| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| base_model: law-ai/InLegalBERT |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: InLegalBERT |
| results: [] |
| --- |
| ## Metrics |
|
|
| - loss: 1.0342 |
| - accuracy: 0.8359 |
| - precision: 0.8409 |
| - recall: 0.8359 |
| - precision_macro: 0.8136 |
| - recall_macro: 0.8000 |
| - macro_fpr: 0.0142 |
| - weighted_fpr: 0.0138 |
| - weighted_specificity: 0.9792 |
| - macro_specificity: 0.9877 |
| - weighted_sensitivity: 0.8359 |
| - macro_sensitivity: 0.8000 |
| - f1_micro: 0.8359 |
| - f1_macro: 0.8010 |
| - f1_weighted: 0.8352 |
| - runtime: 19.9583 |
| - samples_per_second: 64.4340 |
| - steps_per_second: 8.0670 |
| |
| ## Metrics |
| |
| - loss: 1.0345 |
| - accuracy: 0.8358 |
| - precision: 0.8408 |
| - recall: 0.8358 |
| - precision_macro: 0.8207 |
| - recall_macro: 0.7957 |
| - macro_fpr: 0.0143 |
| - weighted_fpr: 0.0138 |
| - weighted_specificity: 0.9790 |
| - macro_specificity: 0.9877 |
| - weighted_sensitivity: 0.8358 |
| - macro_sensitivity: 0.7957 |
| - f1_micro: 0.8358 |
| - f1_macro: 0.8020 |
| - f1_weighted: 0.8352 |
| - runtime: 22.0569 |
| - samples_per_second: 58.5300 |
| - steps_per_second: 7.3450 |
|
|
|
|
| <!-- 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: 1.0763 |
| - Accuracy: 0.8304 |
| - Precision: 0.8363 |
| - Recall: 0.8304 |
| - 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 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.38.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.1.0 |
| - Tokenizers 0.15.2 |
|
|
