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---
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license: mit
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base_model: FacebookAI/roberta-large
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tags:
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- generated_from_trainer
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model-index:
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- name: non_green_as_train_contextroberta-large_20e
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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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# non_green_as_train_contextroberta-large_20e
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This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3214
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- Val Accuracy: 0.9779
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- Val Precision: 0.6893
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- Val Recall: 0.7568
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- Val F1: 0.7215
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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: 5e-06
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- train_batch_size: 16
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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: 20
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Val Accuracy | Val Precision | Val Recall | Val F1 |
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|:-------------:|:-----:|:------:|:---------------:|:------------:|:-------------:|:----------:|:------:|
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| 0.0601 | 1.0 | 7739 | 0.0767 | 0.9763 | 0.6646 | 0.7518 | 0.7055 |
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| 0.0493 | 2.0 | 15478 | 0.0995 | 0.9785 | 0.7181 | 0.7094 | 0.7137 |
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| 0.0305 | 3.0 | 23217 | 0.1216 | 0.9765 | 0.6670 | 0.7578 | 0.7095 |
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| 0.0196 | 4.0 | 30956 | 0.1275 | 0.9786 | 0.7066 | 0.7437 | 0.7247 |
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| 0.0161 | 5.0 | 38695 | 0.1521 | 0.9768 | 0.7164 | 0.6398 | 0.6759 |
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| 0.0141 | 6.0 | 46434 | 0.1643 | 0.9785 | 0.7103 | 0.7275 | 0.7188 |
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| 0.007 | 7.0 | 54173 | 0.1660 | 0.9769 | 0.6739 | 0.7528 | 0.7112 |
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| 0.0052 | 8.0 | 61912 | 0.1855 | 0.9783 | 0.7036 | 0.7376 | 0.7202 |
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| 0.0048 | 9.0 | 69651 | 0.1845 | 0.9781 | 0.7042 | 0.7255 | 0.7147 |
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| 0.0031 | 10.0 | 77390 | 0.2165 | 0.9782 | 0.7225 | 0.6882 | 0.7049 |
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| 0.0036 | 11.0 | 85129 | 0.2271 | 0.9783 | 0.7223 | 0.6902 | 0.7059 |
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| 0.0029 | 12.0 | 92868 | 0.2345 | 0.9770 | 0.6887 | 0.7144 | 0.7013 |
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| 0.0015 | 13.0 | 100607 | 0.2636 | 0.9781 | 0.7307 | 0.6680 | 0.6979 |
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| 0.0045 | 14.0 | 108346 | 0.2493 | 0.9781 | 0.6846 | 0.7820 | 0.7301 |
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| 0.0005 | 15.0 | 116085 | 0.2563 | 0.9774 | 0.6789 | 0.7639 | 0.7189 |
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| 0.0007 | 16.0 | 123824 | 0.2856 | 0.9784 | 0.7193 | 0.7033 | 0.7112 |
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| 0.0 | 17.0 | 131563 | 0.2809 | 0.9782 | 0.7136 | 0.7064 | 0.7099 |
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| 0.0 | 18.0 | 139302 | 0.3033 | 0.9781 | 0.6957 | 0.7497 | 0.7217 |
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| 0.0 | 19.0 | 147041 | 0.3207 | 0.9782 | 0.6909 | 0.7669 | 0.7269 |
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| 0.0 | 20.0 | 154780 | 0.3214 | 0.9779 | 0.6893 | 0.7568 | 0.7215 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.1.2
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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