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# ml-roberta-large-finetuned-gesture-prediction-21-classes

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the validation set:
- Loss: 0.7506
- Accuracy: 0.7927
- Precision: 0.7829
- Recall: 0.7927
- F1:  0.7837

It achieves the following results on the test set:
- Loss: 0.8029
- Accuracy: 0.7720
- Precision: 0.7764
- Recall: 0.7720
- F1:  0.7636

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

This model has been trained on 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.4441        | 1.0   | 104  | 1.5033          | 0.5798   | 0.5071    | 0.5798 | 0.5015 |
| 1.2049        | 2.0   | 208  | 0.8885          | 0.7532   | 0.7434    | 0.7532 | 0.7331 |
| 0.7329        | 3.0   | 312  | 0.7506          | 0.7927   | 0.7829    | 0.7927 | 0.7837 |
| 0.4949        | 4.0   | 416  | 0.7801          | 0.7936   | 0.7946    | 0.7936 | 0.7866 |
| 0.3221        | 5.0   | 520  | 0.8761          | 0.7957   | 0.7889    | 0.7957 | 0.7865 |
| 0.2112        | 6.0   | 624  | 0.9118          | 0.8062   | 0.8085    | 0.8062 | 0.8004 |
| 0.1458        | 7.0   | 728  | 0.9391          | 0.8071   | 0.8057    | 0.8071 | 0.8019 |
| 0.0988        | 8.0   | 832  | 0.9592          | 0.8105   | 0.8073    | 0.8105 | 0.8065 |
| 0.0685        | 9.0   | 936  | 1.0358          | 0.8057   | 0.8043    | 0.8057 | 0.8016 |
| 0.052         | 10.0  | 1040 | 1.0511          | 0.8089   | 0.8080    | 0.8089 | 0.8037 |


### 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"], "metrics": ["accuracy", "precision", "recall", "f1"], "model-index": [{"name": "ml-roberta-large-finetuned-gesture-prediction-21-classes", "results": []}]}
transformers pytorch tensorboard xlm-roberta token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

ml-roberta-large-finetuned-gesture-prediction-21-classes
========================================================


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


* Loss: 0.7506
* Accuracy: 0.7927
* Precision: 0.7829
* Recall: 0.7927
* F1: 0.7837


It achieves the following results on the test set:


* Loss: 0.8029
* Accuracy: 0.7720
* Precision: 0.7764
* Recall: 0.7720
* F1: 0.7636


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


More information needed


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


More information needed


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


This model has been trained on 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 #xlm-roberta #token-classification #generated_from_trainer #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 #xlm-roberta #token-classification #generated_from_trainer #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