters were used during training:
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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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- | 2.2376 | 1.0 | 643 | 2.2455 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2504 | 2.0 | 1286 | 2.2412 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2292 | 3.0 | 1929 | 2.2300 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.218 | 4.0 | 2572 | 2.2316 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2317 | 5.0 | 3215 | 2.2295 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2355 | 6.0 | 3858 | 2.2310 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2231 | 7.0 | 4501 | 2.2300 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2212 | 8.0 | 5144 | 2.2291 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2318 | 9.0 | 5787 | 2.2258 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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- | 2.2128 | 10.0 | 6430 | 2.2259 | 0.2455 | 0.0603 | 0.2455 | 0.0164 | 0.0667 | 0.0667 | 0.1800 | 0.7545 | 0.9333 | 0.2455 | 0.0667 | 0.2455 | 0.0263 | 0.0968 |
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  ### Framework versions
 
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  ---
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  license: cc-by-sa-4.0
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+ base_model: nlpaueb/legal-bert-base-uncased
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  tags:
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  - generated_from_trainer
 
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  metrics:
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  - accuracy
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  - precision
 
19
 
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  This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2564
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+ - Accuracy: 0.8273
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+ - Precision: 0.8292
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+ - Recall: 0.8273
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+ - Precision Macro: 0.7794
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+ - Recall Macro: 0.7759
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+ - Macro Fpr: 0.0153
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+ - Weighted Fpr: 0.0147
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+ - Weighted Specificity: 0.9772
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+ - Macro Specificity: 0.9870
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+ - Weighted Sensitivity: 0.8273
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+ - Macro Sensitivity: 0.7759
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+ - F1 Micro: 0.8273
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+ - F1 Macro: 0.7741
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+ - F1 Weighted: 0.8269
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  ## Model description
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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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+ - 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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+ | 1.2105 | 1.0 | 643 | 0.7916 | 0.7761 | 0.7729 | 0.7761 | 0.6230 | 0.5920 | 0.0214 | 0.0202 | 0.9664 | 0.9828 | 0.7761 | 0.5920 | 0.7761 | 0.5703 | 0.7551 |
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+ | 0.6521 | 2.0 | 1286 | 0.6834 | 0.8025 | 0.8067 | 0.8025 | 0.7779 | 0.7152 | 0.0180 | 0.0173 | 0.9721 | 0.9850 | 0.8025 | 0.7152 | 0.8025 | 0.7181 | 0.7983 |
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+ | 0.513 | 3.0 | 1929 | 0.8107 | 0.8141 | 0.8142 | 0.8141 | 0.7859 | 0.7227 | 0.0168 | 0.0160 | 0.9740 | 0.9859 | 0.8141 | 0.7227 | 0.8141 | 0.7261 | 0.8083 |
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+ | 0.2635 | 4.0 | 2572 | 0.8442 | 0.8249 | 0.8285 | 0.8249 | 0.8298 | 0.7733 | 0.0156 | 0.0149 | 0.9759 | 0.9867 | 0.8249 | 0.7733 | 0.8249 | 0.7812 | 0.8242 |
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+ | 0.1821 | 5.0 | 3215 | 0.9549 | 0.8226 | 0.8287 | 0.8226 | 0.8135 | 0.7623 | 0.0157 | 0.0152 | 0.9766 | 0.9866 | 0.8226 | 0.7623 | 0.8226 | 0.7758 | 0.8233 |
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+ | 0.1123 | 6.0 | 3858 | 1.0790 | 0.8273 | 0.8316 | 0.8273 | 0.7865 | 0.7758 | 0.0152 | 0.0147 | 0.9779 | 0.9870 | 0.8273 | 0.7758 | 0.8273 | 0.7671 | 0.8268 |
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+ | 0.0465 | 7.0 | 4501 | 1.1538 | 0.8280 | 0.8324 | 0.8280 | 0.7857 | 0.8054 | 0.0152 | 0.0146 | 0.9780 | 0.9871 | 0.8280 | 0.8054 | 0.8280 | 0.7890 | 0.8285 |
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+ | 0.0256 | 8.0 | 5144 | 1.2413 | 0.8180 | 0.8263 | 0.8180 | 0.7780 | 0.8012 | 0.0162 | 0.0156 | 0.9771 | 0.9863 | 0.8180 | 0.8012 | 0.8180 | 0.7792 | 0.8196 |
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+ | 0.0166 | 9.0 | 5787 | 1.2510 | 0.8218 | 0.8222 | 0.8218 | 0.7782 | 0.7600 | 0.0159 | 0.0152 | 0.9755 | 0.9865 | 0.8218 | 0.7600 | 0.8218 | 0.7660 | 0.8210 |
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+ | 0.0107 | 10.0 | 6430 | 1.2564 | 0.8273 | 0.8292 | 0.8273 | 0.7794 | 0.7759 | 0.0153 | 0.0147 | 0.9772 | 0.9870 | 0.8273 | 0.7759 | 0.8273 | 0.7741 | 0.8269 |
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  ### Framework versions
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