
<!-- 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. -->

# roberta-finetuned-gesture-prediction-9-classes

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the validation set:
- Loss: 0.6668
- Accuracy: 0.8289
- Precision: 0.8288
- Recall: 0.8289
- F1: 0.8258

It achieves the following results on the test set:
- Loss: 0.6158
- Accuracy: 0.83
- Precision: 0.8296
- Recall: 0.83
- F1: 0.8274

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7138        | 1.0   | 87   | 1.0975          | 0.6915   | 0.6290    | 0.6915 | 0.6303 |
| 0.846         | 2.0   | 174  | 0.7497          | 0.7948   | 0.7790    | 0.7948 | 0.7772 |
| 0.5545        | 3.0   | 261  | 0.7078          | 0.8020   | 0.8000    | 0.8020 | 0.7927 |
| 0.3955        | 4.0   | 348  | 0.6668          | 0.8289   | 0.8288    | 0.8289 | 0.8258 |
| 0.279         | 5.0   | 435  | 0.6922          | 0.8291   | 0.8340    | 0.8291 | 0.8277 |
| 0.2203        | 6.0   | 522  | 0.6955          | 0.8373   | 0.8390    | 0.8373 | 0.8336 |
| 0.1595        | 7.0   | 609  | 0.7149          | 0.8395   | 0.8405    | 0.8395 | 0.8365 |
| 0.1349        | 8.0   | 696  | 0.7065          | 0.8436   | 0.8447    | 0.8436 | 0.8399 |
| 0.1047        | 9.0   | 783  | 0.7408          | 0.8481   | 0.8502    | 0.8481 | 0.8445 |
| 0.0906        | 10.0  | 870  | 0.7439          | 0.8495   | 0.8501    | 0.8495 | 0.8465 |


### Framework versions

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

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

roberta-finetuned-gesture-prediction-9-classes
==============================================


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


* Loss: 0.6668
* Accuracy: 0.8289
* Precision: 0.8288
* Recall: 0.8289
* F1: 0.8258


It achieves the following results on the test set:


* Loss: 0.6158
* Accuracy: 0.83
* Precision: 0.8296
* Recall: 0.83
* F1: 0.8274


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 #roberta #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-9-classes #license-mit #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 #roberta #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-9-classes #license-mit #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