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legal-bert-lora-no-grad

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README.md ADDED
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+ ---
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+ license: cc-by-sa-4.0
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+ base_model: nlpaueb/legal-bert-base-uncased
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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 [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.5803
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+ - Accuracy: 0.8234
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+ - Precision: 0.8234
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+ - Recall: 0.8234
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+ - Precision Macro: 0.7941
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+ - Recall Macro: 0.7774
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+ - Macro Fpr: 0.0157
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+ - Weighted Fpr: 0.0151
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+ - Weighted Specificity: 0.9767
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+ - Macro Specificity: 0.9867
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+ - Weighted Sensitivity: 0.8234
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+ - Macro Sensitivity: 0.7774
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+ - F1 Micro: 0.8234
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+ - F1 Macro: 0.7824
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+ - F1 Weighted: 0.8225
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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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+ | 1.5473 | 1.0 | 643 | 0.8485 | 0.7173 | 0.6897 | 0.7173 | 0.3892 | 0.4422 | 0.0278 | 0.0274 | 0.9658 | 0.9789 | 0.7173 | 0.4422 | 0.7173 | 0.3920 | 0.6869 |
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+ | 0.7816 | 2.0 | 1286 | 0.7113 | 0.7545 | 0.7492 | 0.7545 | 0.5282 | 0.5054 | 0.0231 | 0.0227 | 0.9711 | 0.9817 | 0.7545 | 0.5054 | 0.7545 | 0.4751 | 0.7304 |
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+ | 0.6956 | 3.0 | 1929 | 0.6460 | 0.7986 | 0.7793 | 0.7986 | 0.5436 | 0.5701 | 0.0184 | 0.0177 | 0.9741 | 0.9848 | 0.7986 | 0.5701 | 0.7986 | 0.5439 | 0.7833 |
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+ | 0.4942 | 4.0 | 2572 | 0.6430 | 0.8110 | 0.8014 | 0.8110 | 0.6315 | 0.6670 | 0.0169 | 0.0164 | 0.9763 | 0.9858 | 0.8110 | 0.6670 | 0.8110 | 0.6413 | 0.8037 |
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+ | 0.4088 | 5.0 | 3215 | 0.7148 | 0.8319 | 0.8301 | 0.8319 | 0.7951 | 0.7444 | 0.0150 | 0.0142 | 0.9758 | 0.9872 | 0.8319 | 0.7444 | 0.8319 | 0.7459 | 0.8254 |
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+ | 0.3722 | 6.0 | 3858 | 0.7203 | 0.8319 | 0.8282 | 0.8319 | 0.7594 | 0.7604 | 0.0149 | 0.0142 | 0.9779 | 0.9873 | 0.8319 | 0.7604 | 0.8319 | 0.7582 | 0.8285 |
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+ | 0.3088 | 7.0 | 4501 | 0.7796 | 0.8218 | 0.8283 | 0.8218 | 0.7927 | 0.7436 | 0.0158 | 0.0152 | 0.9765 | 0.9866 | 0.8218 | 0.7436 | 0.8218 | 0.7486 | 0.8203 |
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+ | 0.245 | 8.0 | 5144 | 0.8732 | 0.8187 | 0.8171 | 0.8187 | 0.7696 | 0.7403 | 0.0163 | 0.0156 | 0.9752 | 0.9863 | 0.8187 | 0.7403 | 0.8187 | 0.7409 | 0.8135 |
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+ | 0.2331 | 9.0 | 5787 | 0.8710 | 0.8265 | 0.8280 | 0.8265 | 0.7595 | 0.7521 | 0.0152 | 0.0148 | 0.9776 | 0.9869 | 0.8265 | 0.7521 | 0.8265 | 0.7530 | 0.8261 |
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+ | 0.1878 | 10.0 | 6430 | 0.9866 | 0.8257 | 0.8261 | 0.8257 | 0.7672 | 0.7604 | 0.0153 | 0.0149 | 0.9788 | 0.9870 | 0.8257 | 0.7604 | 0.8257 | 0.7588 | 0.8240 |
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+ | 0.1627 | 11.0 | 7073 | 1.0530 | 0.8257 | 0.8269 | 0.8257 | 0.7706 | 0.7711 | 0.0154 | 0.0149 | 0.9787 | 0.9870 | 0.8257 | 0.7711 | 0.8257 | 0.7675 | 0.8234 |
