---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: '20230826121217'
  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. -->

# 20230826121217

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: 0.4150
- Accuracy: 0.63

## 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: 0.001
- 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   | 0.4146          | 0.66     |
| No log        | 2.0   | 50   | 0.4116          | 0.66     |
| No log        | 3.0   | 75   | 0.4139          | 0.66     |
| No log        | 4.0   | 100  | 0.4170          | 0.64     |
| No log        | 5.0   | 125  | 0.4182          | 0.65     |
| No log        | 6.0   | 150  | 0.4208          | 0.57     |
| No log        | 7.0   | 175  | 0.4115          | 0.66     |
| No log        | 8.0   | 200  | 0.4157          | 0.66     |
| No log        | 9.0   | 225  | 0.4229          | 0.64     |
| No log        | 10.0  | 250  | 0.4205          | 0.65     |
| No log        | 11.0  | 275  | 0.4178          | 0.64     |
| No log        | 12.0  | 300  | 0.4131          | 0.67     |
| No log        | 13.0  | 325  | 0.4146          | 0.65     |
| No log        | 14.0  | 350  | 0.4202          | 0.63     |
| No log        | 15.0  | 375  | 0.4331          | 0.62     |
| No log        | 16.0  | 400  | 0.4120          | 0.66     |
| No log        | 17.0  | 425  | 0.4144          | 0.63     |
| No log        | 18.0  | 450  | 0.4182          | 0.64     |
| No log        | 19.0  | 475  | 0.4184          | 0.59     |
| 0.5392        | 20.0  | 500  | 0.4161          | 0.65     |
| 0.5392        | 21.0  | 525  | 0.4185          | 0.64     |
| 0.5392        | 22.0  | 550  | 0.4187          | 0.59     |
| 0.5392        | 23.0  | 575  | 0.4186          | 0.62     |
| 0.5392        | 24.0  | 600  | 0.4159          | 0.65     |
| 0.5392        | 25.0  | 625  | 0.4152          | 0.64     |
| 0.5392        | 26.0  | 650  | 0.4151          | 0.62     |
| 0.5392        | 27.0  | 675  | 0.4136          | 0.63     |
| 0.5392        | 28.0  | 700  | 0.4190          | 0.65     |
| 0.5392        | 29.0  | 725  | 0.4225          | 0.61     |
| 0.5392        | 30.0  | 750  | 0.4209          | 0.57     |
| 0.5392        | 31.0  | 775  | 0.4167          | 0.63     |
| 0.5392        | 32.0  | 800  | 0.4153          | 0.62     |
| 0.5392        | 33.0  | 825  | 0.4236          | 0.6      |
| 0.5392        | 34.0  | 850  | 0.4191          | 0.58     |
| 0.5392        | 35.0  | 875  | 0.4160          | 0.61     |
| 0.5392        | 36.0  | 900  | 0.4163          | 0.62     |
| 0.5392        | 37.0  | 925  | 0.4193          | 0.59     |
| 0.5392        | 38.0  | 950  | 0.4208          | 0.62     |
| 0.5392        | 39.0  | 975  | 0.4163          | 0.6      |
| 0.5359        | 40.0  | 1000 | 0.4159          | 0.6      |
| 0.5359        | 41.0  | 1025 | 0.4146          | 0.62     |
| 0.5359        | 42.0  | 1050 | 0.4158          | 0.6      |
| 0.5359        | 43.0  | 1075 | 0.4211          | 0.59     |
| 0.5359        | 44.0  | 1100 | 0.4203          | 0.59     |
| 0.5359        | 45.0  | 1125 | 0.4217          | 0.57     |
| 0.5359        | 46.0  | 1150 | 0.4183          | 0.6      |
| 0.5359        | 47.0  | 1175 | 0.4138          | 0.63     |
| 0.5359        | 48.0  | 1200 | 0.4124          | 0.63     |
| 0.5359        | 49.0  | 1225 | 0.4140          | 0.63     |
| 0.5359        | 50.0  | 1250 | 0.4118          | 0.64     |
| 0.5359        | 51.0  | 1275 | 0.4137          | 0.62     |
| 0.5359        | 52.0  | 1300 | 0.4113          | 0.63     |
| 0.5359        | 53.0  | 1325 | 0.4112          | 0.62     |
| 0.5359        | 54.0  | 1350 | 0.4140          | 0.63     |
| 0.5359        | 55.0  | 1375 | 0.4129          | 0.64     |
| 0.5359        | 56.0  | 1400 | 0.4151          | 0.64     |
| 0.5359        | 57.0  | 1425 | 0.4155          | 0.63     |
| 0.5359        | 58.0  | 1450 | 0.4140          | 0.63     |
| 0.5359        | 59.0  | 1475 | 0.4145          | 0.64     |
| 0.5347        | 60.0  | 1500 | 0.4158          | 0.63     |
| 0.5347        | 61.0  | 1525 | 0.4148          | 0.62     |
| 0.5347        | 62.0  | 1550 | 0.4147          | 0.6      |
| 0.5347        | 63.0  | 1575 | 0.4153          | 0.64     |
| 0.5347        | 64.0  | 1600 | 0.4156          | 0.63     |
| 0.5347        | 65.0  | 1625 | 0.4152          | 0.64     |
| 0.5347        | 66.0  | 1650 | 0.4146          | 0.64     |
| 0.5347        | 67.0  | 1675 | 0.4151          | 0.64     |
| 0.5347        | 68.0  | 1700 | 0.4145          | 0.61     |
| 0.5347        | 69.0  | 1725 | 0.4153          | 0.61     |
| 0.5347        | 70.0  | 1750 | 0.4147          | 0.64     |
| 0.5347        | 71.0  | 1775 | 0.4146          | 0.64     |
| 0.5347        | 72.0  | 1800 | 0.4134          | 0.62     |
| 0.5347        | 73.0  | 1825 | 0.4140          | 0.63     |
| 0.5347        | 74.0  | 1850 | 0.4141          | 0.64     |
| 0.5347        | 75.0  | 1875 | 0.4151          | 0.63     |
| 0.5347        | 76.0  | 1900 | 0.4150          | 0.62     |
| 0.5347        | 77.0  | 1925 | 0.4148          | 0.61     |
| 0.5347        | 78.0  | 1950 | 0.4149          | 0.62     |
| 0.5347        | 79.0  | 1975 | 0.4150          | 0.63     |
| 0.5285        | 80.0  | 2000 | 0.4150          | 0.63     |


### Framework versions

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