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--- |
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library_name: transformers |
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license: mit |
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base_model: roberta-base |
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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: roberta-base-downstream-build_rr |
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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-downstream-build_rr |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8610 |
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- Precision-macro: 0.6015 |
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- Recall-macro: 0.5642 |
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- Macro-f1: 0.5742 |
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- Precision-micro: 0.7871 |
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- Recall-micro: 0.7871 |
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- Micro-f1: 0.7871 |
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- Accuracy: 0.7871 |
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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: 3e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 1 |
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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: 20.0 |
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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 | Precision-macro | Recall-macro | Macro-f1 | Precision-micro | Recall-micro | Micro-f1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:--------:| |
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| No log | 1.0 | 124 | 0.9703 | 0.5485 | 0.3447 | 0.3566 | 0.7155 | 0.7155 | 0.7155 | 0.7155 | |
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| No log | 2.0 | 248 | 0.8005 | 0.5181 | 0.5222 | 0.5080 | 0.7353 | 0.7353 | 0.7353 | 0.7353 | |
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| No log | 3.0 | 372 | 0.8156 | 0.5626 | 0.5322 | 0.5288 | 0.7454 | 0.7454 | 0.7454 | 0.7454 | |
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| No log | 4.0 | 496 | 0.7056 | 0.5881 | 0.5197 | 0.5180 | 0.7704 | 0.7704 | 0.7704 | 0.7704 | |
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| 1.0549 | 5.0 | 620 | 0.7526 | 0.5878 | 0.5906 | 0.5775 | 0.7642 | 0.7642 | 0.7642 | 0.7642 | |
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| 1.0549 | 6.0 | 744 | 0.7094 | 0.6336 | 0.5395 | 0.5649 | 0.7812 | 0.7812 | 0.7812 | 0.7812 | |
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| 1.0549 | 7.0 | 868 | 0.7391 | 0.6475 | 0.5339 | 0.5535 | 0.7808 | 0.7808 | 0.7808 | 0.7808 | |
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| 1.0549 | 8.0 | 992 | 0.7354 | 0.6169 | 0.5756 | 0.5881 | 0.7930 | 0.7930 | 0.7930 | 0.7930 | |
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| 0.545 | 9.0 | 1116 | 0.8143 | 0.5951 | 0.5963 | 0.5928 | 0.7805 | 0.7805 | 0.7805 | 0.7805 | |
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| 0.545 | 10.0 | 1240 | 0.8352 | 0.6029 | 0.5915 | 0.5918 | 0.7794 | 0.7794 | 0.7794 | 0.7794 | |
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| 0.545 | 11.0 | 1364 | 0.8610 | 0.6015 | 0.5642 | 0.5742 | 0.7871 | 0.7871 | 0.7871 | 0.7871 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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