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# deberta-v3-xsmall-finetuned-DAGPap22

This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0798
- Accuracy: 0.9907
- F1: 0.9934

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4.5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 402  | 0.1626          | 0.9477   | 0.9616 |
| 0.4003        | 2.0   | 804  | 0.0586          | 0.9794   | 0.9853 |
| 0.1075        | 3.0   | 1206 | 0.0342          | 0.9907   | 0.9933 |
| 0.0581        | 4.0   | 1608 | 0.1140          | 0.9776   | 0.9838 |
| 0.0245        | 5.0   | 2010 | 0.1409          | 0.9776   | 0.9842 |
| 0.0245        | 6.0   | 2412 | 0.0732          | 0.9832   | 0.9881 |
| 0.0167        | 7.0   | 2814 | 0.1996          | 0.9682   | 0.9778 |
| 0.0139        | 8.0   | 3216 | 0.1219          | 0.9850   | 0.9894 |
| 0.006         | 9.0   | 3618 | 0.0670          | 0.9907   | 0.9934 |
| 0.0067        | 10.0  | 4020 | 0.1036          | 0.9869   | 0.9907 |
| 0.0067        | 11.0  | 4422 | 0.1220          | 0.9776   | 0.9838 |
| 0.0041        | 12.0  | 4824 | 0.1768          | 0.9776   | 0.9839 |
| 0.0007        | 13.0  | 5226 | 0.0943          | 0.9888   | 0.9920 |
| 0.0           | 14.0  | 5628 | 0.0959          | 0.9907   | 0.9934 |
| 0.0054        | 15.0  | 6030 | 0.0915          | 0.9888   | 0.9921 |
| 0.0054        | 16.0  | 6432 | 0.1618          | 0.9794   | 0.9855 |
| 0.0019        | 17.0  | 6834 | 0.0794          | 0.9907   | 0.9934 |
| 0.0           | 18.0  | 7236 | 0.0799          | 0.9907   | 0.9934 |
| 0.0           | 19.0  | 7638 | 0.0797          | 0.9907   | 0.9934 |
| 0.0           | 20.0  | 8040 | 0.0798          | 0.9907   | 0.9934 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-xsmall-finetuned-DAGPap22", "results": []}]}
transformers pytorch tensorboard deberta-v2 text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

deberta-v3-xsmall-finetuned-DAGPap22
====================================


This model is a fine-tuned version of microsoft/deberta-v3-xsmall on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0798
* Accuracy: 0.9907
* F1: 0.9934


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


More information needed


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


More information needed


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


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.5e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.5e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.5e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 4.5e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1