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# bert-finetuned-gesture-prediction-5-classes

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the validation set:
- Loss: 0.4958 
- Accuracy: 0.8636
- Precision: 0.8662
- Recall: 0.8636
- F1: 0.8625

It achieves the following results on the test set:
- Loss: 0.4599 
- Accuracy: 0.8561
- Precision: 0.8578
- Recall: 0.8561
- F1: 0.8533

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

The 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3816        | 1.0   | 71   | 0.6929          | 0.7820   | 0.7518    | 0.7820 | 0.7510 |
| 0.546         | 2.0   | 142  | 0.5128          | 0.8591   | 0.8614    | 0.8591 | 0.8579 |
| 0.3026        | 3.0   | 213  | 0.4958          | 0.8636   | 0.8662    | 0.8636 | 0.8625 |
| 0.1689        | 4.0   | 284  | 0.5090          | 0.8688   | 0.8694    | 0.8688 | 0.8678 |
| 0.1085        | 5.0   | 355  | 0.5306          | 0.8794   | 0.8813    | 0.8794 | 0.8786 |
| 0.0684        | 6.0   | 426  | 0.5516          | 0.8776   | 0.8774    | 0.8776 | 0.8765 |
| 0.0485        | 7.0   | 497  | 0.6051          | 0.8779   | 0.8794    | 0.8779 | 0.8770 |
| 0.0341        | 8.0   | 568  | 0.6224          | 0.8781   | 0.8780    | 0.8781 | 0.8776 |
| 0.0251        | 9.0   | 639  | 0.6429          | 0.8812   | 0.8817    | 0.8812 | 0.8805 |
| 0.021         | 10.0  | 710  | 0.6456          | 0.8807   | 0.8811    | 0.8807 | 0.8798 |


### 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": "bert-finetuned-gesture-prediction-5-classes", "results": []}]}
transformers pytorch tensorboard bert 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 #bert #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-5-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-gesture-prediction-5-classes
===========================================


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


* Loss: 0.4958
* Accuracy: 0.8636
* Precision: 0.8662
* Recall: 0.8636
* F1: 0.8625


It achieves the following results on the test set:


* Loss: 0.4599
* Accuracy: 0.8561
* Precision: 0.8578
* Recall: 0.8561
* F1: 0.8533


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


More information needed


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


More information needed


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


The 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 #bert #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 #bert #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