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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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model-index: |
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- name: bert-dp-4 |
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results: [] |
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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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# bert-dp-4 |
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This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4611 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: cosine |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 180 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 6.3492 | 1.89 | 1000 | 5.9327 | |
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| 5.8333 | 3.78 | 2000 | 5.8515 | |
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| 5.7604 | 5.67 | 3000 | 5.8483 | |
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| 5.7137 | 7.56 | 4000 | 5.7914 | |
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| 5.6597 | 9.45 | 5000 | 5.7672 | |
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| 5.6213 | 11.34 | 6000 | 5.7594 | |
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| 5.5798 | 13.23 | 7000 | 5.7352 | |
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| 5.5482 | 15.12 | 8000 | 5.7275 | |
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| 5.513 | 17.01 | 9000 | 5.7203 | |
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| 5.485 | 18.9 | 10000 | 5.7211 | |
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| 5.4498 | 20.79 | 11000 | 5.6947 | |
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| 5.4175 | 22.68 | 12000 | 5.6923 | |
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| 5.3877 | 24.57 | 13000 | 5.6879 | |
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| 5.3635 | 26.47 | 14000 | 5.6776 | |
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| 5.3389 | 28.36 | 15000 | 5.6757 | |
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| 5.3166 | 30.25 | 16000 | 5.6758 | |
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| 5.2951 | 32.14 | 17000 | 5.6676 | |
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| 5.2793 | 34.03 | 18000 | 5.6711 | |
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| 5.2684 | 35.92 | 19000 | 5.6687 | |
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| 5.2609 | 37.81 | 20000 | 5.6684 | |
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| 5.2606 | 39.7 | 21000 | 5.6719 | |
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| 5.2624 | 41.59 | 22000 | 5.6697 | |
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| 5.2551 | 43.48 | 23000 | 5.6718 | |
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| 5.2461 | 45.37 | 24000 | 5.6699 | |
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| 5.2431 | 47.26 | 25000 | 5.6692 | |
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| 5.2414 | 49.15 | 26000 | 5.6691 | |
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| 5.2856 | 51.04 | 27000 | 5.6823 | |
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| 5.2753 | 52.93 | 28000 | 5.6860 | |
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| 5.2549 | 54.82 | 29000 | 5.6877 | |
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| 5.2276 | 56.71 | 30000 | 5.6285 | |
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| 5.1674 | 58.6 | 31000 | 5.5439 | |
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| 5.0894 | 60.49 | 32000 | 5.4082 | |
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| 4.9508 | 62.38 | 33000 | 5.1598 | |
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| 4.7453 | 64.27 | 34000 | 4.9274 | |
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| 4.5898 | 66.16 | 35000 | 4.7884 | |
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| 4.4656 | 68.05 | 36000 | 4.6531 | |
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| 4.35 | 69.94 | 37000 | 4.5123 | |
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| 4.2378 | 71.83 | 38000 | 4.4012 | |
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| 4.1496 | 73.72 | 39000 | 4.3240 | |
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| 4.0891 | 75.61 | 40000 | 4.2763 | |
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| 4.0538 | 77.5 | 41000 | 4.2520 | |
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| 4.0448 | 79.4 | 42000 | 4.2485 | |
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| 3.9724 | 81.29 | 43000 | 3.9940 | |
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| 3.6527 | 83.18 | 44000 | 3.7442 | |
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| 3.4172 | 85.07 | 45000 | 3.5713 | |
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| 3.2446 | 86.96 | 46000 | 3.4403 | |
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| 3.4764 | 88.85 | 47000 | 3.3796 | |
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| 3.0543 | 90.74 | 48000 | 3.2884 | |
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| 2.9549 | 92.63 | 49000 | 3.2107 | |
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| 2.8785 | 94.52 | 50000 | 3.1466 | |
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| 2.8143 | 96.41 | 51000 | 3.0788 | |
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| 2.7605 | 98.3 | 52000 | 3.0230 | |
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| 2.7111 | 100.19 | 53000 | 2.9802 | |
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| 2.6727 | 102.08 | 54000 | 2.9414 | |
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| 2.6417 | 103.97 | 55000 | 2.9167 | |
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| 2.612 | 105.86 | 56000 | 2.8927 | |
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| 2.5918 | 107.75 | 57000 | 2.8769 | |
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| 2.5769 | 109.64 | 58000 | 2.8637 | |
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| 2.566 | 111.53 | 59000 | 2.8551 | |
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| 2.556 | 113.42 | 60000 | 2.8458 | |
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| 2.548 | 115.31 | 61000 | 2.8488 | |
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| 2.5468 | 117.2 | 62000 | 2.8412 | |
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| 2.5453 | 119.09 | 63000 | 2.8383 | |
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| 2.7567 | 120.98 | 64000 | 2.8857 | |
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| 2.6017 | 122.87 | 65000 | 2.8382 | |
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| 2.5416 | 124.76 | 66000 | 2.7862 | |
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| 2.484 | 126.65 | 67000 | 2.7415 | |
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| 2.4361 | 128.54 | 68000 | 2.7079 | |
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| 2.3925 | 130.43 | 69000 | 2.6771 | |
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| 2.3512 | 132.33 | 70000 | 2.6542 | |
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| 2.3146 | 134.22 | 71000 | 2.6327 | |
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| 2.2805 | 136.11 | 72000 | 2.6119 | |
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| 2.2494 | 138.0 | 73000 | 2.5903 | |
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| 2.2218 | 139.89 | 74000 | 2.5734 | |
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| 2.1955 | 141.78 | 75000 | 2.5584 | |
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| 2.1739 | 143.67 | 76000 | 2.5459 | |
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| 2.154 | 145.56 | 77000 | 2.5337 | |
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| 2.1324 | 147.45 | 78000 | 2.5260 | |
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| 2.1149 | 149.34 | 79000 | 2.5169 | |
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| 2.096 | 151.23 | 80000 | 2.5095 | |
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| 2.083 | 153.12 | 81000 | 2.5045 | |
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| 2.0666 | 155.01 | 82000 | 2.4911 | |
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| 2.0562 | 156.9 | 83000 | 2.4907 | |
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| 2.0437 | 158.79 | 84000 | 2.4808 | |
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| 2.0356 | 160.68 | 85000 | 2.4816 | |
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| 2.0317 | 162.57 | 86000 | 2.4758 | |
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| 2.0201 | 164.46 | 87000 | 2.4724 | |
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| 2.0138 | 166.35 | 88000 | 2.4723 | |
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| 2.0095 | 168.24 | 89000 | 2.4651 | |
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| 2.0056 | 170.13 | 90000 | 2.4651 | |
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| 2.0021 | 172.02 | 91000 | 2.4616 | |
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| 1.9974 | 173.91 | 92000 | 2.4611 | |
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| 1.9985 | 175.8 | 93000 | 2.4613 | |
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| 1.9954 | 177.69 | 94000 | 2.4579 | |
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| 1.9979 | 179.58 | 95000 | 2.4611 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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