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---
license: mit
tags:
- generated_from_trainer
base_model: law-ai/InLegalBERT
metrics:
- accuracy
- precision
- recall
model-index:
- name: InLegalBERT
results: []
---
<!-- 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. -->
# 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: 1.5914
- Accuracy: 0.8296
- Precision: 0.8293
- Recall: 0.8296
- Precision Macro: 0.7959
- Recall Macro: 0.8029
- Macro Fpr: 0.0150
- Weighted Fpr: 0.0145
- Weighted Specificity: 0.9774
- Macro Specificity: 0.9871
- Weighted Sensitivity: 0.8296
- Macro Sensitivity: 0.8029
- F1 Micro: 0.8296
- F1 Macro: 0.7954
- F1 Weighted: 0.8283
## 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: 15
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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