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# Regression_BERT_aug_MSEloss

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: 0.1118
- Mse: 0.1118
- Mae: 0.2369
- R2: 0.7519
- Accuracy: 0.8733

## 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: 4
- eval_batch_size: 4
- 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   | 263  | 0.1491          | 0.1491 | 0.2707 | 0.6520 | 0.8367   |
| 0.1428        | 2.0   | 526  | 0.0948          | 0.0948 | 0.1805 | 0.7788 | 0.9033   |
| 0.1428        | 3.0   | 789  | 0.0596          | 0.0596 | 0.1209 | 0.8610 | 0.9533   |
| 0.0215        | 4.0   | 1052 | 0.0534          | 0.0534 | 0.1034 | 0.8755 | 0.9533   |
| 0.0215        | 5.0   | 1315 | 0.0464          | 0.0464 | 0.0882 | 0.8917 | 0.9567   |
| 0.0111        | 6.0   | 1578 | 0.0420          | 0.0420 | 0.0852 | 0.9019 | 0.9633   |
| 0.0111        | 7.0   | 1841 | 0.0419          | 0.0419 | 0.0744 | 0.9022 | 0.9633   |
| 0.0051        | 8.0   | 2104 | 0.0424          | 0.0424 | 0.0736 | 0.9010 | 0.96     |
| 0.0051        | 9.0   | 2367 | 0.0457          | 0.0457 | 0.0737 | 0.8935 | 0.9533   |
| 0.0034        | 10.0  | 2630 | 0.0396          | 0.0396 | 0.0692 | 0.9076 | 0.96     |
| 0.0034        | 11.0  | 2893 | 0.0419          | 0.0419 | 0.0740 | 0.9023 | 0.9633   |
| 0.0027        | 12.0  | 3156 | 0.0370          | 0.0370 | 0.0684 | 0.9136 | 0.9667   |
| 0.0027        | 13.0  | 3419 | 0.0389          | 0.0389 | 0.0688 | 0.9092 | 0.9633   |
| 0.0023        | 14.0  | 3682 | 0.0392          | 0.0392 | 0.0654 | 0.9085 | 0.9633   |
| 0.0023        | 15.0  | 3945 | 0.0382          | 0.0382 | 0.0663 | 0.9108 | 0.9633   |
| 0.0018        | 16.0  | 4208 | 0.0403          | 0.0403 | 0.0655 | 0.9059 | 0.96     |
| 0.0018        | 17.0  | 4471 | 0.0391          | 0.0391 | 0.0675 | 0.9087 | 0.96     |
| 0.0016        | 18.0  | 4734 | 0.0386          | 0.0386 | 0.0618 | 0.9099 | 0.9633   |
| 0.0016        | 19.0  | 4997 | 0.0389          | 0.0389 | 0.0640 | 0.9093 | 0.9633   |
| 0.0013        | 20.0  | 5260 | 0.0384          | 0.0384 | 0.0623 | 0.9104 | 0.9633   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Regression_BERT_aug_MSEloss", "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\_aug\_MSEloss
==============================


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: 0.1118
* Mse: 0.1118
* Mae: 0.2369
* R2: 0.7519
* Accuracy: 0.8733


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: 4
* eval\_batch\_size: 4
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 4
* eval\_batch\_size: 4
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
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: 4
* eval\_batch\_size: 4
* 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 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3