---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
base_model: xlm-roberta-base
model-index:
- name: edos-2023-baseline-xlm-roberta-base-label_vector
  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. -->

# edos-2023-baseline-xlm-roberta-base-label_vector

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5797
- F1: 0.2746

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1596        | 1.18  | 100  | 1.9772          | 0.0891 |
| 1.8651        | 2.35  | 200  | 1.7720          | 0.1159 |
| 1.6848        | 3.53  | 300  | 1.7193          | 0.1892 |
| 1.5532        | 4.71  | 400  | 1.6794          | 0.2191 |
| 1.466         | 5.88  | 500  | 1.6095          | 0.2419 |
| 1.3562        | 7.06  | 600  | 1.5771          | 0.2694 |
| 1.2909        | 8.24  | 700  | 1.5761          | 0.2707 |
| 1.2027        | 9.41  | 800  | 1.5747          | 0.2764 |
| 1.192         | 10.59 | 900  | 1.5893          | 0.2686 |
| 1.1256        | 11.76 | 1000 | 1.5797          | 0.2746 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2