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TSE_XLNET_5E

Text Classification
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PyTorch
xlnet
Generated from Trainer
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+ ---
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+ license: mit
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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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+ model-index:
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+ - name: TSE_XLNET_5E
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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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+ # TSE_XLNET_5E
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+
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+ This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/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: 0.4463
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+ - Accuracy: 0.9333
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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: 3e-05
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+ - train_batch_size: 16
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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: 5
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6717 | 0.06 | 50 | 0.4377 | 0.8533 |
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+ | 0.3989 | 0.12 | 100 | 0.4525 | 0.84 |
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+ | 0.3433 | 0.17 | 150 | 0.3348 | 0.9133 |
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+ | 0.2646 | 0.23 | 200 | 0.3722 | 0.9 |
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+ | 0.3052 | 0.29 | 250 | 0.3306 | 0.8933 |
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+ | 0.2583 | 0.35 | 300 | 0.3129 | 0.92 |
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+ | 0.2712 | 0.4 | 350 | 0.3147 | 0.9 |
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+ | 0.2708 | 0.46 | 400 | 0.2680 | 0.9 |
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+ | 0.2443 | 0.52 | 450 | 0.2261 | 0.9133 |
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+ | 0.2463 | 0.58 | 500 | 0.2583 | 0.9067 |
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+ | 0.2525 | 0.63 | 550 | 0.2719 | 0.92 |
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+ | 0.2522 | 0.69 | 600 | 0.3905 | 0.8933 |
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+ | 0.2078 | 0.75 | 650 | 0.2674 | 0.9133 |
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+ | 0.264 | 0.81 | 700 | 0.2774 | 0.9133 |
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+ | 0.211 | 0.87 | 750 | 0.2652 | 0.9333 |
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+ | 0.286 | 0.92 | 800 | 0.1777 | 0.94 |
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+ | 0.2341 | 0.98 | 850 | 0.2570 | 0.9133 |
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+ | 0.1797 | 1.04 | 900 | 0.3162 | 0.92 |
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+ | 0.1831 | 1.1 | 950 | 0.3205 | 0.92 |
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+ | 0.2006 | 1.15 | 1000 | 0.3173 | 0.9133 |
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+ | 0.1555 | 1.21 | 1050 | 0.3388 | 0.9267 |
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+ | 0.1712 | 1.27 | 1100 | 0.3968 | 0.92 |
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+ | 0.1488 | 1.33 | 1150 | 0.4167 | 0.9133 |
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+ | 0.1893 | 1.38 | 1200 | 0.3269 | 0.9267 |
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+ | 0.1543 | 1.44 | 1250 | 0.3797 | 0.9133 |
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+ | 0.1825 | 1.5 | 1300 | 0.2203 | 0.94 |
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+ | 0.1841 | 1.56 | 1350 | 0.2744 | 0.9133 |
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+ | 0.1523 | 1.61 | 1400 | 0.3561 | 0.9067 |
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+ | 0.1914 | 1.67 | 1450 | 0.2859 | 0.9067 |
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+ | 0.1742 | 1.73 | 1500 | 0.2461 | 0.9267 |
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+ | 0.145 | 1.79 | 1550 | 0.4266 | 0.9133 |
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+ | 0.208 | 1.85 | 1600 | 0.3470 | 0.9067 |
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+ | 0.147 | 1.9 | 1650 | 0.4521 | 0.9133 |
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+ | 0.1867 | 1.96 | 1700 | 0.3648 | 0.9067 |
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+ | 0.182 | 2.02 | 1750 | 0.2659 | 0.9333 |
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+ | 0.1079 | 2.08 | 1800 | 0.3393 | 0.92 |
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+ | 0.1338 | 2.13 | 1850 | 0.3483 | 0.9267 |
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+ | 0.1181 | 2.19 | 1900 | 0.4384 | 0.92 |
