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# finetuned_bert-base-on-IEMOCAP_2

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: 1.3186
- Accuracy: 0.7410

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3127        | 1.0   | 113  | 1.2620          | 0.4192   |
| 0.957         | 2.0   | 226  | 0.9294          | 0.6095   |
| 0.6409        | 3.0   | 339  | 0.8338          | 0.6969   |
| 0.5966        | 4.0   | 452  | 0.8386          | 0.6958   |
| 0.3725        | 5.0   | 565  | 0.8635          | 0.7035   |
| 0.3556        | 6.0   | 678  | 0.9218          | 0.7069   |
| 0.2466        | 7.0   | 791  | 0.9679          | 0.6914   |
| 0.1737        | 8.0   | 904  | 1.1180          | 0.6869   |
| 0.1694        | 9.0   | 1017 | 1.1068          | 0.6947   |
| 0.1843        | 10.0  | 1130 | 1.1409          | 0.6903   |
| 0.1747        | 11.0  | 1243 | 1.1143          | 0.7080   |
| 0.1387        | 12.0  | 1356 | 1.2127          | 0.6991   |
| 0.1524        | 13.0  | 1469 | 1.2309          | 0.7069   |
| 0.1113        | 14.0  | 1582 | 1.2382          | 0.7113   |
| 0.1278        | 15.0  | 1695 | 1.3048          | 0.7124   |
| 0.1214        | 16.0  | 1808 | 1.3267          | 0.7146   |
| 0.1965        | 17.0  | 1921 | 1.3726          | 0.7013   |
| 0.1227        | 18.0  | 2034 | 1.3371          | 0.7168   |
| 0.1014        | 19.0  | 2147 | 1.3508          | 0.7124   |
| 0.1028        | 20.0  | 2260 | 1.3585          | 0.7080   |


### 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_2", "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\_2
==================================


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: 1.3186
* Accuracy: 0.7410


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