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update model card README.md
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README.md
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| 1 |
+
---
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| 2 |
+
license: mit
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| 3 |
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tags:
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+
- generated_from_trainer
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| 5 |
+
model-index:
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- name: BERiT_2000_2_layers_40_epochs
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| 7 |
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results: []
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| 8 |
+
---
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| 9 |
+
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| 10 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| 11 |
+
should probably proofread and complete it, then remove this comment. -->
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| 12 |
+
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| 13 |
+
# BERiT_2000_2_layers_40_epochs
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+
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| 15 |
+
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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| 16 |
+
It achieves the following results on the evaluation set:
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- Loss: 6.8375
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| 18 |
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## Model description
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| 20 |
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More information needed
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## Intended uses & limitations
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| 24 |
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More information needed
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| 26 |
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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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| 32 |
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### Training hyperparameters
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| 34 |
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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| 37 |
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- train_batch_size: 8
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| 38 |
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- eval_batch_size: 8
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| 39 |
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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: 40
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- label_smoothing_factor: 0.2
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| 44 |
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| 45 |
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### Training results
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| 46 |
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| 47 |
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| Training Loss | Epoch | Step | Validation Loss |
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| 48 |
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|:-------------:|:-----:|:------:|:---------------:|
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| 49 |
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| 15.0851 | 0.19 | 500 | 8.5468 |
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| 50 |
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| 7.8971 | 0.39 | 1000 | 7.3376 |
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| 51 |
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| 7.3108 | 0.58 | 1500 | 7.1632 |
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| 52 |
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| 7.134 | 0.77 | 2000 | 7.0700 |
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| 53 |
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| 7.0956 | 0.97 | 2500 | 7.0723 |
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| 54 |
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| 7.0511 | 1.16 | 3000 | 6.9560 |
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| 55 |
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| 7.0313 | 1.36 | 3500 | 6.9492 |
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| 56 |
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| 7.0028 | 1.55 | 4000 | 6.9048 |
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| 57 |
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| 6.9563 | 1.74 | 4500 | 6.8456 |
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| 58 |
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| 6.9214 | 1.94 | 5000 | 6.8019 |
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| 59 |
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| 11.1596 | 2.13 | 5500 | 7.5882 |
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| 60 |
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| 7.5824 | 2.32 | 6000 | 7.1291 |
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| 61 |
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| 7.2581 | 2.52 | 6500 | 7.1123 |
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| 62 |
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| 7.2232 | 2.71 | 7000 | 7.1059 |
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| 63 |
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| 7.1734 | 2.9 | 7500 | 7.1120 |
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| 64 |
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| 7.1504 | 3.1 | 8000 | 7.0946 |
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| 65 |
