---
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