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# Regression_bert_2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8766
- Mse: 3.8766
- Mae: 1.3858
- R2: -1.0002
- Accuracy: 0.5714

## 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: 2e-05
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    | R2      | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 1    | 3.5920          | 3.5920 | 1.5846 | -2.3703 | 0.2857   |
| No log        | 2.0   | 2    | 3.3981          | 3.3981 | 1.5187 | -2.1884 | 0.2857   |
| No log        | 3.0   | 3    | 3.2200          | 3.2200 | 1.4559 | -2.0213 | 0.2857   |
| No log        | 4.0   | 4    | 3.0496          | 3.0496 | 1.3924 | -1.8614 | 0.4286   |
| No log        | 5.0   | 5    | 2.8847          | 2.8847 | 1.3402 | -1.7067 | 0.4286   |
| No log        | 6.0   | 6    | 2.7261          | 2.7261 | 1.2955 | -1.5579 | 0.4286   |
| No log        | 7.0   | 7    | 2.5772          | 2.5772 | 1.2590 | -1.4182 | 0.4286   |
| No log        | 8.0   | 8    | 2.4361          | 2.4361 | 1.2302 | -1.2858 | 0.4286   |
| No log        | 9.0   | 9    | 2.3055          | 2.3055 | 1.2027 | -1.1632 | 0.4286   |
| No log        | 10.0  | 10   | 2.1844          | 2.1844 | 1.1765 | -1.0496 | 0.4286   |
| No log        | 11.0  | 11   | 2.0725          | 2.0725 | 1.1546 | -0.9446 | 0.4286   |
| No log        | 12.0  | 12   | 1.9723          | 1.9723 | 1.1457 | -0.8506 | 0.4286   |
| No log        | 13.0  | 13   | 1.8851          | 1.8851 | 1.1381 | -0.7688 | 0.4286   |
| No log        | 14.0  | 14   | 1.8103          | 1.8103 | 1.1315 | -0.6985 | 0.2857   |
| No log        | 15.0  | 15   | 1.7472          | 1.7472 | 1.1258 | -0.6394 | 0.2857   |
| No log        | 16.0  | 16   | 1.6959          | 1.6959 | 1.1211 | -0.5912 | 0.2857   |
| No log        | 17.0  | 17   | 1.6558          | 1.6558 | 1.1174 | -0.5536 | 0.2857   |
| No log        | 18.0  | 18   | 1.6262          | 1.6262 | 1.1146 | -0.5259 | 0.2857   |
| No log        | 19.0  | 19   | 1.6067          | 1.6067 | 1.1128 | -0.5076 | 0.2857   |
| No log        | 20.0  | 20   | 1.5970          | 1.5970 | 1.1118 | -0.4984 | 0.2857   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_bert_2", "results": []}]}
transformers pytorch tensorboard distilbert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Regression\_bert\_2
===================


This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 3.8766
* Mse: 3.8766
* Mae: 1.3858
* R2: -1.0002
* Accuracy: 0.5714


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: 2e-05
* 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: 20


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 20### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2