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# roberta-base-finetuned-emotion

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1669
- Accuracy: 0.94
- F1: 0.9404

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure
The model has trained twice. In the first run hyperparameters was the same except num_epochs was 3.
So results below actually shows 8 epoch of fine-tuning in total.
### 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1508        | 1.0   | 250  | 0.1969          | 0.934    | 0.9334 |
| 0.1035        | 2.0   | 500  | 0.1660          | 0.9335   | 0.9341 |
| 0.0926        | 3.0   | 750  | 0.1626          | 0.935    | 0.9359 |
| 0.0855        | 4.0   | 1000 | 0.1680          | 0.934    | 0.9337 |
| 0.0682        | 5.0   | 1250 | 0.1669          | 0.94     | 0.9404 |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roberta-base-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.94, "name": "Accuracy"}, {"type": "f1", "value": 0.9404039265121519, "name": "F1"}]}]}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer dataset:emotion license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-emotion #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-finetuned-emotion
==============================


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


* Loss: 0.1669
* Accuracy: 0.94
* F1: 0.9404


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


More information needed


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


More information needed


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


More information needed


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


The model has trained twice. In the first run hyperparameters was the same except num\_epochs was 3.
So results below actually shows 8 epoch of fine-tuning in total.


### 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: 5


### Training results



### Framework versions


* Transformers 4.13.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.10.3



### 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: 5 ### Training results ### Framework versions


* Transformers 4.13.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-emotion #license-mit #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: 5 ### Training results ### Framework versions


* Transformers 4.13.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-emotion #license-mit #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: 5### Training results### Framework versions


* Transformers 4.13.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.10.3