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# bert-finetuned-gesture-prediction-9-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: 0.6948 
- Accuracy: 0.8332 
- Precision: 0.8352
- Recall: 0.8332
- F1: 0.8311

It achieves the following results on the test set:
- Loss: 0.6337
- Accuracy: 0.8297 
- Precision: 0.8365
- Recall: 0.8297
- F1: 0.8281

## 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-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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.6408        | 1.0   | 87   | 1.0168          | 0.7110   | 0.6825    | 0.7110 | 0.6559 |
| 0.7629        | 2.0   | 174  | 0.7777          | 0.7977   | 0.7863    | 0.7977 | 0.7856 |
| 0.4526        | 3.0   | 261  | 0.6951          | 0.8263   | 0.8276    | 0.8263 | 0.8199 |
| 0.285         | 4.0   | 348  | 0.6948          | 0.8332   | 0.8352    | 0.8332 | 0.8311 |
| 0.1788        | 5.0   | 435  | 0.7196          | 0.8277   | 0.8296    | 0.8277 | 0.8260 |
| 0.1246        | 6.0   | 522  | 0.7677          | 0.8314   | 0.8357    | 0.8314 | 0.8284 |
| 0.0866        | 7.0   | 609  | 0.7865          | 0.8407   | 0.8433    | 0.8407 | 0.8391 |
| 0.0629        | 8.0   | 696  | 0.8168          | 0.8435   | 0.8457    | 0.8435 | 0.8420 |
| 0.0489        | 9.0   | 783  | 0.8292          | 0.8417   | 0.8439    | 0.8417 | 0.8395 |
| 0.0398        | 10.0  | 870  | 0.8391          | 0.8443   | 0.8461    | 0.8443 | 0.8422 |


### 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": "bert-finetuned-gesture-prediction-9-classes", "results": []}]}
transformers pytorch tensorboard bert 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 #bert #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-9-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-gesture-prediction-9-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: 0.6948
* Accuracy: 0.8332
* Precision: 0.8352
* Recall: 0.8332
* F1: 0.8311


It achieves the following results on the test set:


* Loss: 0.6337
* Accuracy: 0.8297
* Precision: 0.8365
* Recall: 0.8297
* F1: 0.8281


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-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 #bert #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 #bert #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