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---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
base_model: nlpaueb/legal-bert-base-uncased
metrics:
- accuracy
- precision
- recall
model-index:
- name: case-analysis-legal-bert-base-uncased
results: []
---
## Metrics
- loss: 1.0628
- accuracy: 0.8708
- precision: 0.8661
- recall: 0.8708
- precision_macro: 0.8180
- recall_macro: 0.6890
- macro_fpr: 0.0681
- weighted_fpr: 0.0471
- weighted_specificity: 0.8788
- macro_specificity: 0.9374
- weighted_sensitivity: 0.8708
- macro_sensitivity: 0.6890
- f1_micro: 0.8708
- f1_macro: 0.7165
- f1_weighted: 0.8586
- runtime: 13.9241
- samples_per_second: 32.2460
- steps_per_second: 4.0940
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# case-analysis-legal-bert-base-uncased
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.
It achieves the following results on the evaluation set:
- Loss: 1.3822
- Accuracy: 0.8263
- Precision: 0.8205
- Recall: 0.8263
- Precision Macro: 0.6455
- Recall Macro: 0.6413
- Macro Fpr: 0.0910
- Weighted Fpr: 0.0732
- Weighted Specificity: 0.8622
- Macro Specificity: 0.9177
- Weighted Sensitivity: 0.8085
- Macro Sensitivity: 0.6413
- F1 Micro: 0.8085
- F1 Macro: 0.6429
- F1 Weighted: 0.8061
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| No log | 1.0 | 224 | 1.0114 | 0.6570 | 0.6179 | 0.6570 | 0.4664 | 0.4075 | 0.1948 | 0.1482 | 0.6727 | 0.8324 | 0.6570 | 0.4075 | 0.6570 | 0.4081 | 0.6166 |
| No log | 2.0 | 448 | 0.7650 | 0.7751 | 0.7425 | 0.7751 | 0.5566 | 0.5806 | 0.1094 | 0.0882 | 0.8406 | 0.9039 | 0.7751 | 0.5806 | 0.7751 | 0.5659 | 0.7564 |
| 0.7677 | 3.0 | 672 | 0.7342 | 0.7817 | 0.7695 | 0.7817 | 0.5674 | 0.5967 | 0.1041 | 0.0851 | 0.8515 | 0.9083 | 0.7817 | 0.5967 | 0.7817 | 0.5707 | 0.7678 |
| 0.7677 | 4.0 | 896 | 0.7968 | 0.8174 | 0.7766 | 0.8174 | 0.6036 | 0.5893 | 0.0965 | 0.0693 | 0.8368 | 0.9136 | 0.8174 | 0.5893 | 0.8174 | 0.5904 | 0.7921 |
| 0.482 | 5.0 | 1120 | 0.8171 | 0.8085 | 0.7853 | 0.8085 | 0.6366 | 0.6038 | 0.0990 | 0.0732 | 0.8346 | 0.9108 | 0.8085 | 0.6038 | 0.8085 | 0.6141 | 0.7940 |
| 0.482 | 6.0 | 1344 | 0.8910 | 0.8241 | 0.8028 | 0.8241 | 0.6660 | 0.6359 | 0.0875 | 0.0664 | 0.8606 | 0.9212 | 0.8241 | 0.6359 | 0.8241 | 0.6315 | 0.8084 |
| 0.2993 | 7.0 | 1568 | 1.0094 | 0.8040 | 0.8109 | 0.8040 | 0.6742 | 0.6774 | 0.0891 | 0.0751 | 0.8829 | 0.9217 | 0.8040 | 0.6774 | 0.8040 | 0.6742 | 0.8067 |
| 0.2993 | 8.0 | 1792 | 1.1504 | 0.8107 | 0.7968 | 0.8107 | 0.6228 | 0.6330 | 0.0897 | 0.0722 | 0.8708 | 0.9204 | 0.8107 | 0.6330 | 0.8107 | 0.6229 | 0.8016 |
| 0.1367 | 9.0 | 2016 | 1.2533 | 0.8062 | 0.8059 | 0.8062 | 0.6537 | 0.6225 | 0.0948 | 0.0742 | 0.8595 | 0.9164 | 0.8062 | 0.6225 | 0.8062 | 0.6360 | 0.8045 |
| 0.1367 | 10.0 | 2240 | 1.2516 | 0.8174 | 0.8107 | 0.8174 | 0.6621 | 0.6499 | 0.0873 | 0.0693 | 0.8701 | 0.9219 | 0.8174 | 0.6499 | 0.8174 | 0.6554 | 0.8137 |
| 0.1367 | 11.0 | 2464 | 1.3822 | 0.8263 | 0.8205 | 0.8263 | 0.7085 | 0.6696 | 0.0833 | 0.0655 | 0.8711 | 0.9243 | 0.8263 | 0.6696 | 0.8263 | 0.6764 | 0.8195 |
