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# outputs

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0041
- Pearson: 0.9845

## Model description

O modelo verifica se a mensagem é spam o não. Caso o valor seja maior ou igual a 0.6 ele é spam, caso seja menor ele não é spam.

Aqui temos algumas mensagens do dataframe de teste:
- Send a logo 2 ur lover - 2 names joined by a heart. Txt LOVE NAME1 NAME2 MOBNO eg LOVE ADAM EVE 07123456789 to 87077 Yahoo! POBox36504W45WQ TxtNO 4 no ads 150p | Spam
- Not directly behind... Abt 4 rows behind ü... | Non-Spam

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Pearson |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log        | 1.0   | 24   | 0.0338          | 0.9274  |
| No log        | 2.0   | 48   | 0.0070          | 0.9667  |
| No log        | 3.0   | 72   | 0.0110          | 0.9504  |
| No log        | 4.0   | 96   | 0.0078          | 0.9634  |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "outputs", "results": []}]}
transformers pytorch deberta-v2 text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

outputs
=======


This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0041
* Pearson: 0.9845


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


O modelo verifica se a mensagem é spam o não. Caso o valor seja maior ou igual a 0.6 ele é spam, caso seja menor ele não é spam.


Aqui temos algumas mensagens do dataframe de teste:


* Send a logo 2 ur lover - 2 names joined by a heart. Txt LOVE NAME1 NAME2 MOBNO eg LOVE ADAM EVE 07123456789 to 87077 Yahoo! POBox36504W45WQ TxtNO 4 no ads 150p | Spam
* Not directly behind... Abt 4 rows behind ü... | Non-Spam


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 8e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 8e-05
* train\_batch\_size: 32
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.1.0
* Tokenizers 0.13.3