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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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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: DIALOGUE_one |
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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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# DIALOGUE_one |
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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: 0.1809 |
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- Precision: 0.9762 |
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- Recall: 0.9737 |
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- F1: 0.9736 |
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- Accuracy: 0.9737 |
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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: 8 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 1.1724 | 0.62 | 30 | 0.8000 | 0.9224 | 0.9079 | 0.9071 | 0.9079 | |
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| 0.6097 | 1.25 | 60 | 0.2953 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.2608 | 1.88 | 90 | 0.1342 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0958 | 2.5 | 120 | 0.0711 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0424 | 3.12 | 150 | 0.1116 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0205 | 3.75 | 180 | 0.1195 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0119 | 4.38 | 210 | 0.0987 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0087 | 5.0 | 240 | 0.1092 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0068 | 5.62 | 270 | 0.1103 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0057 | 6.25 | 300 | 0.1116 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0048 | 6.88 | 330 | 0.1350 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0041 | 7.5 | 360 | 0.1359 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0037 | 8.12 | 390 | 0.1339 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0031 | 8.75 | 420 | 0.1655 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0028 | 9.38 | 450 | 0.1608 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0026 | 10.0 | 480 | 0.1545 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0024 | 10.62 | 510 | 0.1554 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0022 | 11.25 | 540 | 0.1602 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.002 | 11.88 | 570 | 0.1619 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0019 | 12.5 | 600 | 0.1632 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0017 | 13.12 | 630 | 0.1643 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0016 | 13.75 | 660 | 0.1647 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0016 | 14.38 | 690 | 0.1667 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0015 | 15.0 | 720 | 0.1683 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0013 | 15.62 | 750 | 0.1688 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0013 | 16.25 | 780 | 0.1702 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0012 | 16.88 | 810 | 0.1708 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0011 | 17.5 | 840 | 0.1715 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0011 | 18.12 | 870 | 0.1742 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.001 | 18.75 | 900 | 0.1754 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.001 | 19.38 | 930 | 0.1755 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.001 | 20.0 | 960 | 0.1759 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0009 | 20.62 | 990 | 0.1765 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0009 | 21.25 | 1020 | 0.1776 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0009 | 21.88 | 1050 | 0.1779 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 22.5 | 1080 | 0.1776 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0009 | 23.12 | 1110 | 0.1782 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 23.75 | 1140 | 0.1789 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 24.38 | 1170 | 0.1792 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 25.0 | 1200 | 0.1796 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 25.62 | 1230 | 0.1799 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 26.25 | 1260 | 0.1803 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0007 | 26.88 | 1290 | 0.1804 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 27.5 | 1320 | 0.1808 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0007 | 28.12 | 1350 | 0.1809 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0007 | 28.75 | 1380 | 0.1810 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 29.38 | 1410 | 0.1809 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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| 0.0008 | 30.0 | 1440 | 0.1809 | 0.9762 | 0.9737 | 0.9736 | 0.9737 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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