
<!-- 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. -->

# bert-finetuned-gesture-prediction-21-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: 0.8664
- Accuracy: 0.8123
- Precision: 0.8122
- Recall: 0.8123
- F1: 0.8048

It achieves the following results on the test set:
- Loss: 0.8381
- Accuracy: 0.7884
- Precision: 0.7954
- Recall: 0.7884
- F1: 0.7827

## 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-21-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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.225         | 1.0   | 104  | 1.3314          | 0.7115   | 0.6469    | 0.7115 | 0.6675 |
| 1.0881        | 2.0   | 208  | 0.9569          | 0.7750   | 0.7577    | 0.7750 | 0.7525 |
| 0.7006        | 3.0   | 312  | 0.8805          | 0.7959   | 0.7917    | 0.7959 | 0.7831 |
| 0.4943        | 4.0   | 416  | 0.8664          | 0.8123   | 0.8122    | 0.8123 | 0.8048 |
| 0.3372        | 5.0   | 520  | 0.8765          | 0.8130   | 0.8102    | 0.8130 | 0.8053 |
| 0.2416        | 6.0   | 624  | 0.8772          | 0.8166   | 0.8139    | 0.8166 | 0.8107 |
| 0.178         | 7.0   | 728  | 0.9186          | 0.8217   | 0.8186    | 0.8217 | 0.8167 |
| 0.1302        | 8.0   | 832  | 0.9186          | 0.8202   | 0.8183    | 0.8202 | 0.8165 |
| 0.1063        | 9.0   | 936  | 0.9618          | 0.8245   | 0.8213    | 0.8245 | 0.8198 |
| 0.094         | 10.0  | 1040 | 0.9660          | 0.8214   | 0.8184    | 0.8214 | 0.8166 |


### 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-21-classes", "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "bert-finetuned-gesture-prediction-21-classes", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:qfrodicio/gesture-prediction-21-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-21-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

bert-finetuned-gesture-prediction-21-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: 0.8664
* Accuracy: 0.8123
* Precision: 0.8122
* Recall: 0.8123
* F1: 0.8048


It achieves the following results on the test set:


* Loss: 0.8381
* Accuracy: 0.7884
* Precision: 0.7954
* Recall: 0.7884
* F1: 0.7827


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-21-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-21-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-21-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