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--- |
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license: mit |
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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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- precision |
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- recall |
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- f1 |
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base_model: roberta-base |
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model-index: |
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- name: run-3 |
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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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# run-3 |
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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: 2.4223 |
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- Accuracy: 0.75 |
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- Precision: 0.7128 |
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- Recall: 0.6998 |
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- F1: 0.7043 |
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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-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0025 | 1.0 | 50 | 0.8925 | 0.63 | 0.6703 | 0.5704 | 0.5060 | |
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| 0.8187 | 2.0 | 100 | 0.7915 | 0.595 | 0.6007 | 0.5926 | 0.5344 | |
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| 0.5671 | 3.0 | 150 | 0.9573 | 0.695 | 0.6520 | 0.6350 | 0.6380 | |
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| 0.3218 | 4.0 | 200 | 0.9195 | 0.68 | 0.6447 | 0.6539 | 0.6461 | |
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| 0.2208 | 5.0 | 250 | 1.2429 | 0.715 | 0.6801 | 0.6617 | 0.6663 | |
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| 0.1614 | 6.0 | 300 | 1.5295 | 0.71 | 0.6736 | 0.6543 | 0.6423 | |
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| 0.1129 | 7.0 | 350 | 2.1055 | 0.71 | 0.6779 | 0.6413 | 0.6511 | |
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| 0.098 | 8.0 | 400 | 1.9579 | 0.705 | 0.6697 | 0.6558 | 0.6601 | |
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| 0.0479 | 9.0 | 450 | 2.0535 | 0.72 | 0.6794 | 0.6663 | 0.6711 | |
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| 0.0173 | 10.0 | 500 | 2.5381 | 0.7 | 0.6838 | 0.6604 | 0.6608 | |
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| 0.0308 | 11.0 | 550 | 2.4592 | 0.735 | 0.7014 | 0.6851 | 0.6901 | |
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| 0.0265 | 12.0 | 600 | 2.3131 | 0.725 | 0.6910 | 0.6845 | 0.6849 | |
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| 0.016 | 13.0 | 650 | 2.4025 | 0.74 | 0.7035 | 0.6915 | 0.6949 | |
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| 0.013 | 14.0 | 700 | 2.3933 | 0.745 | 0.7070 | 0.6831 | 0.6909 | |
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| 0.016 | 15.0 | 750 | 2.6819 | 0.725 | 0.7006 | 0.6738 | 0.6759 | |
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| 0.0126 | 16.0 | 800 | 2.3679 | 0.74 | 0.7050 | 0.6839 | 0.6898 | |
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| 0.0023 | 17.0 | 850 | 2.5252 | 0.745 | 0.7119 | 0.6880 | 0.6933 | |
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| 0.01 | 18.0 | 900 | 2.5598 | 0.74 | 0.7056 | 0.6828 | 0.6906 | |
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| 0.0093 | 19.0 | 950 | 2.4353 | 0.745 | 0.7057 | 0.6922 | 0.6974 | |
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| 0.0039 | 20.0 | 1000 | 2.4223 | 0.75 | 0.7128 | 0.6998 | 0.7043 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Tokenizers 0.13.2 |
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