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---
license: mit
tags:
- generated_from_trainer
base_model: law-ai/InLegalBERT
metrics:
- accuracy
- precision
- recall
model-index:
- name: case-analysis-InLegalBERT
results: []
---
## Metrics
- loss: 1.0434
- accuracy: 0.8218
- precision: 0.8145
- recall: 0.8218
- precision_macro: 0.6907
- recall_macro: 0.6533
- macro_fpr: 0.0897
- weighted_fpr: 0.0674
- weighted_specificity: 0.8528
- macro_specificity: 0.9187
- weighted_sensitivity: 0.8218
- macro_sensitivity: 0.6533
- f1_micro: 0.8218
- f1_macro: 0.6690
- f1_weighted: 0.8159
- runtime: 198.6459
- samples_per_second: 2.2600
- steps_per_second: 0.2870
<!-- 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-InLegalBERT
This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0434
- Accuracy: 0.8218
- Precision: 0.8145
- Recall: 0.8218
- Precision Macro: 0.6439
- Recall Macro: 0.6295
- Macro Fpr: 0.0890
- Weighted Fpr: 0.0674
- Weighted Specificity: 0.8544
- Macro Specificity: 0.9191
- Weighted Sensitivity: 0.8218
- Macro Sensitivity: 0.6295
- F1 Micro: 0.8218
- F1 Macro: 0.6335
- F1 Weighted: 0.8106
## 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 | 0.6546 | 0.8018 | 0.7632 | 0.8018 | 0.5777 | 0.6106 | 0.0978 | 0.0761 | 0.8432 | 0.9112 | 0.8018 | 0.6106 | 0.8018 | 0.5936 | 0.7820 |
| No log | 2.0 | 448 | 0.6831 | 0.8129 | 0.7732 | 0.8129 | 0.5845 | 0.6154 | 0.0923 | 0.0712 | 0.8554 | 0.9171 | 0.8129 | 0.6154 | 0.8129 | 0.5996 | 0.7926 |
| 0.607 | 3.0 | 672 | 0.7626 | 0.8263 | 0.8060 | 0.8263 | 0.6773 | 0.6341 | 0.0885 | 0.0655 | 0.8464 | 0.9182 | 0.8263 | 0.6341 | 0.8263 | 0.6362 | 0.8105 |
| 0.607 | 4.0 | 896 | 0.7839 | 0.8085 | 0.7991 | 0.8085 | 0.6391 | 0.6306 | 0.0896 | 0.0732 | 0.8754 | 0.9210 | 0.8085 | 0.6306 | 0.8085 | 0.6314 | 0.8017 |
| 0.316 | 5.0 | 1120 | 0.9381 | 0.8263 | 0.8127 | 0.8263 | 0.6688 | 0.6573 | 0.0822 | 0.0655 | 0.8780 | 0.9261 | 0.8263 | 0.6573 | 0.8263 | 0.6514 | 0.8161 |
| 0.316 | 6.0 | 1344 | 1.0434 | 0.8218 | 0.8145 | 0.8218 | 0.6907 | 0.6533 | 0.0897 | 0.0674 | 0.8528 | 0.9187 | 0.8218 | 0.6533 | 0.8218 | 0.6690 | 0.8159 |
| 0.1513 | 7.0 | 1568 | 1.2182 | 0.8018 | 0.8066 | 0.8018 | 0.6382 | 0.6399 | 0.0916 | 0.0761 | 0.8802 | 0.9205 | 0.8018 | 0.6399 | 0.8018 | 0.6375 | 0.8030 |
| 0.1513 | 8.0 | 1792 | 1.3193 | 0.8285 | 0.8070 | 0.8285 | 0.6566 | 0.6280 | 0.0882 | 0.0645 | 0.8521 | 0.9202 | 0.8285 | 0.6280 | 0.8285 | 0.6376 | 0.8152 |
| 0.0491 | 9.0 | 2016 | 1.3169 | 0.8330 | 0.8180 | 0.8330 | 0.6950 | 0.6555 | 0.0828 | 0.0627 | 0.8653 | 0.9246 | 0.8330 | 0.6555 | 0.8330 | 0.6687 | 0.8235 |
| 0.0491 | 10.0 | 2240 | 1.4460 | 0.8307 | 0.8109 | 0.8307 | 0.6584 | 0.6291 | 0.0868 | 0.0636 | 0.8533 | 0.9210 | 0.8307 | 0.6291 | 0.8307 | 0.6398 | 0.8184 |
| 0.0491 | 11.0 | 2464 | 1.4100 | 0.8419 | 0.8166 | 0.8419 | 0.6718 | 0.6399 | 0.0806 | 0.0589 | 0.8642 | 0.9265 | 0.8419 | 0.6399 | 0.8419 | 0.6464 | 0.8263 |
| 0.0148 | 12.0 | 2688 | 1.5364 | 0.8218 | 0.8105 | 0.8218 | 0.6661 | 0.6340 | 0.0903 | 0.0674 | 0.8505 | 0.9181 | 0.8218 | 0.6340 | 0.8218 | 0.6469 | 0.8137 |