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| 7.1314 | 3.29 | 8500 | 7.0799 |
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| 66 |
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| 7.1236 | 3.49 | 9000 | 7.1175 |
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| 67 |
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| 7.1275 | 3.68 | 9500 | 7.0905 |
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| 68 |
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| 7.1087 | 3.87 | 10000 | 7.0839 |
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| 69 |
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| 7.1212 | 4.07 | 10500 | 7.0822 |
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| 70 |
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| 7.1136 | 4.26 | 11000 | 7.0703 |
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| 71 |
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| 7.1025 | 4.45 | 11500 | 7.1035 |
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| 72 |
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| 7.0931 | 4.65 | 12000 | 7.0759 |
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| 73 |
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| 7.0899 | 4.84 | 12500 | 7.0883 |
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| 74 |
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| 7.0834 | 5.03 | 13000 | 7.1307 |
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| 75 |
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| 7.0761 | 5.23 | 13500 | 7.0642 |
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| 76 |
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| 7.0706 | 5.42 | 14000 | 7.0324 |
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| 77 |
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| 7.0678 | 5.62 | 14500 | 7.0704 |
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| 78 |
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| 7.0614 | 5.81 | 15000 | 7.0317 |
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| 79 |
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| 7.0569 | 6.0 | 15500 | 7.0421 |
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| 80 |
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| 7.057 | 6.2 | 16000 | 7.0250 |
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| 81 |
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| 7.0503 | 6.39 | 16500 | 7.0129 |
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| 82 |
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| 7.0529 | 6.58 | 17000 | 7.0316 |
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| 83 |
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| 7.0453 | 6.78 | 17500 | 7.0436 |
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| 84 |
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| 7.0218 | 6.97 | 18000 | 7.0064 |
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| 85 |
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| 7.0415 | 7.16 | 18500 | 7.0385 |
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| 86 |
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| 7.0338 | 7.36 | 19000 | 6.9756 |
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| 87 |
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| 7.0488 | 7.55 | 19500 | 7.0054 |
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| 88 |
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| 7.0347 | 7.75 | 20000 | 6.9946 |
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| 89 |
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| 7.0464 | 7.94 | 20500 | 7.0055 |
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| 90 |
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| 7.017 | 8.13 | 21000 | 7.0158 |
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| 91 |
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| 7.0159 | 8.33 | 21500 | 7.0052 |
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| 92 |
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| 7.0223 | 8.52 | 22000 | 6.9925 |
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| 6.9989 | 8.71 | 22500 | 7.0307 |
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| 7.0218 | 8.91 | 23000 | 6.9767 |
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| 95 |
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| 6.9998 | 9.1 | 23500 | 7.0096 |
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| 96 |
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| 7.01 | 9.3 | 24000 | 6.9599 |
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| 6.9964 | 9.49 | 24500 | 6.9896 |
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| 98 |
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| 6.9906 | 9.68 | 25000 | 6.9903 |
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| 99 |
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| 7.0336 | 9.88 | 25500 | 6.9807 |
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| 100 |
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| 7.0053 | 10.07 | 26000 | 6.9776 |
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| 101 |
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| 6.9826 | 10.26 | 26500 | 6.9836 |
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| 102 |
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| 6.9897 | 10.46 | 27000 | 6.9886 |
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| 103 |
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| 6.9829 | 10.65 | 27500 | 6.9991 |
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| 104 |
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| 6.9849 | 10.84 | 28000 | 6.9651 |
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| 105 |
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| 6.9901 | 11.04 | 28500 | 6.9822 |
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| 6.9852 | 11.23 | 29000 | 6.9921 |
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| 107 |
