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# BERT-Emotion detection model 
____
## ⚙️ Model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [Emotion Dataset from Kaggle](https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp).
It achieves the following results on the test set after being trained and evaluated with the Trainer interface:
- Loss: 0.2362
- Accuracy: 0.923
- F1: 0.9226
- Precision: 0.9226
- Recall: 0.923

### 💡How to: Inference API
Mapping of labels: 
* 'anger': 0, 'fear': 1, 'joy': 2, 'love': 3, 'sadness': 4, 'surprise': 5

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.5691        | 1.0   | 250  | 1.2681          | 0.564    | 0.4477 | 0.3868    | 0.564  |
| 0.9132        | 2.0   | 500  | 0.4917          | 0.8465   | 0.8349 | 0.8508    | 0.8465 |
| 0.3131        | 3.0   | 750  | 0.2362          | 0.923    | 0.9226 | 0.9226    | 0.923  |

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Training and evaluation data can be found on the same link as in the model description

## Training procedure


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "models", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

BERT-Emotion detection model
============================




---


️ Model
-------


This model is a fine-tuned version of bert-base-uncased on the Emotion Dataset from Kaggle.
It achieves the following results on the test set after being trained and evaluated with the Trainer interface:


* Loss: 0.2362
* Accuracy: 0.923
* F1: 0.9226
* Precision: 0.9226
* Recall: 0.923


### How to: Inference API


Mapping of labels:


* 'anger': 0, 'fear': 1, 'joy': 2, 'love': 3, 'sadness': 4, 'surprise': 5


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3


### Training results



Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


Training and evaluation data can be found on the same link as in the model description


Training procedure
------------------


### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2



### How to: Inference API


Mapping of labels:


* 'anger': 0, 'fear': 1, 'joy': 2, 'love': 3, 'sadness': 4, 'surprise': 5 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3 ### Training results



Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


Training and evaluation data can be found on the same link as in the model description


Training procedure
------------------ ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### How to: Inference API


Mapping of labels:


* 'anger': 0, 'fear': 1, 'joy': 2, 'love': 3, 'sadness': 4, 'surprise': 5 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3 ### Training results



Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


Training and evaluation data can be found on the same link as in the model description


Training procedure
------------------ ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### How to: Inference API


Mapping of labels:


* 'anger': 0, 'fear': 1, 'joy': 2, 'love': 3, 'sadness': 4, 'surprise': 5### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3### Training results



Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


Training and evaluation data can be found on the same link as in the model description


Training procedure
------------------### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Tokenizers 0.13.2