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TSE_BERT_5E

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
text-embeddings-inference
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+ ---
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+ license: apache-2.0
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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_BERT_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_BERT_5E
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+
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3664
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+ - Accuracy: 0.9267
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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: 1e-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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+ | 0.6836 | 0.06 | 50 | 0.5614 | 0.8267 |
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+ | 0.4679 | 0.12 | 100 | 0.3521 | 0.9 |
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+ | 0.3325 | 0.17 | 150 | 0.2747 | 0.8933 |
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+ | 0.2493 | 0.23 | 200 | 0.2712 | 0.9067 |
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+ | 0.273 | 0.29 | 250 | 0.2304 | 0.9333 |
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+ | 0.2888 | 0.35 | 300 | 0.2253 | 0.92 |
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+ | 0.2558 | 0.4 | 350 | 0.2110 | 0.9267 |
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+ | 0.1997 | 0.46 | 400 | 0.2206 | 0.9267 |
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+ | 0.2748 | 0.52 | 450 | 0.2358 | 0.9267 |
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+ | 0.2448 | 0.58 | 500 | 0.2942 | 0.8933 |
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+ | 0.2247 | 0.63 | 550 | 0.2410 | 0.9067 |
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+ | 0.2002 | 0.69 | 600 | 0.2222 | 0.9133 |
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+ | 0.2668 | 0.75 | 650 | 0.2372 | 0.9133 |
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+ | 0.2701 | 0.81 | 700 | 0.2288 | 0.9333 |
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+ | 0.2034 | 0.87 | 750 | 0.2415 | 0.9267 |
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+ | 0.2374 | 0.92 | 800 | 0.2278 | 0.92 |
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+ | 0.2305 | 0.98 | 850 | 0.2270 | 0.92 |
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+ | 0.1704 | 1.04 | 900 | 0.2591 | 0.9333 |
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+ | 0.1826 | 1.1 | 950 | 0.2481 | 0.9267 |
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+ | 0.1116 | 1.15 | 1000 | 0.2906 | 0.9133 |
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+ | 0.1527 | 1.21 | 1050 | 0.2902 | 0.92 |
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+ | 0.1692 | 1.27 | 1100 | 0.2489 | 0.9333 |
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+ | 0.158 | 1.33 | 1150 | 0.2576 | 0.9333 |
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+ | 0.1608 | 1.38 | 1200 | 0.3344 | 0.9267 |
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+ | 0.1194 | 1.44 | 1250 | 0.3615 | 0.9267 |
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+ | 0.201 | 1.5 | 1300 | 0.3374 | 0.92 |
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+ | 0.1938 | 1.56 | 1350 | 0.2847 | 0.92 |
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+ | 0.1479 | 1.61 | 1400 | 0.3044 | 0.9267 |
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+ | 0.1628 | 1.67 | 1450 | 0.2980 | 0.9267 |
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+ | 0.1783 | 1.73 | 1500 | 0.3132 | 0.9267 |
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+ | 0.1885 | 1.79 | 1550 | 0.2676 | 0.9333 |
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+ | 0.1651 | 1.85 | 1600 | 0.2709 | 0.9333 |
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+ | 0.1376 | 1.9 | 1650 | 0.2777 | 0.94 |
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+ | 0.1571 | 1.96 | 1700 | 0.2761 | 0.9333 |
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+ | 0.1561 | 2.02 | 1750 | 0.2912 | 0.94 |
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+ | 0.1187 | 2.08 | 1800 | 0.2893 | 0.9467 |
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+ | 0.1205 | 2.13 | 1850 | 0.2882 | 0.9467 |
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+ | 0.0751 | 2.19 | 1900 | 0.3032 | 0.9467 |
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+ | 0.1412 | 2.25 | 1950 | 0.2926 | 0.9467 |
