scenario-TCR-data-AmazonScience-massive-all_1.1-model-xlm-roberta-base
This model is a fine-tuned version of xlm-roberta-base on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.8546
- Accuracy: 0.8443
- F1: 0.8187
Model description
More information needed
Intended uses & limitations
More information needed
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
0.6138 | 0.27 | 5000 | 0.6955 | 0.8197 | 0.7723 |
0.4705 | 0.53 | 10000 | 0.6878 | 0.8280 | 0.7855 |
0.4019 | 0.8 | 15000 | 0.6850 | 0.8368 | 0.8005 |
0.2929 | 1.07 | 20000 | 0.7296 | 0.8316 | 0.7965 |
0.3011 | 1.34 | 25000 | 0.7140 | 0.8426 | 0.8139 |
0.2921 | 1.6 | 30000 | 0.7252 | 0.8418 | 0.8152 |
0.2799 | 1.87 | 35000 | 0.7186 | 0.8431 | 0.8152 |
0.2103 | 2.14 | 40000 | 0.7557 | 0.8462 | 0.8172 |
0.2251 | 2.41 | 45000 | 0.7926 | 0.8411 | 0.8095 |
0.2118 | 2.67 | 50000 | 0.7915 | 0.8427 | 0.8126 |
0.2239 | 2.94 | 55000 | 0.7813 | 0.8416 | 0.8076 |
0.1727 | 3.21 | 60000 | 0.8273 | 0.8471 | 0.8224 |
0.1785 | 3.47 | 65000 | 0.8192 | 0.8447 | 0.8149 |
0.2008 | 3.74 | 70000 | 0.8043 | 0.8464 | 0.8213 |
0.1773 | 4.01 | 75000 | 0.8555 | 0.8407 | 0.8121 |
0.165 | 4.28 | 80000 | 0.8556 | 0.8456 | 0.8218 |
0.1658 | 4.54 | 85000 | 0.8546 | 0.8443 | 0.8187 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.0
- Downloads last month
- 28
Finetuned from
Evaluation results
- Accuracy on massivevalidation set self-reported0.844
- F1 on massivevalidation set self-reported0.819