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xlnet-base-cased

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: xlnet/xlnet-base-cased
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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: xlnet-base-cased
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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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+ # xlnet-base-cased
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+
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+ This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co/xlnet/xlnet-base-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.9234
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+ - Accuracy: 0.8218
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+ - Precision: 0.8189
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+ - Recall: 0.8218
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+ - Precision Macro: 0.7836
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+ - Recall Macro: 0.7606
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+ - Macro Fpr: 0.0159
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+ - Weighted Fpr: 0.0152
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+ - Weighted Specificity: 0.9756
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+ - Macro Specificity: 0.9865
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+ - Weighted Sensitivity: 0.8218
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+ - Macro Sensitivity: 0.7606
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+ - F1 Micro: 0.8218
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+ - F1 Macro: 0.7664
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+ - F1 Weighted: 0.8189
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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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+ - mixed_precision_training: Native AMP
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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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+ | 1.2613 | 1.0 | 643 | 0.7758 | 0.7676 | 0.7673 | 0.7676 | 0.5269 | 0.5129 | 0.0220 | 0.0212 | 0.9680 | 0.9824 | 0.7676 | 0.5129 | 0.7676 | 0.4819 | 0.7524 |
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+ | 0.7364 | 2.0 | 1286 | 0.6755 | 0.8071 | 0.8088 | 0.8071 | 0.7425 | 0.6972 | 0.0174 | 0.0168 | 0.9751 | 0.9855 | 0.8071 | 0.6972 | 0.8071 | 0.7019 | 0.8013 |
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+ | 0.6021 | 3.0 | 1929 | 0.8443 | 0.8064 | 0.8016 | 0.8064 | 0.7270 | 0.7262 | 0.0176 | 0.0169 | 0.9718 | 0.9852 | 0.8064 | 0.7262 | 0.8064 | 0.7229 | 0.8014 |
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+ | 0.4361 | 4.0 | 2572 | 0.8850 | 0.8002 | 0.8001 | 0.8002 | 0.7167 | 0.7048 | 0.0180 | 0.0175 | 0.9731 | 0.9849 | 0.8002 | 0.7048 | 0.8002 | 0.7051 | 0.7971 |
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+ | 0.3359 | 5.0 | 3215 | 1.1264 | 0.8017 | 0.7981 | 0.8017 | 0.6531 | 0.6681 | 0.0181 | 0.0174 | 0.9732 | 0.9850 | 0.8017 | 0.6681 | 0.8017 | 0.6459 | 0.7962 |
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+ | 0.2827 | 6.0 | 3858 | 1.1471 | 0.7994 | 0.8092 | 0.7994 | 0.7389 | 0.6922 | 0.0183 | 0.0176 | 0.9686 | 0.9845 | 0.7994 | 0.6922 | 0.7994 | 0.7042 | 0.7952 |
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+ | 0.1945 | 7.0 | 4501 | 1.1841 | 0.8149 | 0.8129 | 0.8149 | 0.7850 | 0.7598 | 0.0166 | 0.0160 | 0.9746 | 0.9860 | 0.8149 | 0.7598 | 0.8149 | 0.7667 | 0.8122 |
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+ | 0.1286 | 8.0 | 5144 | 1.3231 | 0.8079 | 0.8105 | 0.8079 | 0.7630 | 0.7216 | 0.0171 | 0.0167 | 0.9757 | 0.9856 | 0.8079 | 0.7216 | 0.8079 | 0.7283 | 0.8067 |
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+ | 0.1304 | 9.0 | 5787 | 1.3869 | 0.8102 | 0.8118 | 0.8102 | 0.7705 | 0.7603 | 0.0171 | 0.0165 | 0.9741 | 0.9856 | 0.8102 | 0.7603 | 0.8102 | 0.7570 | 0.8088 |
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+ | 0.0875 | 10.0 | 6430 | 1.6901 | 0.7823 | 0.7932 | 0.7823 | 0.7601 | 0.7020 | 0.0199 | 0.0195 | 0.9680 | 0.9834 | 0.7823 | 0.7020 | 0.7823 | 0.7192 | 0.7817 |
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+ | 0.1075 | 11.0 | 7073 | 1.6517 | 0.7978 | 0.8021 | 0.7978 | 0.7513 | 0.7567 | 0.0183 | 0.0178 | 0.9758 | 0.9849 | 0.7978 | 0.7567 | 0.7978 | 0.7470 | 0.7935 |
