
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-base-uncased-finetuned-emotion

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3648
- Accuracy: 0.9405
- F1: 0.9404

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0177        | 1.0   | 250  | 0.3372          | 0.933    | 0.9331 |
| 0.0149        | 2.0   | 500  | 0.3434          | 0.9385   | 0.9386 |
| 0.012         | 3.0   | 750  | 0.3878          | 0.9355   | 0.9353 |
| 0.0135        | 4.0   | 1000 | 0.3981          | 0.938    | 0.9371 |
| 0.0088        | 5.0   | 1250 | 0.3695          | 0.94     | 0.9400 |
| 0.0112        | 6.0   | 1500 | 0.4133          | 0.933    | 0.9334 |
| 0.0105        | 7.0   | 1750 | 0.3733          | 0.937    | 0.9370 |
| 0.0117        | 8.0   | 2000 | 0.3625          | 0.938    | 0.9381 |
| 0.0126        | 9.0   | 2250 | 0.3539          | 0.9405   | 0.9405 |
| 0.0095        | 10.0  | 2500 | 0.3963          | 0.9315   | 0.9318 |
| 0.0088        | 11.0  | 2750 | 0.3692          | 0.9355   | 0.9353 |
| 0.0072        | 12.0  | 3000 | 0.3646          | 0.9385   | 0.9385 |
| 0.0064        | 13.0  | 3250 | 0.3630          | 0.9375   | 0.9373 |
| 0.0052        | 14.0  | 3500 | 0.3659          | 0.9405   | 0.9403 |
| 0.005         | 15.0  | 3750 | 0.3648          | 0.9405   | 0.9404 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9405, "name": "Accuracy"}, {"type": "f1", "value": 0.9404154624819866, "name": "F1"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:emotion license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-uncased-finetuned-emotion
===================================


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


* Loss: 0.3648
* Accuracy: 0.9405
* F1: 0.9404


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 15 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3