---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: disi-unibo-nlp
  results: []
datasets:
- disi-unibo-nlp/foodex2-clean
---

<!-- 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. -->

# DeBERTa FoodEx2 Coder

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the **train_task1** split of the dataset [foodex2-clean]().
It achieves the following results on the evaluation set:
- Loss: 0.0548
- Accuracy: 0.9822
- F1: 0.8507
- Precision: 0.9301
- Recall: 0.7838

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.124         | 0.2899 | 1000  | 0.1032          | 0.9671   | 0.7090 | 0.8324    | 0.6174 |
| 0.1004        | 0.5799 | 2000  | 0.0855          | 0.9721   | 0.7551 | 0.8769    | 0.6631 |
| 0.0858        | 0.8698 | 3000  | 0.0737          | 0.9757   | 0.7873 | 0.9102    | 0.6937 |
| 0.0736        | 1.1598 | 4000  | 0.0696          | 0.9786   | 0.8196 | 0.9031    | 0.7502 |
| 0.0696        | 1.4497 | 5000  | 0.0639          | 0.9795   | 0.8294 | 0.8996    | 0.7694 |
| 0.068         | 1.7396 | 6000  | 0.0606          | 0.9812   | 0.8401 | 0.9385    | 0.7604 |
| 0.0634        | 2.0296 | 7000  | 0.0593          | 0.9809   | 0.8414 | 0.9123    | 0.7808 |
| 0.0565        | 2.3195 | 8000  | 0.0568          | 0.9820   | 0.8485 | 0.9318    | 0.7790 |
| 0.0584        | 2.6095 | 9000  | 0.0553          | 0.9822   | 0.8512 | 0.9296    | 0.7850 |
| 0.0568        | 2.8994 | 10000 | 0.0548          | 0.9822   | 0.8507 | 0.9301    | 0.7838 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0