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# finetuned_bert-base-on-IEMOCAP_3

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: 1.0342
- Accuracy: 0.6490

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2553        | 1.0   | 110  | 1.2256          | 0.4636   |
| 0.8935        | 2.0   | 220  | 0.9211          | 0.6355   |
| 0.7189        | 3.0   | 330  | 0.7761          | 0.7107   |
| 0.5676        | 4.0   | 440  | 0.8031          | 0.7210   |
| 0.3598        | 5.0   | 550  | 0.8454          | 0.7301   |
| 0.2774        | 6.0   | 660  | 0.8871          | 0.7153   |
| 0.2699        | 7.0   | 770  | 0.9372          | 0.7175   |
| 0.1963        | 8.0   | 880  | 0.9686          | 0.7244   |
| 0.2565        | 9.0   | 990  | 1.0099          | 0.7016   |
| 0.1849        | 10.0  | 1100 | 1.0425          | 0.7175   |
| 0.1769        | 11.0  | 1210 | 1.1145          | 0.7118   |
| 0.1727        | 12.0  | 1320 | 1.1316          | 0.7187   |
| 0.1132        | 13.0  | 1430 | 1.0963          | 0.7244   |
| 0.1314        | 14.0  | 1540 | 1.1908          | 0.7118   |
| 0.0833        | 15.0  | 1650 | 1.2133          | 0.7164   |
| 0.1378        | 16.0  | 1760 | 1.1971          | 0.7210   |
| 0.1067        | 17.0  | 1870 | 1.2330          | 0.7118   |
| 0.0539        | 18.0  | 1980 | 1.2246          | 0.7198   |
| 0.0718        | 19.0  | 2090 | 1.2364          | 0.7164   |
| 0.1036        | 20.0  | 2200 | 1.2446          | 0.7164   |


### 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_3", "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\_3
==================================


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: 1.0342
* Accuracy: 0.6490


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