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--- |
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base_model: Patcas/plbart-works |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: v3-my_awesome |
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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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# v3-my_awesome |
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This model is a fine-tuned version of [Patcas/plbart-works](https://huggingface.co/Patcas/plbart-works) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4256 |
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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: 2e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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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: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| No log | 1.0 | 165 | 1.0474 | |
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| No log | 2.0 | 330 | 1.0274 | |
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| No log | 3.0 | 495 | 1.0536 | |
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| 0.2458 | 4.0 | 660 | 1.0316 | |
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| 0.2458 | 5.0 | 825 | 1.0409 | |
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| 0.2458 | 6.0 | 990 | 1.0534 | |
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| 0.1408 | 7.0 | 1155 | 1.0838 | |
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| 0.1408 | 8.0 | 1320 | 1.0757 | |
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| 0.1408 | 9.0 | 1485 | 1.1114 | |
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| 0.0813 | 10.0 | 1650 | 1.1037 | |
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| 0.0813 | 11.0 | 1815 | 1.0990 | |
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| 0.0813 | 12.0 | 1980 | 1.1385 | |
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| 0.0514 | 13.0 | 2145 | 1.1595 | |
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| 0.0514 | 14.0 | 2310 | 1.1591 | |
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| 0.0514 | 15.0 | 2475 | 1.1526 | |
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| 0.0358 | 16.0 | 2640 | 1.1712 | |
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| 0.0358 | 17.0 | 2805 | 1.1831 | |
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| 0.0358 | 18.0 | 2970 | 1.1991 | |
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| 0.027 | 19.0 | 3135 | 1.1804 | |
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| 0.027 | 20.0 | 3300 | 1.1840 | |
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| 0.027 | 21.0 | 3465 | 1.2039 | |
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| 0.0231 | 22.0 | 3630 | 1.2017 | |
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| 0.0231 | 23.0 | 3795 | 1.2293 | |
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| 0.0231 | 24.0 | 3960 | 1.2377 | |
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| 0.0182 | 25.0 | 4125 | 1.2383 | |
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| 0.0182 | 26.0 | 4290 | 1.2409 | |
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| 0.0182 | 27.0 | 4455 | 1.2399 | |
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| 0.0138 | 28.0 | 4620 | 1.2400 | |
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| 0.0138 | 29.0 | 4785 | 1.2569 | |
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| 0.0138 | 30.0 | 4950 | 1.2861 | |
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| 0.0102 | 31.0 | 5115 | 1.2626 | |
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| 0.0102 | 32.0 | 5280 | 1.2841 | |
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| 0.0102 | 33.0 | 5445 | 1.2767 | |
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| 0.0088 | 34.0 | 5610 | 1.2558 | |
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| 0.0088 | 35.0 | 5775 | 1.2666 | |
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| 0.0088 | 36.0 | 5940 | 1.2852 | |
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| 0.0088 | 37.0 | 6105 | 1.2958 | |
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| 0.0088 | 38.0 | 6270 | 1.3174 | |
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| 0.0088 | 39.0 | 6435 | 1.2938 | |
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| 0.0099 | 40.0 | 6600 | 1.3063 | |
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| 0.0099 | 41.0 | 6765 | 1.2998 | |
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| 0.0099 | 42.0 | 6930 | 1.3176 | |
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| 0.0078 | 43.0 | 7095 | 1.3139 | |
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| 0.0078 | 44.0 | 7260 | 1.2946 | |
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| 0.0078 | 45.0 | 7425 | 1.3100 | |
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| 0.0068 | 46.0 | 7590 | 1.3153 | |
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| 0.0068 | 47.0 | 7755 | 1.3185 | |
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| 0.0068 | 48.0 | 7920 | 1.3339 | |
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| 0.0063 | 49.0 | 8085 | 1.3284 | |
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| 0.0063 | 50.0 | 8250 | 1.3353 | |
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| 0.0063 | 51.0 | 8415 | 1.3271 | |
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| 0.0045 | 52.0 | 8580 | 1.3470 | |
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| 0.0045 | 53.0 | 8745 | 1.3348 | |
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| 0.0045 | 54.0 | 8910 | 1.3485 | |
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| 0.0038 | 55.0 | 9075 | 1.3368 | |
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| 0.0038 | 56.0 | 9240 | 1.3429 | |
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| 0.0038 | 57.0 | 9405 | 1.3564 | |
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| 0.0041 | 58.0 | 9570 | 1.3642 | |
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| 0.0041 | 59.0 | 9735 | 1.3657 | |
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| 0.0041 | 60.0 | 9900 | 1.3540 | |
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| 0.0033 | 61.0 | 10065 | 1.3671 | |
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| 0.0033 | 62.0 | 10230 | 1.3632 | |
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| 0.0033 | 63.0 | 10395 | 1.3698 | |
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| 0.0029 | 64.0 | 10560 | 1.3805 | |
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| 0.0029 | 65.0 | 10725 | 1.3878 | |
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| 0.0029 | 66.0 | 10890 | 1.3864 | |
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| 0.0026 | 67.0 | 11055 | 1.3906 | |
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| 0.0026 | 68.0 | 11220 | 1.3981 | |
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| 0.0026 | 69.0 | 11385 | 1.3931 | |
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| 0.0027 | 70.0 | 11550 | 1.3868 | |
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| 0.0027 | 71.0 | 11715 | 1.3873 | |
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| 0.0027 | 72.0 | 11880 | 1.3857 | |
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| 0.0025 | 73.0 | 12045 | 1.3879 | |
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| 0.0025 | 74.0 | 12210 | 1.3871 | |
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| 0.0025 | 75.0 | 12375 | 1.3937 | |
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| 0.002 | 76.0 | 12540 | 1.4003 | |
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| 0.002 | 77.0 | 12705 | 1.4048 | |
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| 0.002 | 78.0 | 12870 | 1.4056 | |
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| 0.0022 | 79.0 | 13035 | 1.4074 | |
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| 0.0022 | 80.0 | 13200 | 1.4064 | |
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| 0.0022 | 81.0 | 13365 | 1.4059 | |
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| 0.0016 | 82.0 | 13530 | 1.4160 | |
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| 0.0016 | 83.0 | 13695 | 1.4078 | |
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| 0.0016 | 84.0 | 13860 | 1.4132 | |
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| 0.0015 | 85.0 | 14025 | 1.4119 | |
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| 0.0015 | 86.0 | 14190 | 1.4147 | |
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| 0.0015 | 87.0 | 14355 | 1.4131 | |
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| 0.0014 | 88.0 | 14520 | 1.4131 | |
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| 0.0014 | 89.0 | 14685 | 1.4118 | |
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| 0.0014 | 90.0 | 14850 | 1.4152 | |
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| 0.0013 | 91.0 | 15015 | 1.4211 | |
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| 0.0013 | 92.0 | 15180 | 1.4213 | |
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| 0.0013 | 93.0 | 15345 | 1.4238 | |
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| 0.0012 | 94.0 | 15510 | 1.4222 | |
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| 0.0012 | 95.0 | 15675 | 1.4246 | |
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| 0.0012 | 96.0 | 15840 | 1.4247 | |
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| 0.0011 | 97.0 | 16005 | 1.4261 | |
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| 0.0011 | 98.0 | 16170 | 1.4259 | |
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| 0.0011 | 99.0 | 16335 | 1.4255 | |
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| 0.0011 | 100.0 | 16500 | 1.4256 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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