---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: '20230826054840'
  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. -->

# 20230826054840

This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4136
- Accuracy: 0.71

## 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: 0.02
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 80.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 25   | 0.6183          | 0.53     |
| No log        | 2.0   | 50   | 0.4189          | 0.62     |
| No log        | 3.0   | 75   | 0.4351          | 0.6      |
| No log        | 4.0   | 100  | 0.4181          | 0.6      |
| No log        | 5.0   | 125  | 0.4105          | 0.62     |
| No log        | 6.0   | 150  | 0.4140          | 0.63     |
| No log        | 7.0   | 175  | 0.4052          | 0.66     |
| No log        | 8.0   | 200  | 0.4322          | 0.66     |
| No log        | 9.0   | 225  | 0.4364          | 0.41     |
| No log        | 10.0  | 250  | 0.4247          | 0.55     |
| No log        | 11.0  | 275  | 0.4261          | 0.53     |
| No log        | 12.0  | 300  | 0.4176          | 0.6      |
| No log        | 13.0  | 325  | 0.4108          | 0.58     |
| No log        | 14.0  | 350  | 0.4305          | 0.51     |
| No log        | 15.0  | 375  | 0.4064          | 0.61     |
| No log        | 16.0  | 400  | 0.4032          | 0.59     |
| No log        | 17.0  | 425  | 0.4098          | 0.63     |
| No log        | 18.0  | 450  | 0.4132          | 0.61     |
| No log        | 19.0  | 475  | 0.3925          | 0.65     |
| 0.7171        | 20.0  | 500  | 0.3957          | 0.69     |
| 0.7171        | 21.0  | 525  | 0.4292          | 0.64     |
| 0.7171        | 22.0  | 550  | 0.4025          | 0.63     |
| 0.7171        | 23.0  | 575  | 0.3997          | 0.69     |
| 0.7171        | 24.0  | 600  | 0.4115          | 0.62     |
| 0.7171        | 25.0  | 625  | 0.4044          | 0.67     |
| 0.7171        | 26.0  | 650  | 0.4098          | 0.69     |
| 0.7171        | 27.0  | 675  | 0.4051          | 0.65     |
| 0.7171        | 28.0  | 700  | 0.4244          | 0.72     |
| 0.7171        | 29.0  | 725  | 0.4032          | 0.64     |
| 0.7171        | 30.0  | 750  | 0.4136          | 0.7      |
| 0.7171        | 31.0  | 775  | 0.3993          | 0.68     |
| 0.7171        | 32.0  | 800  | 0.4170          | 0.72     |
| 0.7171        | 33.0  | 825  | 0.4038          | 0.71     |
| 0.7171        | 34.0  | 850  | 0.4251          | 0.72     |
| 0.7171        | 35.0  | 875  | 0.4079          | 0.66     |
| 0.7171        | 36.0  | 900  | 0.4119          | 0.71     |
| 0.7171        | 37.0  | 925  | 0.4075          | 0.67     |
| 0.7171        | 38.0  | 950  | 0.4406          | 0.73     |
| 0.7171        | 39.0  | 975  | 0.4081          | 0.72     |
| 0.4731        | 40.0  | 1000 | 0.4191          | 0.67     |
| 0.4731        | 41.0  | 1025 | 0.4217          | 0.68     |
| 0.4731        | 42.0  | 1050 | 0.3983          | 0.73     |
| 0.4731        | 43.0  | 1075 | 0.4092          | 0.66     |
| 0.4731        | 44.0  | 1100 | 0.4248          | 0.69     |
| 0.4731        | 45.0  | 1125 | 0.4218          | 0.68     |
| 0.4731        | 46.0  | 1150 | 0.4371          | 0.7      |
| 0.4731        | 47.0  | 1175 | 0.4099          | 0.69     |
| 0.4731        | 48.0  | 1200 | 0.4300          | 0.69     |
| 0.4731        | 49.0  | 1225 | 0.4094          | 0.72     |
| 0.4731        | 50.0  | 1250 | 0.4206          | 0.71     |
| 0.4731        | 51.0  | 1275 | 0.4241          | 0.72     |
| 0.4731        | 52.0  | 1300 | 0.4253          | 0.66     |
| 0.4731        | 53.0  | 1325 | 0.4117          | 0.66     |
| 0.4731        | 54.0  | 1350 | 0.4174          | 0.67     |
| 0.4731        | 55.0  | 1375 | 0.4131          | 0.67     |
| 0.4731        | 56.0  | 1400 | 0.4231          | 0.67     |
| 0.4731        | 57.0  | 1425 | 0.4059          | 0.7      |
| 0.4731        | 58.0  | 1450 | 0.4168          | 0.72     |
| 0.4731        | 59.0  | 1475 | 0.4236          | 0.68     |
| 0.4204        | 60.0  | 1500 | 0.4001          | 0.68     |
| 0.4204        | 61.0  | 1525 | 0.4158          | 0.71     |
| 0.4204        | 62.0  | 1550 | 0.4303          | 0.68     |
| 0.4204        | 63.0  | 1575 | 0.4155          | 0.65     |
| 0.4204        | 64.0  | 1600 | 0.4195          | 0.66     |
| 0.4204        | 65.0  | 1625 | 0.4315          | 0.67     |
| 0.4204        | 66.0  | 1650 | 0.4240          | 0.71     |
| 0.4204        | 67.0  | 1675 | 0.4191          | 0.68     |
| 0.4204        | 68.0  | 1700 | 0.4214          | 0.71     |
| 0.4204        | 69.0  | 1725 | 0.4170          | 0.71     |
| 0.4204        | 70.0  | 1750 | 0.4158          | 0.68     |
| 0.4204        | 71.0  | 1775 | 0.4230          | 0.69     |
| 0.4204        | 72.0  | 1800 | 0.4106          | 0.69     |
| 0.4204        | 73.0  | 1825 | 0.4255          | 0.68     |
| 0.4204        | 74.0  | 1850 | 0.4223          | 0.67     |
| 0.4204        | 75.0  | 1875 | 0.4124          | 0.7      |
| 0.4204        | 76.0  | 1900 | 0.4114          | 0.7      |
| 0.4204        | 77.0  | 1925 | 0.4115          | 0.71     |
| 0.4204        | 78.0  | 1950 | 0.4136          | 0.71     |
| 0.4204        | 79.0  | 1975 | 0.4150          | 0.71     |
| 0.3939        | 80.0  | 2000 | 0.4136          | 0.71     |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3