
<!-- 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-9-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: 0.6479
- Accuracy: 0.8214 
- Precision: 0.8230
- Recall: 0.8214
- F1: 0.8172

It achieves the following results on the test set:
- Loss: 0.6475
- Accuracy: 0.8144
- Precision: 0.8144
- Recall: 0.8144
- F1: 0.8095

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

The model has been trained on the qfrodicio/gesture-prediction-9-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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6636        | 1.0   | 87   | 0.9715          | 0.7270   | 0.6909    | 0.7270 | 0.6897 |
| 0.7503        | 2.0   | 174  | 0.7360          | 0.7987   | 0.7874    | 0.7987 | 0.7879 |
| 0.5283        | 3.0   | 261  | 0.6831          | 0.8056   | 0.8046    | 0.8056 | 0.8005 |
| 0.3853        | 4.0   | 348  | 0.6479          | 0.8214   | 0.8230    | 0.8214 | 0.8172 |
| 0.28          | 5.0   | 435  | 0.6570          | 0.8314   | 0.8348    | 0.8314 | 0.8289 |
| 0.2163        | 6.0   | 522  | 0.6887          | 0.8322   | 0.8346    | 0.8322 | 0.8298 |
| 0.158         | 7.0   | 609  | 0.7078          | 0.8336   | 0.8362    | 0.8336 | 0.8311 |
| 0.1308        | 8.0   | 696  | 0.7197          | 0.8415   | 0.8444    | 0.8415 | 0.8394 |
| 0.1061        | 9.0   | 783  | 0.7362          | 0.8419   | 0.8441    | 0.8419 | 0.8394 |
| 0.0947        | 10.0  | 870  | 0.7412          | 0.8435   | 0.8458    | 0.8435 | 0.8410 |


### 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-9-classes", "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "distilbert-finetuned-gesture-prediction-9-classes", "results": []}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer dataset:qfrodicio/gesture-prediction-9-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-9-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-finetuned-gesture-prediction-9-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: 0.6479
* Accuracy: 0.8214
* Precision: 0.8230
* Recall: 0.8214
* F1: 0.8172


It achieves the following results on the test set:


* Loss: 0.6475
* Accuracy: 0.8144
* Precision: 0.8144
* Recall: 0.8144
* F1: 0.8095


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


More information needed


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


More information needed


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


The model has been trained on the qfrodicio/gesture-prediction-9-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-9-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-9-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