---
license: mit
base_model: austin/Austin-MeDeBERTa
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fold_2
  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. -->

# fold_2

This model is a fine-tuned version of [austin/Austin-MeDeBERTa](https://huggingface.co/austin/Austin-MeDeBERTa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0090
- Precision: 0.7446
- Recall: 0.7718
- F1: 0.7580
- Accuracy: 0.9975

## 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: 5
- eval_batch_size: 5
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0403        | 1.0   | 635  | 0.0103          | 0.6230    | 0.6563 | 0.6392 | 0.9968   |
| 0.0097        | 2.0   | 1270 | 0.0082          | 0.7291    | 0.7352 | 0.7321 | 0.9974   |
| 0.0056        | 3.0   | 1905 | 0.0090          | 0.7446    | 0.7718 | 0.7580 | 0.9975   |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0