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# bert-base-uncased-finetuned-iemocap8

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8968
- Accuracy: 0.6654
- F1: 0.6723

## 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: 4.319412088241492e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 51   | 1.0531          | 0.5597   | 0.5655 |
| 1.0284        | 2.0   | 102  | 0.9370          | 0.6227   | 0.6304 |
| 1.0284        | 3.0   | 153  | 0.8796          | 0.6722   | 0.6765 |
| 0.4432        | 4.0   | 204  | 0.9785          | 0.6654   | 0.6727 |
| 0.4432        | 5.0   | 255  | 1.0664          | 0.6586   | 0.6634 |
| 0.2492        | 6.0   | 306  | 1.1291          | 0.6499   | 0.6606 |
| 0.2492        | 7.0   | 357  | 1.1847          | 0.6702   | 0.6777 |
| 0.1707        | 8.0   | 408  | 1.4084          | 0.6508   | 0.6534 |
| 0.1707        | 9.0   | 459  | 1.3468          | 0.6702   | 0.6762 |
| 0.1461        | 10.0  | 510  | 1.4245          | 0.6634   | 0.6710 |
| 0.1461        | 11.0  | 561  | 1.4865          | 0.6499   | 0.6600 |
| 0.1262        | 12.0  | 612  | 1.4616          | 0.6576   | 0.6656 |
| 0.1262        | 13.0  | 663  | 1.5335          | 0.6663   | 0.6711 |
| 0.1203        | 14.0  | 714  | 1.4855          | 0.6731   | 0.6806 |
| 0.1203        | 15.0  | 765  | 1.5825          | 0.6712   | 0.6792 |
| 0.1023        | 16.0  | 816  | 1.7145          | 0.6731   | 0.6794 |
| 0.1023        | 17.0  | 867  | 1.6676          | 0.6751   | 0.6823 |
| 0.0976        | 18.0  | 918  | 1.8013          | 0.6693   | 0.6719 |
| 0.0976        | 19.0  | 969  | 1.7192          | 0.6673   | 0.6755 |
| 0.0937        | 20.0  | 1020 | 1.7837          | 0.6654   | 0.6731 |
| 0.0937        | 21.0  | 1071 | 1.7779          | 0.6760   | 0.6831 |
| 0.0901        | 22.0  | 1122 | 1.8352          | 0.6615   | 0.6687 |
| 0.0901        | 23.0  | 1173 | 1.8601          | 0.6596   | 0.6656 |
| 0.0844        | 24.0  | 1224 | 1.9129          | 0.6625   | 0.6719 |
| 0.0844        | 25.0  | 1275 | 1.8507          | 0.6731   | 0.6784 |
| 0.0829        | 26.0  | 1326 | 1.8582          | 0.6673   | 0.6735 |
| 0.0829        | 27.0  | 1377 | 1.8670          | 0.6770   | 0.6825 |
| 0.0839        | 28.0  | 1428 | 1.8763          | 0.6741   | 0.6800 |
| 0.0839        | 29.0  | 1479 | 1.8925          | 0.6702   | 0.6769 |
| 0.0802        | 30.0  | 1530 | 1.8968          | 0.6654   | 0.6723 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-iemocap8", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-finetuned-iemocap8
====================================


This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.8968
* Accuracy: 0.6654
* F1: 0.6723


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: 4.319412088241492e-05
* train\_batch\_size: 64
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.319412088241492e-05
* train\_batch\_size: 64
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.319412088241492e-05
* train\_batch\_size: 64
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.319412088241492e-05
* train\_batch\_size: 64
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.0
* Tokenizers 0.13.2