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+ | 0.1301 | 12.0 | 7716 | 1.1042 | 0.8265 | 0.8246 | 0.8265 | 0.7633 | 0.7587 | 0.0155 | 0.0148 | 0.9763 | 0.9869 | 0.8265 | 0.7587 | 0.8265 | 0.7576 | 0.8228 |
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+ | 0.1291 | 13.0 | 8359 | 1.1461 | 0.8234 | 0.8215 | 0.8234 | 0.7582 | 0.7613 | 0.0157 | 0.0151 | 0.9768 | 0.9867 | 0.8234 | 0.7613 | 0.8234 | 0.7581 | 0.8211 |
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+ | 0.11 | 14.0 | 9002 | 1.1837 | 0.8226 | 0.8182 | 0.8226 | 0.7716 | 0.7576 | 0.0159 | 0.0152 | 0.9756 | 0.9865 | 0.8226 | 0.7576 | 0.8226 | 0.7627 | 0.8195 |
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+ | 0.0863 | 15.0 | 9645 | 1.2020 | 0.8218 | 0.8185 | 0.8218 | 0.7616 | 0.7458 | 0.0160 | 0.0152 | 0.9754 | 0.9865 | 0.8218 | 0.7458 | 0.8218 | 0.7498 | 0.8183 |
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+ | 0.0735 | 16.0 | 10288 | 1.2491 | 0.8187 | 0.8160 | 0.8187 | 0.7620 | 0.7464 | 0.0162 | 0.0156 | 0.9755 | 0.9863 | 0.8187 | 0.7464 | 0.8187 | 0.7517 | 0.8168 |
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+ | 0.0802 | 17.0 | 10931 | 1.3288 | 0.8164 | 0.8165 | 0.8164 | 0.7471 | 0.7531 | 0.0164 | 0.0158 | 0.9765 | 0.9862 | 0.8164 | 0.7531 | 0.8164 | 0.7483 | 0.8152 |
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+ | 0.0525 | 18.0 | 11574 | 1.3620 | 0.8133 | 0.8127 | 0.8133 | 0.7557 | 0.7478 | 0.0168 | 0.0161 | 0.9754 | 0.9859 | 0.8133 | 0.7478 | 0.8133 | 0.7486 | 0.8106 |
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+ | 0.0474 | 19.0 | 12217 | 1.3783 | 0.8187 | 0.8220 | 0.8187 | 0.8154 | 0.7972 | 0.0162 | 0.0156 | 0.9755 | 0.9863 | 0.8187 | 0.7972 | 0.8187 | 0.8024 | 0.8187 |
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+ | 0.0315 | 20.0 | 12860 | 1.4004 | 0.8226 | 0.8266 | 0.8226 | 0.8011 | 0.7910 | 0.0157 | 0.0152 | 0.9778 | 0.9867 | 0.8226 | 0.7910 | 0.8226 | 0.7907 | 0.8231 |
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+ | 0.0325 | 21.0 | 13503 | 1.4683 | 0.8187 | 0.8198 | 0.8187 | 0.8030 | 0.7876 | 0.0161 | 0.0156 | 0.9765 | 0.9863 | 0.8187 | 0.7876 | 0.8187 | 0.7921 | 0.8182 |
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+ | 0.0192 | 22.0 | 14146 | 1.4677 | 0.8249 | 0.8224 | 0.8249 | 0.7598 | 0.7482 | 0.0155 | 0.0149 | 0.9768 | 0.9868 | 0.8249 | 0.7482 | 0.8249 | 0.7505 | 0.8225 |
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+ | 0.0235 | 23.0 | 14789 | 1.4610 | 0.8211 | 0.8221 | 0.8211 | 0.8090 | 0.7941 | 0.0160 | 0.0153 | 0.9765 | 0.9865 | 0.8211 | 0.7941 | 0.8211 | 0.7982 | 0.8200 |
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+ | 0.0142 | 24.0 | 15432 | 1.4787 | 0.8273 | 0.8270 | 0.8273 | 0.8179 | 0.7977 | 0.0153 | 0.0147 | 0.9774 | 0.9870 | 0.8273 | 0.7977 | 0.8273 | 0.8054 | 0.8260 |
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+ | 0.0172 | 25.0 | 16075 | 1.5374 | 0.8211 | 0.8231 | 0.8211 | 0.7830 | 0.7711 | 0.0159 | 0.0153 | 0.9767 | 0.9865 | 0.8211 | 0.7711 | 0.8211 | 0.7737 | 0.8212 |
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+ | 0.0097 | 26.0 | 16718 | 1.5153 | 0.8242 | 0.8262 | 0.8242 | 0.8105 | 0.7894 | 0.0156 | 0.0150 | 0.9772 | 0.9868 | 0.8242 | 0.7894 | 0.8242 | 0.7954 | 0.8240 |
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+ | 0.0109 | 27.0 | 17361 | 1.5565 | 0.8218 | 0.8237 | 0.8218 | 0.7876 | 0.7848 | 0.0158 | 0.0152 | 0.9770 | 0.9866 | 0.8218 | 0.7848 | 0.8218 | 0.7832 | 0.8214 |
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+ | 0.0076 | 28.0 | 18004 | 1.5574 | 0.8226 | 0.8247 | 0.8226 | 0.7882 | 0.7843 | 0.0157 | 0.0152 | 0.9771 | 0.9867 | 0.8226 | 0.7843 | 0.8226 | 0.7836 | 0.8229 |
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+ | 0.006 | 29.0 | 18647 | 1.5721 | 0.8234 | 0.8235 | 0.8234 | 0.7868 | 0.7771 | 0.0157 | 0.0151 | 0.9769 | 0.9867 | 0.8234 | 0.7771 | 0.8234 | 0.7788 | 0.8224 |
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+ | 0.0035 | 30.0 | 19290 | 1.5803 | 0.8234 | 0.8234 | 0.8234 | 0.7941 | 0.7774 | 0.0157 | 0.0151 | 0.9767 | 0.9867 | 0.8234 | 0.7774 | 0.8234 | 0.7824 | 0.8225 |
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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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