---
license: mit
base_model: LIAMF-USP/roberta-large-finetuned-race
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bigbird-roberta-large
  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. -->

# bigbird-roberta-large

This model is a fine-tuned version of [LIAMF-USP/roberta-large-finetuned-race](https://huggingface.co./LIAMF-USP/roberta-large-finetuned-race) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.1976
- F1: 0.1757
- Precision: 0.1893
- Recall: 0.1911

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6272        | 0.3233 | 1200  | 1.6094          | 0.2082   | 0.1431 | 0.2007    | 0.1996 |
| 1.6218        | 0.6466 | 2400  | 1.6094          | 0.2117   | 0.1340 | 0.1876    | 0.1998 |
| 1.6235        | 0.9698 | 3600  | 1.6094          | 0.2104   | 0.1752 | 0.2005    | 0.2015 |
| 1.617         | 1.2931 | 4800  | 1.6094          | 0.2088   | 0.1956 | 0.2037    | 0.2028 |
| 1.61          | 1.6164 | 6000  | 1.6094          | 0.2091   | 0.1606 | 0.2127    | 0.2024 |
| 1.6126        | 1.9397 | 7200  | 1.6094          | 0.2108   | 0.1796 | 0.1965    | 0.2011 |
| 1.6174        | 2.2629 | 8400  | 1.6094          | 0.2095   | 0.1833 | 0.2036    | 0.2024 |
| 1.6125        | 2.5862 | 9600  | 1.6094          | 0.2097   | 0.1847 | 0.1963    | 0.2016 |
| 1.6192        | 2.9095 | 10800 | 1.6094          | 0.1976   | 0.1757 | 0.1893    | 0.1911 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1