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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: roberta-base-culinary-finetuned |
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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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# roberta-base-culinary-finetuned |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0657 |
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- F1: 0.9929 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 0.1803 | 0.11 | 500 | 0.1939 | 0.9611 | |
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| 0.1543 | 0.22 | 1000 | 0.1364 | 0.9669 | |
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| 0.1213 | 0.32 | 1500 | 0.1487 | 0.9728 | |
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| 0.1079 | 0.43 | 2000 | 0.0855 | 0.9773 | |
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| 0.0975 | 0.54 | 2500 | 0.0844 | 0.9831 | |
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| 0.0855 | 0.65 | 3000 | 0.0785 | 0.9831 | |
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| 0.0844 | 0.76 | 3500 | 0.0679 | 0.9857 | |
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| 0.0793 | 0.86 | 4000 | 0.0489 | 0.9890 | |
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| 0.0864 | 0.97 | 4500 | 0.0399 | 0.9903 | |
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| 0.049 | 1.08 | 5000 | 0.0528 | 0.9890 | |
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| 0.0353 | 1.19 | 5500 | 0.0635 | 0.9877 | |
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| 0.0321 | 1.3 | 6000 | 0.0542 | 0.9903 | |
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| 0.0311 | 1.41 | 6500 | 0.0559 | 0.9896 | |
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| 0.0315 | 1.51 | 7000 | 0.0736 | 0.9857 | |
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| 0.04 | 1.62 | 7500 | 0.0648 | 0.9909 | |
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| 0.0265 | 1.73 | 8000 | 0.0608 | 0.9909 | |
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| 0.0443 | 1.84 | 8500 | 0.0617 | 0.9883 | |
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| 0.0443 | 1.95 | 9000 | 0.0555 | 0.9896 | |
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| 0.0235 | 2.05 | 9500 | 0.0608 | 0.9903 | |
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| 0.0139 | 2.16 | 10000 | 0.0613 | 0.9922 | |
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| 0.0126 | 2.27 | 10500 | 0.0739 | 0.9903 | |
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| 0.0164 | 2.38 | 11000 | 0.0679 | 0.9903 | |
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| 0.0172 | 2.49 | 11500 | 0.0606 | 0.9922 | |
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| 0.0175 | 2.59 | 12000 | 0.0442 | 0.9942 | |
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| 0.01 | 2.7 | 12500 | 0.0661 | 0.9916 | |
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| 0.0059 | 2.81 | 13000 | 0.0659 | 0.9929 | |
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| 0.0216 | 2.92 | 13500 | 0.0504 | 0.9929 | |
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| 0.0123 | 3.03 | 14000 | 0.0584 | 0.9929 | |
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| 0.0047 | 3.14 | 14500 | 0.0573 | 0.9929 | |
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| 0.0123 | 3.24 | 15000 | 0.0511 | 0.9935 | |
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| 0.0027 | 3.35 | 15500 | 0.0579 | 0.9942 | |
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| 0.0025 | 3.46 | 16000 | 0.0602 | 0.9935 | |
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| 0.0051 | 3.57 | 16500 | 0.0598 | 0.9935 | |
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| 0.0044 | 3.68 | 17000 | 0.0617 | 0.9929 | |
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| 0.0061 | 3.78 | 17500 | 0.0634 | 0.9935 | |
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| 0.0048 | 3.89 | 18000 | 0.0672 | 0.9929 | |
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| 0.0078 | 4.0 | 18500 | 0.0657 | 0.9929 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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