jrtec-distilroberta-base-mrpc-glue-omar-espejel
This model is a fine-tuned version of distilroberta-base on the datasetX dataset. It achieves the following results on the evaluation set:
- Loss: 0.4901
- Accuracy: 0.8162
- F1: 0.8748
Model description
More information needed
Intended uses & limitations
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Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
0.4845 | 1.09 | 500 | 0.4901 | 0.8162 | 0.8748 |
0.3706 | 2.18 | 1000 | 0.6421 | 0.8162 | 0.8691 |
0.2003 | 3.27 | 1500 | 0.9711 | 0.8162 | 0.8760 |
0.1281 | 4.36 | 2000 | 0.8224 | 0.8480 | 0.8893 |
0.0717 | 5.45 | 2500 | 1.1803 | 0.8113 | 0.8511 |
0.0344 | 6.54 | 3000 | 1.1759 | 0.8480 | 0.8935 |
0.0277 | 7.63 | 3500 | 1.2140 | 0.8456 | 0.8927 |
0.0212 | 8.71 | 4000 | 1.0895 | 0.8554 | 0.8974 |
0.0071 | 9.8 | 4500 | 1.1849 | 0.8554 | 0.8991 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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Dataset used to train jrtec/jrtec-distilroberta-base-mrpc-glue-omar-espejel
Evaluation results
- Accuracy on datasetXself-reported0.816
- F1 on datasetXself-reported0.875