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+ | 0.1418 | 2.25 | 1950 | 0.3468 | 0.9267 |
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+ | 0.0953 | 2.31 | 2000 | 0.4008 | 0.9267 |
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+ | 0.1313 | 2.36 | 2050 | 0.3301 | 0.9333 |
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+ | 0.0499 | 2.42 | 2100 | 0.4018 | 0.92 |
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+ | 0.1197 | 2.48 | 2150 | 0.3394 | 0.9267 |
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+ | 0.1237 | 2.54 | 2200 | 0.3399 | 0.92 |
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+ | 0.0766 | 2.6 | 2250 | 0.3947 | 0.9267 |
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+ | 0.1142 | 2.65 | 2300 | 0.4055 | 0.9133 |
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+ | 0.1362 | 2.71 | 2350 | 0.2599 | 0.9333 |
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+ | 0.1332 | 2.77 | 2400 | 0.3293 | 0.9133 |
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+ | 0.1241 | 2.83 | 2450 | 0.3717 | 0.92 |
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+ | 0.0696 | 2.88 | 2500 | 0.4440 | 0.92 |
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+ | 0.1012 | 2.94 | 2550 | 0.4026 | 0.92 |
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+ | 0.1028 | 3.0 | 2600 | 0.4202 | 0.9133 |
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+ | 0.0551 | 3.06 | 2650 | 0.4649 | 0.9133 |
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+ | 0.0796 | 3.11 | 2700 | 0.4053 | 0.92 |
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+ | 0.0786 | 3.17 | 2750 | 0.4862 | 0.9067 |
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+ | 0.0843 | 3.23 | 2800 | 0.4007 | 0.9267 |
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+ | 0.0502 | 3.29 | 2850 | 0.4510 | 0.92 |
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+ | 0.0726 | 3.34 | 2900 | 0.4171 | 0.9267 |
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+ | 0.0933 | 3.4 | 2950 | 0.3485 | 0.9333 |
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+ | 0.0624 | 3.46 | 3000 | 0.4442 | 0.9133 |
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+ | 0.0475 | 3.52 | 3050 | 0.4449 | 0.92 |
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+ | 0.0498 | 3.58 | 3100 | 0.4147 | 0.9267 |
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+ | 0.1101 | 3.63 | 3150 | 0.3484 | 0.9333 |
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+ | 0.0785 | 3.69 | 3200 | 0.3630 | 0.9267 |
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+ | 0.075 | 3.75 | 3250 | 0.4267 | 0.92 |
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+ | 0.0709 | 3.81 | 3300 | 0.3638 | 0.9267 |
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+ | 0.0754 | 3.86 | 3350 | 0.3890 | 0.9333 |
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+ | 0.1038 | 3.92 | 3400 | 0.3910 | 0.9267 |
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+ | 0.0274 | 3.98 | 3450 | 0.4246 | 0.9267 |
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+ | 0.0723 | 4.04 | 3500 | 0.3847 | 0.9267 |
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+ | 0.015 | 4.09 | 3550 | 0.4134 | 0.9333 |
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+ | 0.0329 | 4.15 | 3600 | 0.4136 | 0.9333 |
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+ | 0.0619 | 4.21 | 3650 | 0.4048 | 0.9333 |
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+ | 0.0505 | 4.27 | 3700 | 0.4228 | 0.9267 |
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+ | 0.0523 | 4.33 | 3750 | 0.4139 | 0.9267 |
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+ | 0.0365 | 4.38 | 3800 | 0.4067 | 0.9267 |
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+ | 0.0434 | 4.44 | 3850 | 0.4132 | 0.9333 |
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+ | 0.0262 | 4.5 | 3900 | 0.4245 | 0.9333 |
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+ | 0.0534 | 4.56 | 3950 | 0.4217 | 0.9333 |
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+ | 0.0186 | 4.61 | 4000 | 0.4282 | 0.9333 |
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+ | 0.0548 | 4.67 | 4050 | 0.4255 | 0.9333 |
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+ | 0.0146 | 4.73 | 4100 | 0.4368 | 0.9333 |
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+ | 0.0442 | 4.79 | 4150 | 0.4470 | 0.9333 |
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+ | 0.0431 | 4.84 | 4200 | 0.4469 | 0.9333 |
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+ | 0.0297 | 4.9 | 4250 | 0.4470 | 0.9333 |
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+ | 0.0601 | 4.96 | 4300 | 0.4463 | 0.9333 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0
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+ - Pytorch 1.13.0
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+ - Datasets 2.3.2
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+ - Tokenizers 0.13.1