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+ | 0.0783 | 2.31 | 2000 | 0.2962 | 0.9467 |
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+ | 0.1094 | 2.36 | 2050 | 0.2909 | 0.9333 |
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+ | 0.1158 | 2.42 | 2100 | 0.3087 | 0.9333 |
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+ | 0.0606 | 2.48 | 2150 | 0.3102 | 0.9467 |
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+ | 0.1164 | 2.54 | 2200 | 0.2812 | 0.94 |
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+ | 0.1311 | 2.6 | 2250 | 0.3736 | 0.9267 |
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+ | 0.1087 | 2.65 | 2300 | 0.3069 | 0.94 |
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+ | 0.109 | 2.71 | 2350 | 0.3176 | 0.94 |
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+ | 0.0789 | 2.77 | 2400 | 0.3130 | 0.94 |
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+ | 0.0784 | 2.83 | 2450 | 0.3338 | 0.94 |
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+ | 0.1388 | 2.88 | 2500 | 0.3440 | 0.9333 |
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+ | 0.1062 | 2.94 | 2550 | 0.2883 | 0.94 |
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+ | 0.1016 | 3.0 | 2600 | 0.2776 | 0.94 |
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+ | 0.0642 | 3.06 | 2650 | 0.3302 | 0.9333 |
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+ | 0.052 | 3.11 | 2700 | 0.3217 | 0.94 |
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+ | 0.0539 | 3.17 | 2750 | 0.3899 | 0.9267 |
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+ | 0.0593 | 3.23 | 2800 | 0.3283 | 0.9467 |
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+ | 0.0468 | 3.29 | 2850 | 0.3382 | 0.9467 |
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+ | 0.0546 | 3.34 | 2900 | 0.3133 | 0.9467 |
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+ | 0.107 | 3.4 | 2950 | 0.3550 | 0.94 |
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+ | 0.1079 | 3.46 | 3000 | 0.3484 | 0.94 |
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+ | 0.0782 | 3.52 | 3050 | 0.3313 | 0.94 |
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+ | 0.0635 | 3.58 | 3100 | 0.3418 | 0.94 |
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+ | 0.0771 | 3.63 | 3150 | 0.3685 | 0.9333 |
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+ | 0.0629 | 3.69 | 3200 | 0.3467 | 0.9333 |
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+ | 0.0552 | 3.75 | 3250 | 0.3677 | 0.94 |
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+ | 0.0531 | 3.81 | 3300 | 0.3436 | 0.9333 |
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+ | 0.0819 | 3.86 | 3350 | 0.3802 | 0.9333 |
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+ | 0.0583 | 3.92 | 3400 | 0.3441 | 0.9333 |
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+ | 0.0434 | 3.98 | 3450 | 0.3666 | 0.9333 |
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+ | 0.0747 | 4.04 | 3500 | 0.3554 | 0.9333 |
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+ | 0.0309 | 4.09 | 3550 | 0.3582 | 0.9333 |
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+ | 0.1057 | 4.15 | 3600 | 0.3615 | 0.9267 |
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+ | 0.0391 | 4.21 | 3650 | 0.3583 | 0.9267 |
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+ | 0.0433 | 4.27 | 3700 | 0.3514 | 0.9333 |
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+ | 0.0597 | 4.33 | 3750 | 0.3580 | 0.9333 |
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+ | 0.0663 | 4.38 | 3800 | 0.3390 | 0.94 |
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+ | 0.0563 | 4.44 | 3850 | 0.3518 | 0.9267 |
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+ | 0.0702 | 4.5 | 3900 | 0.3542 | 0.9267 |
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+ | 0.0383 | 4.56 | 3950 | 0.3528 | 0.9267 |
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+ | 0.0474 | 4.61 | 4000 | 0.3485 | 0.9333 |
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+ | 0.0265 | 4.67 | 4050 | 0.3489 | 0.94 |
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+ | 0.0165 | 4.73 | 4100 | 0.3616 | 0.9333 |
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+ | 0.0489 | 4.79 | 4150 | 0.3579 | 0.9333 |
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+ | 0.0478 | 4.84 | 4200 | 0.3603 | 0.9333 |
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+ | 0.0536 | 4.9 | 4250 | 0.3666 | 0.9267 |
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+ | 0.0551 | 4.96 | 4300 | 0.3664 | 0.9267 |
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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