
<!-- 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. -->

# distilbert-finetuned-gesture-prediction-5-classes

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the validation set:
- Loss: 0.4607
- Accuracy: 0.8740
- Precision: 0.8732
- Recall: 0.8740
- F1: 0.8729

It achieves the following results on the test set:
- Loss: 0.4475
- Accuracy: 0.8555
- Precision: 0.8567
- Recall: 0.8555
- F1: 0.8550
  
## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

This model has been trained with the qfrodicio/gesture-prediction-5-classes dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- weight_decay: 0.01
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.413         | 1.0   | 71   | 0.6772          | 0.7934   | 0.8027    | 0.7934 | 0.7792 |
| 0.5503        | 2.0   | 142  | 0.4928          | 0.8659   | 0.8675    | 0.8659 | 0.8648 |
| 0.3564        | 3.0   | 213  | 0.4749          | 0.8669   | 0.8695    | 0.8669 | 0.8663 |
| 0.2513        | 4.0   | 284  | 0.4844          | 0.8604   | 0.8614    | 0.8604 | 0.8583 |
| 0.1807        | 5.0   | 355  | 0.4607          | 0.8740   | 0.8732    | 0.8740 | 0.8729 |
| 0.1288        | 6.0   | 426  | 0.4815          | 0.8701   | 0.8693    | 0.8701 | 0.8686 |
| 0.0968        | 7.0   | 497  | 0.5054          | 0.8690   | 0.8686    | 0.8690 | 0.8677 |
| 0.0714        | 8.0   | 568  | 0.4949          | 0.8872   | 0.8873    | 0.8872 | 0.8869 |
| 0.0583        | 9.0   | 639  | 0.5058          | 0.8825   | 0.8820    | 0.8825 | 0.8817 |
| 0.0479        | 10.0  | 710  | 0.5081          | 0.8846   | 0.8841    | 0.8846 | 0.8840 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": "qfrodicio/gesture-prediction-5-classes", "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "distilbert-finetuned-gesture-prediction-5-classes", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer dataset:qfrodicio/gesture-prediction-5-classes license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-5-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-finetuned-gesture-prediction-5-classes
=================================================


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


* Loss: 0.4607
* Accuracy: 0.8740
* Precision: 0.8732
* Recall: 0.8740
* F1: 0.8729


It achieves the following results on the test set:


* Loss: 0.4475
* Accuracy: 0.8555
* Precision: 0.8567
* Recall: 0.8555
* F1: 0.8550


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


More information needed


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


More information needed


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


This model has been trained with the qfrodicio/gesture-prediction-5-classes dataset


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* weight\_decay: 0.01
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* weight\_decay: 0.01
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-5-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* weight\_decay: 0.01
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-5-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* weight\_decay: 0.01
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2