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| 6.9757 | 11.43 | 29500 | 6.9636 |
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| 6.991 | 11.62 | 30000 | 6.9952 |
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| 6.9818 | 11.81 | 30500 | 6.9799 |
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| 6.9911 | 12.01 | 31000 | 6.9725 |
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| 6.9423 | 12.2 | 31500 | 6.9540 |
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| 6.9885 | 12.39 | 32000 | 6.9771 |
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| 6.9636 | 12.59 | 32500 | 6.9475 |
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| 6.9567 | 12.78 | 33000 | 6.9653 |
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| 6.9749 | 12.97 | 33500 | 6.9711 |
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| 6.9739 | 13.17 | 34000 | 6.9691 |
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| 6.9651 | 13.36 | 34500 | 6.9569 |
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| 6.9599 | 13.56 | 35000 | 6.9608 |
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| 6.957 | 13.75 | 35500 | 6.9531 |
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| 6.9539 | 13.94 | 36000 | 6.9704 |
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| 6.958 | 14.14 | 36500 | 6.9478 |
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| 6.9597 | 14.33 | 37000 | 6.9510 |
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| 6.9466 | 14.52 | 37500 | 6.9625 |
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| 6.9518 | 14.72 | 38000 | 6.9787 |
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| 6.9509 | 14.91 | 38500 | 6.9391 |
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| 126 |
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| 6.9505 | 15.1 | 39000 | 6.9694 |
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| 6.9311 | 15.3 | 39500 | 6.9440 |
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| 6.9513 | 15.49 | 40000 | 6.9425 |
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| 6.9268 | 15.69 | 40500 | 6.9223 |
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| 6.9415 | 15.88 | 41000 | 6.9435 |
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| 6.9308 | 16.07 | 41500 | 6.9281 |
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| 6.9216 | 16.27 | 42000 | 6.9415 |
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| 6.9265 | 16.46 | 42500 | 6.9164 |
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| 6.9023 | 16.65 | 43000 | 6.9237 |
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| 6.9407 | 16.85 | 43500 | 6.9100 |
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| 6.9211 | 17.04 | 44000 | 6.9295 |
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| 6.9147 | 17.23 | 44500 | 6.9131 |
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| 6.9224 | 17.43 | 45000 | 6.9188 |
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| 6.9215 | 17.62 | 45500 | 6.9077 |
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| 6.915 | 17.82 | 46000 | 6.9371 |
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| 6.906 | 18.01 | 46500 | 6.8932 |
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| 6.91 | 18.2 | 47000 | 6.9100 |
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| 6.8999 | 18.4 | 47500 | 6.9251 |
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| 6.9113 | 18.59 | 48000 | 6.9078 |
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| 6.9197 | 18.78 | 48500 | 6.9099 |
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| 6.8985 | 18.98 | 49000 | 6.9074 |
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| 6.9009 | 19.17 | 49500 | 6.8971 |
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| 6.8937 | 19.36 | 50000 | 6.8982 |
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| 6.9094 | 19.56 | 50500 | 6.9077 |
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| 6.9069 | 19.75 | 51000 | 6.9006 |
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| 6.8991 | 19.95 | 51500 | 6.8912 |
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| 6.8924 | 20.14 | 52000 | 6.8881 |
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| 6.899 | 20.33 | 52500 | 6.8899 |
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| 6.9028 | 20.53 | 53000 | 6.8938 |
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| 6.8997 | 20.72 | 53500 | 6.8822 |
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| 156 |
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| 6.8943 | 20.91 | 54000 | 6.9005 |
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| 6.8804 | 21.11 | 54500 | 6.9048 |
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| 6.8848 | 21.3 | 55000 | 6.9062 |
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| 6.9072 | 21.49 | 55500 | 6.9104 |
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| 6.8783 | 21.69 | 56000 | 6.9069 |
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| 6.8879 | 21.88 | 56500 | 6.8938 |
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| 6.8922 | 22.08 | 57000 | 6.8797 |
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| 6.8892 | 22.27 | 57500 | 6.9168 |
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| 6.8863 | 22.46 | 58000 | 6.8820 |
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| 6.8822 | 22.66 | 58500 | 6.9130 |
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| 6.8752 | 22.85 | 59000 | 6.8973 |
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| 167 |
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| 6.8823 | 23.04 | 59500 | 6.8933 |
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| 168 |
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| 6.8813 | 23.24 | 60000 | 6.8919 |
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| 6.8787 | 23.43 | 60500 | 6.8855 |
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| 6.8886 | 23.63 | 61000 | 6.8956 |
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| 6.8744 | 23.82 | 61500 | 6.9092 |
