---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: kaggle-math
  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. -->

# kaggle-math

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6024
- Accuracy: 0.8204
- F1: 0.8204
- Precision: 0.8204
- Recall: 0.8204

## 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: 5e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6125        | 1.0   | 680  | 0.8193          | 0.7782   | 0.7782 | 0.7782    | 0.7782 |
| 0.5557        | 2.0   | 1360 | 0.6190          | 0.7998   | 0.7998 | 0.7998    | 0.7998 |
| 0.4949        | 3.0   | 2040 | 0.6024          | 0.8204   | 0.8204 | 0.8204    | 0.8204 |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.5.1+cu118
- Datasets 3.2.0
- Tokenizers 0.15.2