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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: distilbert-base-uncased-finetuned-dob |
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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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# distilbert-base-uncased-finetuned-dob |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0001 |
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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: 128 |
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- eval_batch_size: 128 |
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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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| 0.5564 | 1.0 | 10 | 0.3900 | |
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| 0.3348 | 2.0 | 20 | 0.1773 | |
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| 0.1584 | 3.0 | 30 | 0.0763 | |
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| 0.0758 | 4.0 | 40 | 0.0294 | |
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| 0.0322 | 5.0 | 50 | 0.0055 | |
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| 0.0075 | 6.0 | 60 | 0.0023 | |
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| 0.0035 | 7.0 | 70 | 0.0015 | |
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| 0.0021 | 8.0 | 80 | 0.0011 | |
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| 0.0013 | 9.0 | 90 | 0.0009 | |
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| 0.0011 | 10.0 | 100 | 0.0008 | |
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| 0.0009 | 11.0 | 110 | 0.0008 | |
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| 0.0009 | 12.0 | 120 | 0.0007 | |
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| 0.0008 | 13.0 | 130 | 0.0006 | |
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| 0.0008 | 14.0 | 140 | 0.0006 | |
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| 0.0007 | 15.0 | 150 | 0.0006 | |
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| 0.0007 | 16.0 | 160 | 0.0005 | |
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| 0.0006 | 17.0 | 170 | 0.0005 | |
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| 0.0006 | 18.0 | 180 | 0.0005 | |
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| 0.0005 | 19.0 | 190 | 0.0004 | |
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| 0.0005 | 20.0 | 200 | 0.0004 | |
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| 0.0005 | 21.0 | 210 | 0.0004 | |
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| 0.0005 | 22.0 | 220 | 0.0004 | |
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| 0.0005 | 23.0 | 230 | 0.0004 | |
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| 0.0004 | 24.0 | 240 | 0.0004 | |
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| 0.0004 | 25.0 | 250 | 0.0003 | |
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| 0.0004 | 26.0 | 260 | 0.0003 | |
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| 0.0004 | 27.0 | 270 | 0.0003 | |
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| 0.0004 | 28.0 | 280 | 0.0003 | |
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| 0.0003 | 29.0 | 290 | 0.0003 | |
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| 0.0003 | 30.0 | 300 | 0.0003 | |
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| 0.0003 | 31.0 | 310 | 0.0003 | |
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| 0.0004 | 32.0 | 320 | 0.0003 | |
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| 0.0003 | 33.0 | 330 | 0.0003 | |
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| 0.0003 | 34.0 | 340 | 0.0003 | |
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| 0.0003 | 35.0 | 350 | 0.0003 | |
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| 0.0003 | 36.0 | 360 | 0.0003 | |
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| 0.0003 | 37.0 | 370 | 0.0002 | |
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| 0.0003 | 38.0 | 380 | 0.0002 | |
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| 0.0003 | 39.0 | 390 | 0.0002 | |
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| 0.0003 | 40.0 | 400 | 0.0002 | |
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| 0.0003 | 41.0 | 410 | 0.0002 | |
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| 0.0003 | 42.0 | 420 | 0.0002 | |
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| 0.0002 | 43.0 | 430 | 0.0002 | |
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| 0.0002 | 44.0 | 440 | 0.0002 | |
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| 0.0002 | 45.0 | 450 | 0.0002 | |
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| 0.0002 | 46.0 | 460 | 0.0002 | |
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| 0.0002 | 47.0 | 470 | 0.0002 | |
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| 0.0002 | 48.0 | 480 | 0.0002 | |
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| 0.0002 | 49.0 | 490 | 0.0002 | |
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| 0.0002 | 50.0 | 500 | 0.0002 | |
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| 0.0002 | 51.0 | 510 | 0.0002 | |
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| 0.0002 | 52.0 | 520 | 0.0002 | |
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| 0.0002 | 53.0 | 530 | 0.0002 | |
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| 0.0002 | 54.0 | 540 | 0.0002 | |
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| 0.0002 | 55.0 | 550 | 0.0002 | |
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| 0.0002 | 56.0 | 560 | 0.0002 | |
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| 0.0002 | 57.0 | 570 | 0.0002 | |
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| 0.0002 | 58.0 | 580 | 0.0002 | |
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| 0.0002 | 59.0 | 590 | 0.0002 | |
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| 0.0002 | 60.0 | 600 | 0.0002 | |
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| 0.0002 | 61.0 | 610 | 0.0002 | |
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| 0.0002 | 62.0 | 620 | 0.0002 | |
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| 0.0002 | 63.0 | 630 | 0.0002 | |
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| 0.0002 | 64.0 | 640 | 0.0002 | |
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| 0.0002 | 65.0 | 650 | 0.0002 | |
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| 0.0002 | 66.0 | 660 | 0.0002 | |
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| 0.0002 | 67.0 | 670 | 0.0002 | |
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| 0.0002 | 68.0 | 680 | 0.0002 | |
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| 0.0002 | 69.0 | 690 | 0.0002 | |
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| 0.0002 | 70.0 | 700 | 0.0002 | |
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| 0.0002 | 71.0 | 710 | 0.0002 | |
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| 0.0002 | 72.0 | 720 | 0.0002 | |
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| 0.0002 | 73.0 | 730 | 0.0002 | |
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| 0.0002 | 74.0 | 740 | 0.0002 | |
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| 0.0001 | 75.0 | 750 | 0.0002 | |
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| 0.0002 | 76.0 | 760 | 0.0002 | |
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| 0.0002 | 77.0 | 770 | 0.0002 | |
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| 0.0001 | 78.0 | 780 | 0.0002 | |
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| 0.0002 | 79.0 | 790 | 0.0002 | |
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| 0.0002 | 80.0 | 800 | 0.0002 | |
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| 0.0001 | 81.0 | 810 | 0.0002 | |
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| 0.0002 | 82.0 | 820 | 0.0002 | |
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| 0.0001 | 83.0 | 830 | 0.0001 | |
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| 0.0001 | 84.0 | 840 | 0.0001 | |
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| 0.0001 | 85.0 | 850 | 0.0001 | |
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| 0.0001 | 86.0 | 860 | 0.0001 | |
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| 0.0001 | 87.0 | 870 | 0.0001 | |
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| 0.0001 | 88.0 | 880 | 0.0001 | |
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| 0.0001 | 89.0 | 890 | 0.0001 | |
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| 0.0001 | 90.0 | 900 | 0.0001 | |
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| 0.0001 | 91.0 | 910 | 0.0001 | |
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| 0.0001 | 92.0 | 920 | 0.0001 | |
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| 0.0001 | 93.0 | 930 | 0.0001 | |
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| 0.0001 | 94.0 | 940 | 0.0001 | |
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| 0.0001 | 95.0 | 950 | 0.0001 | |
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| 0.0001 | 96.0 | 960 | 0.0001 | |
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| 0.0001 | 97.0 | 970 | 0.0001 | |
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| 0.0001 | 98.0 | 980 | 0.0001 | |
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| 0.0001 | 99.0 | 990 | 0.0001 | |
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| 0.0001 | 100.0 | 1000 | 0.0001 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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