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InLegalBERT-lora-no-grad

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: law-ai/InLegalBERT
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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
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+ - recall
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+ model-index:
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+ - name: legal-bert-lora-no-grad
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # legal-bert-lora-no-grad
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+
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+ This model is a fine-tuned version of [law-ai/InLegalBERT](https://huggingface.co/law-ai/InLegalBERT) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.5927
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+ - Accuracy: 0.8288
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+ - Precision: 0.8291
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+ - Recall: 0.8288
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+ - Precision Macro: 0.7852
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+ - Recall Macro: 0.7756
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+ - Macro Fpr: 0.0151
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+ - Weighted Fpr: 0.0145
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+ - Weighted Specificity: 0.9775
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+ - Macro Specificity: 0.9871
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+ - Weighted Sensitivity: 0.8288
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+ - Macro Sensitivity: 0.7756
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+ - F1 Micro: 0.8288
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+ - F1 Macro: 0.7761
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+ - F1 Weighted: 0.8279
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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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: 30
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+
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+ ### Training results
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+
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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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+ | 1.6412 | 1.0 | 643 | 0.7925 | 0.7514 | 0.7190 | 0.7514 | 0.4123 | 0.4707 | 0.0237 | 0.0231 | 0.9699 | 0.9814 | 0.7514 | 0.4707 | 0.7514 | 0.4277 | 0.7283 |
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+ | 0.7481 | 2.0 | 1286 | 0.6772 | 0.7901 | 0.7726 | 0.7901 | 0.5958 | 0.6252 | 0.0192 | 0.0186 | 0.9741 | 0.9843 | 0.7901 | 0.6252 | 0.7901 | 0.5998 | 0.7769 |
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+ | 0.6465 | 3.0 | 1929 | 0.6500 | 0.8048 | 0.7931 | 0.8048 | 0.6216 | 0.6414 | 0.0176 | 0.0170 | 0.9764 | 0.9854 | 0.8048 | 0.6414 | 0.8048 | 0.6110 | 0.7904 |
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+ | 0.4707 | 4.0 | 2572 | 0.6704 | 0.8095 | 0.8008 | 0.8095 | 0.6322 | 0.6689 | 0.0173 | 0.0165 | 0.9745 | 0.9856 | 0.8095 | 0.6689 | 0.8095 | 0.6425 | 0.8018 |
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+ | 0.4021 | 5.0 | 3215 | 0.7320 | 0.8280 | 0.8269 | 0.8280 | 0.7782 | 0.7573 | 0.0154 | 0.0146 | 0.9765 | 0.9870 | 0.8280 | 0.7573 | 0.8280 | 0.7571 | 0.8219 |
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+ | 0.3627 | 6.0 | 3858 | 0.6892 | 0.8242 | 0.8227 | 0.8242 | 0.7431 | 0.7365 | 0.0156 | 0.0150 | 0.9768 | 0.9867 | 0.8242 | 0.7365 | 0.8242 | 0.7374 | 0.8223 |
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+ | 0.2866 | 7.0 | 4501 | 0.8756 | 0.8180 | 0.8171 | 0.8180 | 0.7748 | 0.7410 | 0.0166 | 0.0156 | 0.9718 | 0.9860 | 0.8180 | 0.7410 | 0.8180 | 0.7444 | 0.8122 |
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+ | 0.2639 | 8.0 | 5144 | 0.8580 | 0.8265 | 0.8259 | 0.8265 | 0.7989 | 0.7428 | 0.0155 | 0.0148 | 0.9756 | 0.9868 | 0.8265 | 0.7428 | 0.8265 | 0.7480 | 0.8217 |
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+ | 0.2295 | 9.0 | 5787 | 0.9366 | 0.8257 | 0.8231 | 0.8257 | 0.7725 | 0.7465 | 0.0155 | 0.0149 | 0.9762 | 0.9868 | 0.8257 | 0.7465 | 0.8257 | 0.7524 | 0.8223 |
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+ | 0.195 | 10.0 | 6430 | 0.9685 | 0.8273 | 0.8236 | 0.8273 | 0.7595 | 0.7515 | 0.0153 | 0.0147 | 0.9767 | 0.9869 | 0.8273 | 0.7515 | 0.8273 | 0.7528 | 0.8241 |
