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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: distilbert-base-uncased-finetuned-yahd |
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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-yahd |
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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: 5.7685 |
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- Accuracy: 0.4010 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 16 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 2.2439 | 1.0 | 9142 | 2.1898 | 0.2130 | |
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| 1.9235 | 2.0 | 18284 | 2.1045 | 0.2372 | |
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| 1.5915 | 3.0 | 27426 | 2.1380 | 0.2550 | |
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| 1.3262 | 4.0 | 36568 | 2.2544 | 0.2758 | |
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| 1.0529 | 5.0 | 45710 | 2.5662 | 0.2955 | |
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| 0.8495 | 6.0 | 54852 | 2.8731 | 0.3078 | |
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| 0.6779 | 7.0 | 63994 | 3.1980 | 0.3218 | |
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| 0.5546 | 8.0 | 73136 | 3.6289 | 0.3380 | |
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| 0.4738 | 9.0 | 82278 | 3.9732 | 0.3448 | |
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| 0.412 | 10.0 | 91420 | 4.2945 | 0.3565 | |
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| 0.3961 | 11.0 | 100562 | 4.6127 | 0.3772 | |
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| 0.3292 | 12.0 | 109704 | 4.9586 | 0.3805 | |
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| 0.318 | 13.0 | 118846 | 5.2615 | 0.3887 | |
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| 0.2936 | 14.0 | 127988 | 5.4567 | 0.3931 | |
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| 0.2671 | 15.0 | 137130 | 5.6902 | 0.3965 | |
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| 0.2301 | 16.0 | 146272 | 5.7685 | 0.4010 | |
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### Framework versions |
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- Transformers 4.12.3 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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