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| 6.8799 | 24.01 | 62000 | 6.8944 |
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| 173 |
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| 6.879 | 24.21 | 62500 | 6.8850 |
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| 6.8797 | 24.4 | 63000 | 6.8782 |
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| 6.8724 | 24.59 | 63500 | 6.8691 |
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| 6.8803 | 24.79 | 64000 | 6.8965 |
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| 6.8899 | 24.98 | 64500 | 6.8986 |
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| 6.8873 | 25.17 | 65000 | 6.9034 |
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| 6.8777 | 25.37 | 65500 | 6.8658 |
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| 6.8784 | 25.56 | 66000 | 6.8803 |
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| 6.8791 | 25.76 | 66500 | 6.8727 |
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| 6.8736 | 25.95 | 67000 | 6.8832 |
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| 6.8865 | 26.14 | 67500 | 6.8811 |
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| 6.8668 | 26.34 | 68000 | 6.8817 |
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| 185 |
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| 6.8709 | 26.53 | 68500 | 6.8945 |
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| 6.8755 | 26.72 | 69000 | 6.8777 |
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| 6.8635 | 26.92 | 69500 | 6.8747 |
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| 6.8752 | 27.11 | 70000 | 6.8875 |
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| 189 |
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| 6.8729 | 27.3 | 70500 | 6.8696 |
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| 6.8728 | 27.5 | 71000 | 6.8659 |
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| 6.8692 | 27.69 | 71500 | 6.8856 |
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| 6.868 | 27.89 | 72000 | 6.8689 |
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| 6.8668 | 28.08 | 72500 | 6.8877 |
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| 6.8576 | 28.27 | 73000 | 6.8783 |
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| 6.8633 | 28.47 | 73500 | 6.8828 |
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| 6.8737 | 28.66 | 74000 | 6.8717 |
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| 197 |
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| 6.8702 | 28.85 | 74500 | 6.8485 |
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| 198 |
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| 6.8785 | 29.05 | 75000 | 6.8771 |
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| 6.8818 | 29.24 | 75500 | 6.8815 |
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| 6.8647 | 29.43 | 76000 | 6.8877 |
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| 6.8574 | 29.63 | 76500 | 6.8920 |
|
| 202 |
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| 6.8474 | 29.82 | 77000 | 6.8936 |
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| 6.8558 | 30.02 | 77500 | 6.8768 |
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| 204 |
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| 6.8645 | 30.21 | 78000 | 6.8921 |
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| 205 |
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| 6.8786 | 30.4 | 78500 | 6.8604 |
|
| 206 |
+
| 6.8693 | 30.6 | 79000 | 6.8603 |
|
| 207 |
+
| 6.855 | 30.79 | 79500 | 6.8559 |
|
| 208 |
+
| 6.8429 | 30.98 | 80000 | 6.8746 |
|
| 209 |
+
| 6.8688 | 31.18 | 80500 | 6.8774 |
|
| 210 |
+
| 6.8735 | 31.37 | 81000 | 6.8643 |
|
| 211 |
+
| 6.8541 | 31.56 | 81500 | 6.8767 |
|
| 212 |
+
| 6.8695 | 31.76 | 82000 | 6.8804 |
|
| 213 |
+
| 6.8607 | 31.95 | 82500 | 6.8674 |
|
| 214 |
+
| 6.8538 | 32.15 | 83000 | 6.8572 |
|
| 215 |
+
| 6.8472 | 32.34 | 83500 | 6.8683 |
|
| 216 |
+
| 6.8763 | 32.53 | 84000 | 6.8758 |
|
| 217 |
+
| 6.8405 | 32.73 | 84500 | 6.8764 |
|
| 218 |
+
| 6.8658 | 32.92 | 85000 | 6.8614 |
|
| 219 |
+
| 6.8834 | 33.11 | 85500 | 6.8641 |
|
| 220 |
+
| 6.8554 | 33.31 | 86000 | 6.8787 |
|
| 221 |
+
| 6.8738 | 33.5 | 86500 | 6.8747 |
|
| 222 |
+
| 6.848 | 33.69 | 87000 | 6.8699 |
|
| 223 |
+
| 6.8621 | 33.89 | 87500 | 6.8654 |
|
| 224 |
+
| 6.8543 | 34.08 | 88000 | 6.8639 |
|
| 225 |
+
| 6.8606 | 34.28 | 88500 | 6.8852 |
|
| 226 |
+
| 6.8666 | 34.47 | 89000 | 6.8840 |
|
| 227 |
+
| 6.8717 | 34.66 | 89500 | 6.8773 |
|
| 228 |
+
| 6.854 | 34.86 | 90000 | 6.8671 |
|
| 229 |
+
| 6.8526 | 35.05 | 90500 | 6.8762 |
|
| 230 |
+
| 6.8592 | 35.24 | 91000 | 6.8644 |
|
| 231 |
+
| 6.8641 | 35.44 | 91500 | 6.8599 |
|
| 232 |
+
| 6.8655 | 35.63 | 92000 | 6.8622 |
|
| 233 |
+
| 6.8557 | 35.82 | 92500 | 6.8671 |
|
| 234 |
+
| 6.8546 | 36.02 | 93000 | 6.8573 |
|
| 235 |
+
| 6.853 | 36.21 | 93500 | 6.8542 |
|
| 236 |
+
| 6.8597 | 36.41 | 94000 | 6.8518 |
|
| 237 |
+
| 6.8576 | 36.6 | 94500 | 6.8700 |
|
| 238 |
+
| 6.8549 | 36.79 | 95000 | 6.8628 |
|
| 239 |
+
| 6.8576 | 36.99 | 95500 | 6.8695 |
|
| 240 |
+
| 6.8505 | 37.18 | 96000 | 6.8870 |
|
| 241 |
+
| 6.8564 | 37.37 | 96500 | 6.8898 |
|
| 242 |
+
| 6.8627 | 37.57 | 97000 | 6.8619 |
|
| 243 |
+
| 6.8502 | 37.76 | 97500 | 6.8696 |
|
| 244 |
+
| 6.8548 | 37.96 | 98000 | 6.8663 |
|
| 245 |
+
| 6.8512 | 38.15 | 98500 | 6.8683 |
|
| 246 |
+
| 6.8484 | 38.34 | 99000 | 6.8605 |
|
| 247 |
+
| 6.8581 | 38.54 | 99500 | 6.8749 |
|
| 248 |
+
| 6.8525 | 38.73 | 100000 | 6.8849 |
|
| 249 |
+
| 6.8375 | 38.92 | 100500 | 6.8712 |
|
| 250 |
+
| 6.8423 | 39.12 | 101000 | 6.8905 |
|
| 251 |
+
| 6.8559 | 39.31 | 101500 | 6.8574 |
|
| 252 |
+
| 6.8441 | 39.5 | 102000 | 6.8722 |
|
| 253 |
+
| 6.8467 | 39.7 | 102500 | 6.8550 |
|
| 254 |
+
| 6.8389 | 39.89 | 103000 | 6.8375 |
|
| 255 |
+
|
| 256 |
+
|
| 257 |
+
### Framework versions
|
| 258 |
+
|
| 259 |
+
- Transformers 4.24.0
|
| 260 |
+
- Pytorch 1.12.1+cu113
|
| 261 |
+
- Datasets 2.7.0
|
| 262 |
+
- Tokenizers 0.13.2
|