| 0.055 | 12.0 | 2688 | 1.4574 | 0.8018 | 0.8127 | 0.8018 | 0.6369 | 0.6443 | 0.0883 | 0.0761 | 0.8844 | 0.9216 | 0.8018 | 0.6443 | 0.8018 | 0.6399 | 0.8068 |
| 0.055 | 13.0 | 2912 | 1.6634 | 0.7884 | 0.7810 | 0.7884 | 0.6090 | 0.6042 | 0.1002 | 0.0821 | 0.8619 | 0.9126 | 0.7884 | 0.6042 | 0.7884 | 0.6042 | 0.7831 |
| 0.0431 | 14.0 | 3136 | 1.5085 | 0.8285 | 0.8077 | 0.8285 | 0.6476 | 0.6367 | 0.0850 | 0.0645 | 0.8633 | 0.9229 | 0.8285 | 0.6367 | 0.8285 | 0.6382 | 0.8166 |
| 0.0431 | 15.0 | 3360 | 1.6411 | 0.8107 | 0.7936 | 0.8107 | 0.6243 | 0.6262 | 0.0914 | 0.0722 | 0.8626 | 0.9183 | 0.8107 | 0.6262 | 0.8107 | 0.6229 | 0.8014 |
| 0.0135 | 16.0 | 3584 | 1.7483 | 0.8062 | 0.7925 | 0.8062 | 0.6201 | 0.6271 | 0.0923 | 0.0742 | 0.8647 | 0.9177 | 0.8062 | 0.6271 | 0.8062 | 0.6221 | 0.7988 |
| 0.0135 | 17.0 | 3808 | 1.7233 | 0.7973 | 0.7897 | 0.7973 | 0.6148 | 0.6263 | 0.0942 | 0.0781 | 0.8682 | 0.9164 | 0.7973 | 0.6263 | 0.7973 | 0.6201 | 0.7933 |
| 0.0066 | 18.0 | 4032 | 1.6457 | 0.8241 | 0.8042 | 0.8241 | 0.6515 | 0.6388 | 0.0879 | 0.0664 | 0.8522 | 0.9191 | 0.8241 | 0.6388 | 0.8241 | 0.6391 | 0.8115 |
| 0.0066 | 19.0 | 4256 | 1.6614 | 0.8174 | 0.7976 | 0.8174 | 0.6324 | 0.6420 | 0.0865 | 0.0693 | 0.8703 | 0.9219 | 0.8174 | 0.6420 | 0.8174 | 0.6318 | 0.8061 |
| 0.0066 | 20.0 | 4480 | 1.6997 | 0.8129 | 0.8023 | 0.8129 | 0.6435 | 0.6576 | 0.0860 | 0.0712 | 0.8759 | 0.9222 | 0.8129 | 0.6576 | 0.8129 | 0.6462 | 0.8061 |
| 0.0067 | 21.0 | 4704 | 1.6540 | 0.8218 | 0.8000 | 0.8218 | 0.6473 | 0.6380 | 0.0880 | 0.0674 | 0.8560 | 0.9195 | 0.8218 | 0.6380 | 0.8218 | 0.6356 | 0.8088 |
| 0.0067 | 22.0 | 4928 | 1.7329 | 0.8085 | 0.7945 | 0.8085 | 0.6313 | 0.6267 | 0.0930 | 0.0732 | 0.8548 | 0.9158 | 0.8085 | 0.6267 | 0.8085 | 0.6282 | 0.8011 |
| 0.0028 | 23.0 | 5152 | 1.7949 | 0.8062 | 0.8004 | 0.8062 | 0.6365 | 0.6419 | 0.0902 | 0.0742 | 0.8708 | 0.9193 | 0.8062 | 0.6419 | 0.8062 | 0.6389 | 0.8032 |
| 0.0028 | 24.0 | 5376 | 1.8086 | 0.8085 | 0.8026 | 0.8085 | 0.6387 | 0.6429 | 0.0893 | 0.0732 | 0.8715 | 0.9200 | 0.8085 | 0.6429 | 0.8085 | 0.6405 | 0.8054 |
| 0.0001 | 25.0 | 5600 | 1.8326 | 0.8085 | 0.7988 | 0.8085 | 0.6343 | 0.6251 | 0.0934 | 0.0732 | 0.8537 | 0.9155 | 0.8085 | 0.6251 | 0.8085 | 0.6287 | 0.8028 |
| 0.0001 | 26.0 | 5824 | 1.8395 | 0.8085 | 0.7988 | 0.8085 | 0.6343 | 0.6251 | 0.0934 | 0.0732 | 0.8537 | 0.9155 | 0.8085 | 0.6251 | 0.8085 | 0.6287 | 0.8028 |
| 0.0003 | 27.0 | 6048 | 1.8816 | 0.8062 | 0.8039 | 0.8062 | 0.6439 | 0.6388 | 0.0920 | 0.0742 | 0.8621 | 0.9171 | 0.8062 | 0.6388 | 0.8062 | 0.6408 | 0.8046 |
| 0.0003 | 28.0 | 6272 | 1.8956 | 0.8062 | 0.8039 | 0.8062 | 0.6439 | 0.6388 | 0.0920 | 0.0742 | 0.8621 | 0.9171 | 0.8062 | 0.6388 | 0.8062 | 0.6408 | 0.8046 |
| 0.0003 | 29.0 | 6496 | 1.8986 | 0.8062 | 0.8039 | 0.8062 | 0.6439 | 0.6388 | 0.0920 | 0.0742 | 0.8621 | 0.9171 | 0.8062 | 0.6388 | 0.8062 | 0.6408 | 0.8046 |
| 0.0 | 30.0 | 6720 | 1.8999 | 0.8085 | 0.8045 | 0.8085 | 0.6455 | 0.6413 | 0.0910 | 0.0732 | 0.8622 | 0.9177 | 0.8085 | 0.6413 | 0.8085 | 0.6429 | 0.8061 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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