---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-base-ukraine-war-official
  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. -->

# xlm-roberta-base-ukraine-war-official

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: 0.5147
- Accuracy: 0.776
- F1: 0.7747
- Precision: 0.7824
- Recall: 0.776

## 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: 16
- eval_batch_size: 64
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4394        | 1.0   | 1875 | 0.3915          | 0.8365   | 0.8362 | 0.8386    | 0.8365 |
| 0.4008        | 2.0   | 3750 | 0.3924          | 0.8325   | 0.8309 | 0.8459    | 0.8325 |
| 0.3456        | 3.0   | 5625 | 0.3699          | 0.8525   | 0.8524 | 0.8533    | 0.8525 |
| 0.298         | 4.0   | 7500 | 0.3894          | 0.8485   | 0.8479 | 0.8540    | 0.8485 |
| 0.2531        | 5.0   | 9375 | 0.4359          | 0.8475   | 0.8469 | 0.8528    | 0.8475 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.3