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# finetuned_bert-base-on-IEMOCAP_5

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3146
- Accuracy: 0.6624

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3037        | 1.0   | 108  | 1.2852          | 0.4349   |
| 1.0277        | 2.0   | 216  | 0.9733          | 0.5942   |
| 0.7985        | 3.0   | 324  | 0.8110          | 0.6895   |
| 0.5244        | 4.0   | 432  | 0.7911          | 0.6919   |
| 0.3289        | 5.0   | 540  | 0.8518          | 0.7012   |
| 0.3281        | 6.0   | 648  | 0.9236          | 0.6884   |
| 0.2689        | 7.0   | 756  | 0.9498          | 0.7058   |
| 0.2162        | 8.0   | 864  | 1.0157          | 0.7023   |
| 0.1811        | 9.0   | 972  | 1.0196          | 0.7151   |
| 0.2283        | 10.0  | 1080 | 1.0987          | 0.7209   |
| 0.1449        | 11.0  | 1188 | 1.1462          | 0.7186   |
| 0.1614        | 12.0  | 1296 | 1.1972          | 0.7116   |
| 0.1589        | 13.0  | 1404 | 1.2174          | 0.7163   |
| 0.1314        | 14.0  | 1512 | 1.2388          | 0.7116   |
| 0.1587        | 15.0  | 1620 | 1.2722          | 0.7186   |
| 0.0928        | 16.0  | 1728 | 1.3090          | 0.7186   |
| 0.1394        | 17.0  | 1836 | 1.3286          | 0.7174   |
| 0.0958        | 18.0  | 1944 | 1.3321          | 0.7209   |
| 0.0862        | 19.0  | 2052 | 1.3439          | 0.7128   |
| 0.1041        | 20.0  | 2160 | 1.3473          | 0.7151   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "finetuned_bert-base-on-IEMOCAP_5", "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 

finetuned\_bert-base-on-IEMOCAP\_5
==================================


This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.3146
* Accuracy: 0.6624


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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.30.1
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3