---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Regression_BERT_NOaug_MSEloss
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Regression_BERT_NOaug_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: 0.4928
- Mse: 0.4928
- Mae: 0.6337
- R2: 0.0926
- Accuracy: 0.4737

## 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   | 33   | 0.3184          | 0.3184 | 0.5205 | 0.0487 | 0.5946   |
| No log        | 2.0   | 66   | 0.2439          | 0.2439 | 0.3571 | 0.2712 | 0.7027   |
| No log        | 3.0   | 99   | 0.2950          | 0.2950 | 0.3792 | 0.1185 | 0.6757   |
| No log        | 4.0   | 132  | 0.3179          | 0.3179 | 0.4267 | 0.0503 | 0.6757   |
| No log        | 5.0   | 165  | 0.2869          | 0.2869 | 0.3984 | 0.1426 | 0.6757   |
| No log        | 6.0   | 198  | 0.2967          | 0.2967 | 0.3688 | 0.1134 | 0.7027   |
| No log        | 7.0   | 231  | 0.2797          | 0.2797 | 0.3599 | 0.1643 | 0.7027   |
| No log        | 8.0   | 264  | 0.2730          | 0.2730 | 0.3438 | 0.1844 | 0.7027   |
| No log        | 9.0   | 297  | 0.2813          | 0.2813 | 0.3623 | 0.1596 | 0.7027   |
| No log        | 10.0  | 330  | 0.2733          | 0.2733 | 0.3296 | 0.1835 | 0.7027   |
| No log        | 11.0  | 363  | 0.2770          | 0.2770 | 0.3432 | 0.1725 | 0.7027   |
| No log        | 12.0  | 396  | 0.3009          | 0.3009 | 0.3574 | 0.1010 | 0.6757   |
| No log        | 13.0  | 429  | 0.2735          | 0.2735 | 0.3318 | 0.1827 | 0.7027   |
| No log        | 14.0  | 462  | 0.2787          | 0.2787 | 0.3341 | 0.1672 | 0.7027   |
| No log        | 15.0  | 495  | 0.2790          | 0.2790 | 0.3312 | 0.1663 | 0.7297   |
| 0.0804        | 16.0  | 528  | 0.2683          | 0.2683 | 0.3229 | 0.1984 | 0.7027   |
| 0.0804        | 17.0  | 561  | 0.2749          | 0.2749 | 0.3273 | 0.1785 | 0.7297   |
| 0.0804        | 18.0  | 594  | 0.2709          | 0.2709 | 0.3202 | 0.1906 | 0.7297   |
| 0.0804        | 19.0  | 627  | 0.2711          | 0.2711 | 0.3205 | 0.1901 | 0.7297   |
| 0.0804        | 20.0  | 660  | 0.2694          | 0.2694 | 0.3197 | 0.1950 | 0.7297   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3