---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_essay
  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. -->

# bert_essay

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3763
- Mse: 0.3763
- Mae: 0.4747
- R2: 0.6434
- Accuracy: 0.2684

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mse    | Mae    | R2     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
| 0.6577        | 1.0   | 866  | 0.5250          | 0.5250 | 0.5685 | 0.5025 | 0.2674   |
| 0.3355        | 2.0   | 1732 | 0.4174          | 0.4174 | 0.5027 | 0.6045 | 0.2615   |
| 0.2592        | 3.0   | 2598 | 0.3763          | 0.3763 | 0.4747 | 0.6434 | 0.2684   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2