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+ | 0.0632 | 12.0 | 7716 | 1.5290 | 0.8149 | 0.8184 | 0.8149 | 0.7746 | 0.7772 | 0.0167 | 0.0160 | 0.9738 | 0.9859 | 0.8149 | 0.7772 | 0.8149 | 0.7707 | 0.8150 |
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+ | 0.0565 | 13.0 | 8359 | 1.5766 | 0.8064 | 0.8107 | 0.8064 | 0.7528 | 0.7628 | 0.0174 | 0.0169 | 0.9769 | 0.9856 | 0.8064 | 0.7628 | 0.8064 | 0.7537 | 0.8061 |
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+ | 0.0504 | 14.0 | 9002 | 1.7548 | 0.8048 | 0.8100 | 0.8048 | 0.7569 | 0.7702 | 0.0174 | 0.0170 | 0.9765 | 0.9854 | 0.8048 | 0.7702 | 0.8048 | 0.7553 | 0.8046 |
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+ | 0.0295 | 15.0 | 9645 | 1.7570 | 0.8102 | 0.8226 | 0.8102 | 0.7705 | 0.7611 | 0.0168 | 0.0165 | 0.9770 | 0.9858 | 0.8102 | 0.7611 | 0.8102 | 0.7610 | 0.8141 |
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+ | 0.0338 | 16.0 | 10288 | 1.7394 | 0.8110 | 0.8138 | 0.8110 | 0.7639 | 0.7659 | 0.0168 | 0.0164 | 0.9775 | 0.9859 | 0.8110 | 0.7659 | 0.8110 | 0.7613 | 0.8100 |
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+ | 0.0444 | 17.0 | 10931 | 1.7975 | 0.8118 | 0.8201 | 0.8118 | 0.7511 | 0.7610 | 0.0168 | 0.0163 | 0.9775 | 0.9859 | 0.8118 | 0.7610 | 0.8118 | 0.7457 | 0.8129 |
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+ | 0.0397 | 18.0 | 11574 | 1.6921 | 0.8149 | 0.8203 | 0.8149 | 0.7540 | 0.7854 | 0.0165 | 0.0160 | 0.9780 | 0.9862 | 0.8149 | 0.7854 | 0.8149 | 0.7553 | 0.8130 |
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+ | 0.0356 | 19.0 | 12217 | 1.6908 | 0.8273 | 0.8307 | 0.8273 | 0.7764 | 0.7992 | 0.0152 | 0.0147 | 0.9784 | 0.9870 | 0.8273 | 0.7992 | 0.8273 | 0.7814 | 0.8265 |
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+ | 0.0306 | 20.0 | 12860 | 1.8374 | 0.8180 | 0.8208 | 0.8180 | 0.7635 | 0.7756 | 0.0162 | 0.0156 | 0.9771 | 0.9863 | 0.8180 | 0.7756 | 0.8180 | 0.7620 | 0.8166 |
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+ | 0.0234 | 21.0 | 13503 | 1.7738 | 0.8195 | 0.8185 | 0.8195 | 0.7947 | 0.7602 | 0.0160 | 0.0155 | 0.9760 | 0.9864 | 0.8195 | 0.7602 | 0.8195 | 0.7713 | 0.8174 |
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+ | 0.0091 | 22.0 | 14146 | 1.8537 | 0.8172 | 0.8167 | 0.8172 | 0.7732 | 0.7646 | 0.0163 | 0.0157 | 0.9764 | 0.9862 | 0.8172 | 0.7646 | 0.8172 | 0.7654 | 0.8143 |
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+ | 0.0138 | 23.0 | 14789 | 1.8306 | 0.8102 | 0.8173 | 0.8102 | 0.7729 | 0.7569 | 0.0167 | 0.0165 | 0.9757 | 0.9857 | 0.8102 | 0.7569 | 0.8102 | 0.7625 | 0.8125 |
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+ | 0.0213 | 24.0 | 15432 | 1.9363 | 0.8125 | 0.8149 | 0.8125 | 0.7777 | 0.7540 | 0.0168 | 0.0162 | 0.9739 | 0.9858 | 0.8125 | 0.7540 | 0.8125 | 0.7622 | 0.8115 |
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+ | 0.0034 | 25.0 | 16075 | 1.9552 | 0.8156 | 0.8179 | 0.8156 | 0.7843 | 0.7583 | 0.0165 | 0.0159 | 0.9740 | 0.9860 | 0.8156 | 0.7583 | 0.8156 | 0.7657 | 0.8147 |
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+ | 0.0028 | 26.0 | 16718 | 1.9404 | 0.8172 | 0.8163 | 0.8172 | 0.7884 | 0.7591 | 0.0164 | 0.0157 | 0.9747 | 0.9861 | 0.8172 | 0.7591 | 0.8172 | 0.7656 | 0.8137 |
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+ | 0.0105 | 27.0 | 17361 | 1.9156 | 0.8180 | 0.8132 | 0.8180 | 0.7848 | 0.7575 | 0.0164 | 0.0156 | 0.9742 | 0.9861 | 0.8180 | 0.7575 | 0.8180 | 0.7667 | 0.8140 |
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+ | 0.0048 | 28.0 | 18004 | 1.9104 | 0.8203 | 0.8196 | 0.8203 | 0.7877 | 0.7615 | 0.0160 | 0.0154 | 0.9758 | 0.9864 | 0.8203 | 0.7615 | 0.8203 | 0.7658 | 0.8175 |
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+ | 0.0011 | 29.0 | 18647 | 1.9312 | 0.8203 | 0.8185 | 0.8203 | 0.7888 | 0.7600 | 0.0161 | 0.0154 | 0.9755 | 0.9864 | 0.8203 | 0.7600 | 0.8203 | 0.7664 | 0.8173 |
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+ | 0.0004 | 30.0 | 19290 | 1.9234 | 0.8218 | 0.8189 | 0.8218 | 0.7836 | 0.7606 | 0.0159 | 0.0152 | 0.9756 | 0.9865 | 0.8218 | 0.7606 | 0.8218 | 0.7664 | 0.8189 |
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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 2.19.0
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+ - Tokenizers 0.15.1
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