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# distilbert-finetuned-gesture-prediction-21-classes

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8430
- Accuracy: 0.8077
- Precision: 0.8063
- Recall: 0.8077
- F1: 0.8038

It achieves the following results on the test set:
- Loss: 0.8332
- Accuracy: 0.7934
- Precision: 0.7925
- Recall: 0.7934
- F1: 0.7875


## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

This 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.2082        | 1.0   | 104  | 1.3318          | 0.6956   | 0.6361    | 0.6956 | 0.6473 |
| 1.1512        | 2.0   | 208  | 1.0114          | 0.7604   | 0.7463    | 0.7604 | 0.7368 |
| 0.8152        | 3.0   | 312  | 0.8805          | 0.7860   | 0.7677    | 0.7860 | 0.7698 |
| 0.6142        | 4.0   | 416  | 0.8486          | 0.8025   | 0.8035    | 0.8025 | 0.7961 |
| 0.4726        | 5.0   | 520  | 0.8651          | 0.7992   | 0.7987    | 0.7992 | 0.7894 |
| 0.3677        | 6.0   | 624  | 0.8430          | 0.8077   | 0.8063    | 0.8077 | 0.8038 |
| 0.2967        | 7.0   | 728  | 0.8564          | 0.8037   | 0.8029    | 0.8037 | 0.7995 |
| 0.2494        | 8.0   | 832  | 0.8567          | 0.8077   | 0.8054    | 0.8077 | 0.8041 |
| 0.2163        | 9.0   | 936  | 0.8789          | 0.8075   | 0.8060    | 0.8075 | 0.8035 |
| 0.193         | 10.0  | 1040 | 0.8880          | 0.8077   | 0.8072    | 0.8077 | 0.8032 |


### 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": "distilbert-finetuned-gesture-prediction-21-classes", "results": []}]}
transformers pytorch tensorboard distilbert 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 #distilbert #token-classification #generated_from_trainer #dataset-qfrodicio/gesture-prediction-21-classes #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-finetuned-gesture-prediction-21-classes
==================================================


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


* Loss: 0.8430
* Accuracy: 0.8077
* Precision: 0.8063
* Recall: 0.8077
* F1: 0.8038


It achieves the following results on the test set:


* Loss: 0.8332
* Accuracy: 0.7934
* Precision: 0.7925
* Recall: 0.7934
* F1: 0.7875


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


More information needed


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


More information needed


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


This 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 #distilbert #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 #distilbert #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