| 0.0148 | 13.0 | 2912 | 1.5380 | 0.8307 | 0.8118 | 0.8307 | 0.6596 | 0.6304 | 0.0870 | 0.0636 | 0.8512 | 0.9205 | 0.8307 | 0.6304 | 0.8307 | 0.6409 | 0.8185 |
| 0.0031 | 14.0 | 3136 | 1.6139 | 0.8218 | 0.8108 | 0.8218 | 0.6451 | 0.6353 | 0.0860 | 0.0674 | 0.8685 | 0.9226 | 0.8218 | 0.6353 | 0.8218 | 0.6396 | 0.8159 |
| 0.0031 | 15.0 | 3360 | 1.6356 | 0.8263 | 0.8117 | 0.8263 | 0.6626 | 0.6477 | 0.0842 | 0.0655 | 0.8700 | 0.9241 | 0.8263 | 0.6477 | 0.8263 | 0.6529 | 0.8183 |
| 0.0043 | 16.0 | 3584 | 1.6745 | 0.8241 | 0.7994 | 0.8241 | 0.6244 | 0.6229 | 0.0884 | 0.0664 | 0.8543 | 0.9196 | 0.8241 | 0.6229 | 0.8241 | 0.6231 | 0.8108 |
| 0.0043 | 17.0 | 3808 | 1.7867 | 0.8085 | 0.7946 | 0.8085 | 0.6221 | 0.6336 | 0.0906 | 0.0732 | 0.8678 | 0.9191 | 0.8085 | 0.6336 | 0.8085 | 0.6229 | 0.7996 |
| 0.0008 | 18.0 | 4032 | 1.7511 | 0.8151 | 0.7971 | 0.8151 | 0.6110 | 0.6216 | 0.0901 | 0.0703 | 0.8644 | 0.9199 | 0.8151 | 0.6216 | 0.8151 | 0.6145 | 0.8046 |
| 0.0008 | 19.0 | 4256 | 1.5909 | 0.8441 | 0.8079 | 0.8441 | 0.6260 | 0.6374 | 0.0792 | 0.0580 | 0.8670 | 0.9278 | 0.8441 | 0.6374 | 0.8441 | 0.6311 | 0.8249 |
| 0.0008 | 20.0 | 4480 | 1.5721 | 0.8463 | 0.8212 | 0.8463 | 0.6727 | 0.6546 | 0.0761 | 0.0571 | 0.8753 | 0.9304 | 0.8463 | 0.6546 | 0.8463 | 0.6547 | 0.8316 |
| 0.0039 | 21.0 | 4704 | 1.5819 | 0.8396 | 0.8054 | 0.8396 | 0.6337 | 0.6200 | 0.0843 | 0.0599 | 0.8527 | 0.9231 | 0.8396 | 0.6200 | 0.8396 | 0.6245 | 0.8199 |
| 0.0039 | 22.0 | 4928 | 1.5906 | 0.8486 | 0.8236 | 0.8486 | 0.6814 | 0.6512 | 0.0770 | 0.0562 | 0.8680 | 0.9291 | 0.8486 | 0.6512 | 0.8486 | 0.6570 | 0.8333 |
| 0.0005 | 23.0 | 5152 | 1.7133 | 0.8263 | 0.8047 | 0.8263 | 0.6403 | 0.6431 | 0.0831 | 0.0655 | 0.8745 | 0.9252 | 0.8263 | 0.6431 | 0.8263 | 0.6367 | 0.8143 |
| 0.0005 | 24.0 | 5376 | 1.7813 | 0.8241 | 0.8022 | 0.8241 | 0.6515 | 0.6290 | 0.0894 | 0.0664 | 0.8490 | 0.9183 | 0.8241 | 0.6290 | 0.8241 | 0.6348 | 0.8108 |
| 0.0033 | 25.0 | 5600 | 1.7983 | 0.8218 | 0.8001 | 0.8218 | 0.6485 | 0.6281 | 0.0902 | 0.0674 | 0.8486 | 0.9176 | 0.8218 | 0.6281 | 0.8218 | 0.6328 | 0.8088 |
| 0.0033 | 26.0 | 5824 | 1.8070 | 0.8218 | 0.8001 | 0.8218 | 0.6485 | 0.6281 | 0.0902 | 0.0674 | 0.8486 | 0.9176 | 0.8218 | 0.6281 | 0.8218 | 0.6328 | 0.8088 |
| 0.0 | 27.0 | 6048 | 1.8198 | 0.8218 | 0.8024 | 0.8218 | 0.6439 | 0.6295 | 0.0890 | 0.0674 | 0.8544 | 0.9191 | 0.8218 | 0.6295 | 0.8218 | 0.6335 | 0.8106 |
| 0.0 | 28.0 | 6272 | 1.8243 | 0.8218 | 0.8024 | 0.8218 | 0.6439 | 0.6295 | 0.0890 | 0.0674 | 0.8544 | 0.9191 | 0.8218 | 0.6295 | 0.8218 | 0.6335 | 0.8106 |
| 0.0 | 29.0 | 6496 | 1.8277 | 0.8218 | 0.8024 | 0.8218 | 0.6439 | 0.6295 | 0.0890 | 0.0674 | 0.8544 | 0.9191 | 0.8218 | 0.6295 | 0.8218 | 0.6335 | 0.8106 |
| 0.0003 | 30.0 | 6720 | 1.8292 | 0.8218 | 0.8024 | 0.8218 | 0.6439 | 0.6295 | 0.0890 | 0.0674 | 0.8544 | 0.9191 | 0.8218 | 0.6295 | 0.8218 | 0.6335 | 0.8106 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
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