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# finetuned_bert-base-on-IEMOCAP_1

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: 1.3271
- Accuracy: 0.6516

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2909        | 1.0   | 112  | 1.2207          | 0.5314   |
| 0.9328        | 2.0   | 224  | 0.9234          | 0.6446   |
| 0.7572        | 3.0   | 336  | 0.7751          | 0.7040   |
| 0.5686        | 4.0   | 448  | 0.7708          | 0.7175   |
| 0.3796        | 5.0   | 560  | 0.8258          | 0.7130   |
| 0.2736        | 6.0   | 672  | 0.8620          | 0.7231   |
| 0.2182        | 7.0   | 784  | 0.8939          | 0.7231   |
| 0.1755        | 8.0   | 896  | 1.0788          | 0.7220   |
| 0.1514        | 9.0   | 1008 | 1.0029          | 0.7365   |
| 0.1665        | 10.0  | 1120 | 1.0819          | 0.7152   |
| 0.2008        | 11.0  | 1232 | 1.1361          | 0.7152   |
| 0.0925        | 12.0  | 1344 | 1.1105          | 0.7253   |
| 0.162         | 13.0  | 1456 | 1.1379          | 0.7197   |
| 0.1302        | 14.0  | 1568 | 1.2054          | 0.7209   |
| 0.0701        | 15.0  | 1680 | 1.2240          | 0.7343   |
| 0.1392        | 16.0  | 1792 | 1.2842          | 0.7175   |
| 0.1419        | 17.0  | 1904 | 1.2614          | 0.7242   |
| 0.1125        | 18.0  | 2016 | 1.2826          | 0.7209   |
| 0.1128        | 19.0  | 2128 | 1.3033          | 0.7209   |
| 0.0813        | 20.0  | 2240 | 1.3068          | 0.7220   |


### 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_1", "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\_1
==================================


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: 1.3271
* Accuracy: 0.6516


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