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# distilbert-base-uncased-finetuned-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1995
- Accuracy: 0.9365
- F1: 0.9371

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.475         | 1.0   | 503  | 0.2171          | 0.928    | 0.9292 |
| 0.1235        | 2.0   | 1006 | 0.1764          | 0.9365   | 0.9372 |
| 0.0802        | 3.0   | 1509 | 0.1788          | 0.938    | 0.9388 |
| 0.0531        | 4.0   | 2012 | 0.2005          | 0.938    | 0.9388 |
| 0.0367        | 5.0   | 2515 | 0.1995          | 0.9365   | 0.9371 |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "bertweet-base-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9365, "name": "Accuracy"}, {"type": "f1", "value": 0.9371, "name": "F1"}]}, {"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.923, "name": "Accuracy", "verified": true}, {"type": "precision", "value": 0.8676576686813523, "name": "Precision Macro", "verified": true}, {"type": "precision", "value": 0.923, "name": "Precision Micro", "verified": true}, {"type": "precision", "value": 0.9268406401714973, "name": "Precision Weighted", "verified": true}, {"type": "recall", "value": 0.8945488803260702, "name": "Recall Macro", "verified": true}, {"type": "recall", "value": 0.923, "name": "Recall Micro", "verified": true}, {"type": "recall", "value": 0.923, "name": "Recall Weighted", "verified": true}, {"type": "f1", "value": 0.8798961895301041, "name": "F1 Macro", "verified": true}, {"type": "f1", "value": 0.923, "name": "F1 Micro", "verified": true}, {"type": "f1", "value": 0.9241278880972197, "name": "F1 Weighted", "verified": true}, {"type": "loss", "value": 0.24626904726028442, "name": "loss", "verified": true}]}]}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer dataset:emotion model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-uncased-finetuned-emotion
=========================================


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1995
* Accuracy: 0.9365
* F1: 0.9371


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: 5


### Training results



### Framework versions


* Transformers 4.13.0
* Pytorch 1.11.0+cu113
* Datasets 1.16.1
* 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 1.11.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #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 1.11.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #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 1.11.0+cu113
* Datasets 1.16.1
* Tokenizers 0.10.3