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+ | 0.1617 | 11.0 | 7073 | 1.0406 | 0.8311 | 0.8263 | 0.8311 | 0.7615 | 0.7552 | 0.0149 | 0.0143 | 0.9776 | 0.9872 | 0.8311 | 0.7552 | 0.8311 | 0.7543 | 0.8265 |
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+ | 0.1421 | 12.0 | 7716 | 1.0713 | 0.8319 | 0.8276 | 0.8319 | 0.7626 | 0.7533 | 0.0148 | 0.0142 | 0.9773 | 0.9873 | 0.8319 | 0.7533 | 0.8319 | 0.7546 | 0.8287 |
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+ | 0.1184 | 13.0 | 8359 | 1.1125 | 0.8257 | 0.8209 | 0.8257 | 0.7569 | 0.7504 | 0.0155 | 0.0149 | 0.9765 | 0.9868 | 0.8257 | 0.7504 | 0.8257 | 0.7510 | 0.8219 |
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+ | 0.1017 | 14.0 | 9002 | 1.1926 | 0.8211 | 0.8215 | 0.8211 | 0.7675 | 0.7815 | 0.0159 | 0.0153 | 0.9776 | 0.9866 | 0.8211 | 0.7815 | 0.8211 | 0.7727 | 0.8196 |
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+ | 0.0752 | 15.0 | 9645 | 1.2508 | 0.8164 | 0.8121 | 0.8164 | 0.7479 | 0.7377 | 0.0164 | 0.0158 | 0.9753 | 0.9861 | 0.8164 | 0.7377 | 0.8164 | 0.7402 | 0.8133 |
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+ | 0.0787 | 16.0 | 10288 | 1.3247 | 0.8218 | 0.8199 | 0.8218 | 0.8034 | 0.7585 | 0.0160 | 0.0152 | 0.9752 | 0.9865 | 0.8218 | 0.7585 | 0.8218 | 0.7698 | 0.8188 |
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+ | 0.0668 | 17.0 | 10931 | 1.3497 | 0.8211 | 0.8201 | 0.8211 | 0.7500 | 0.7487 | 0.0158 | 0.0153 | 0.9778 | 0.9866 | 0.8211 | 0.7487 | 0.8211 | 0.7468 | 0.8198 |
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+ | 0.0471 | 18.0 | 11574 | 1.4278 | 0.8164 | 0.8174 | 0.8164 | 0.7672 | 0.7670 | 0.0165 | 0.0158 | 0.9759 | 0.9862 | 0.8164 | 0.7670 | 0.8164 | 0.7644 | 0.8159 |
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+ | 0.0492 | 19.0 | 12217 | 1.4784 | 0.8180 | 0.8178 | 0.8180 | 0.7631 | 0.7431 | 0.0162 | 0.0156 | 0.9763 | 0.9863 | 0.8180 | 0.7431 | 0.8180 | 0.7453 | 0.8156 |
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+ | 0.0368 | 20.0 | 12860 | 1.4747 | 0.8195 | 0.8183 | 0.8195 | 0.7729 | 0.7568 | 0.0161 | 0.0155 | 0.9760 | 0.9864 | 0.8195 | 0.7568 | 0.8195 | 0.7622 | 0.8180 |
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+ | 0.0329 | 21.0 | 13503 | 1.5075 | 0.8280 | 0.8290 | 0.8280 | 0.7825 | 0.7845 | 0.0152 | 0.0146 | 0.9782 | 0.9871 | 0.8280 | 0.7845 | 0.8280 | 0.7798 | 0.8268 |
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+ | 0.0266 | 22.0 | 14146 | 1.4783 | 0.8273 | 0.8262 | 0.8273 | 0.7780 | 0.7612 | 0.0153 | 0.0147 | 0.9779 | 0.9870 | 0.8273 | 0.7612 | 0.8273 | 0.7651 | 0.8247 |
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+ | 0.0302 | 23.0 | 14789 | 1.5281 | 0.8234 | 0.8246 | 0.8234 | 0.7745 | 0.7699 | 0.0158 | 0.0151 | 0.9760 | 0.9866 | 0.8234 | 0.7699 | 0.8234 | 0.7679 | 0.8224 |
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+ | 0.0207 | 24.0 | 15432 | 1.5475 | 0.8265 | 0.8262 | 0.8265 | 0.7809 | 0.7727 | 0.0155 | 0.0148 | 0.9768 | 0.9869 | 0.8265 | 0.7727 | 0.8265 | 0.7721 | 0.8248 |
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+ | 0.0168 | 25.0 | 16075 | 1.5237 | 0.8242 | 0.8237 | 0.8242 | 0.7726 | 0.7619 | 0.0155 | 0.0150 | 0.9775 | 0.9868 | 0.8242 | 0.7619 | 0.8242 | 0.7629 | 0.8231 |
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+ | 0.0167 | 26.0 | 16718 | 1.5815 | 0.8234 | 0.8255 | 0.8234 | 0.7766 | 0.7728 | 0.0156 | 0.0151 | 0.9775 | 0.9867 | 0.8234 | 0.7728 | 0.8234 | 0.7707 | 0.8232 |
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+ | 0.0127 | 27.0 | 17361 | 1.6010 | 0.8218 | 0.8228 | 0.8218 | 0.7790 | 0.7716 | 0.0158 | 0.0152 | 0.9769 | 0.9866 | 0.8218 | 0.7716 | 0.8218 | 0.7709 | 0.8211 |
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+ | 0.0094 | 28.0 | 18004 | 1.5774 | 0.8265 | 0.8269 | 0.8265 | 0.7788 | 0.7739 | 0.0153 | 0.0148 | 0.9778 | 0.9870 | 0.8265 | 0.7739 | 0.8265 | 0.7728 | 0.8258 |
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+ | 0.0063 | 29.0 | 18647 | 1.5894 | 0.8304 | 0.8306 | 0.8304 | 0.7825 | 0.7764 | 0.0150 | 0.0144 | 0.9779 | 0.9872 | 0.8304 | 0.7764 | 0.8304 | 0.7759 | 0.8296 |
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+ | 0.0126 | 30.0 | 19290 | 1.5927 | 0.8288 | 0.8291 | 0.8288 | 0.7852 | 0.7756 | 0.0151 | 0.0145 | 0.9775 | 0.9871 | 0.8288 | 0.7756 | 0.8288 | 0.7761 | 0.8279 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.1
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