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---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: result_xlmr_siqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result_xlmr_siqa
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the super_glue dataset. It trained first on SIQA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4143
- Accuracy: 0.79
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 44
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0152 | 0.2 | 10 | 1.0207 | 0.77 |
| 0.001 | 0.4 | 20 | 0.7651 | 0.82 |
| 0.0013 | 0.6 | 30 | 0.7756 | 0.79 |
| 0.0012 | 0.8 | 40 | 1.2054 | 0.8 |
| 0.0005 | 1.0 | 50 | 1.3034 | 0.79 |
| 0.0008 | 1.2 | 60 | 1.1920 | 0.76 |
| 0.0138 | 1.4 | 70 | 0.9139 | 0.76 |
| 0.0003 | 1.6 | 80 | 0.9160 | 0.78 |
| 0.0001 | 1.8 | 90 | 1.1525 | 0.8 |
| 0.0085 | 2.0 | 100 | 0.8657 | 0.79 |
| 0.0033 | 2.2 | 110 | 0.8925 | 0.79 |
| 0.0055 | 2.4 | 120 | 1.2264 | 0.78 |
| 0.0014 | 2.6 | 130 | 1.4958 | 0.8 |
| 0.0031 | 2.8 | 140 | 1.4250 | 0.79 |
| 0.0138 | 3.0 | 150 | 1.4240 | 0.81 |
| 0.0304 | 3.2 | 160 | 1.4179 | 0.8 |
| 0.0 | 3.4 | 170 | 1.4685 | 0.8 |
| 0.0 | 3.6 | 180 | 1.4897 | 0.8 |
| 0.0015 | 3.8 | 190 | 1.2689 | 0.8 |
| 0.0001 | 4.0 | 200 | 1.0355 | 0.78 |
| 0.0007 | 4.2 | 210 | 1.1339 | 0.77 |
| 0.0002 | 4.4 | 220 | 1.1915 | 0.79 |
| 0.0001 | 4.6 | 230 | 1.1300 | 0.8 |
| 0.001 | 4.8 | 240 | 1.1464 | 0.79 |
| 0.0001 | 5.0 | 250 | 1.2227 | 0.78 |
| 0.0 | 5.2 | 260 | 1.3048 | 0.81 |
| 0.0 | 5.4 | 270 | 1.3418 | 0.79 |
| 0.0093 | 5.6 | 280 | 1.3442 | 0.78 |
| 0.0004 | 5.8 | 290 | 1.2721 | 0.8 |
| 0.0035 | 6.0 | 300 | 1.1852 | 0.77 |
| 0.0016 | 6.2 | 310 | 1.1745 | 0.77 |
| 0.0003 | 6.4 | 320 | 1.1138 | 0.8 |
| 0.0002 | 6.6 | 330 | 1.2342 | 0.79 |
| 0.0055 | 6.8 | 340 | 1.3594 | 0.79 |
| 0.0 | 7.0 | 350 | 1.4109 | 0.79 |
| 0.0 | 7.2 | 360 | 1.4677 | 0.78 |
| 0.0 | 7.4 | 370 | 1.4951 | 0.77 |
| 0.0 | 7.6 | 380 | 1.4987 | 0.77 |
| 0.0004 | 7.8 | 390 | 1.4517 | 0.77 |
| 0.0 | 8.0 | 400 | 1.4632 | 0.77 |
| 0.0 | 8.2 | 410 | 1.4825 | 0.78 |
| 0.0008 | 8.4 | 420 | 1.4486 | 0.79 |
| 0.0 | 8.6 | 430 | 1.4426 | 0.79 |
| 0.0 | 8.8 | 440 | 1.4216 | 0.79 |
| 0.0 | 9.0 | 450 | 1.4166 | 0.79 |
| 0.0 | 9.2 | 460 | 1.4161 | 0.79 |
| 0.0 | 9.4 | 470 | 1.4172 | 0.79 |
| 0.0003 | 9.6 | 480 | 1.4179 | 0.79 |
| 0.0286 | 9.8 | 490 | 1.4155 | 0.79 |
| 0.0 | 10.0 | 500 | 1.4143 | 0.79 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.0
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