---
license: mit
base_model: ukr-models/xlm-roberta-base-uk
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xddModel
  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. -->

# xddModel

This model is a fine-tuned version of [ukr-models/xlm-roberta-base-uk](https://huggingface.co/ukr-models/xlm-roberta-base-uk) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1495
- Precision: 0.8533
- Recall: 0.8819
- F1: 0.8674
- Accuracy: 0.9625

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 175  | 0.1730          | 0.7640    | 0.8340 | 0.7975 | 0.9463   |
| No log        | 2.0   | 350  | 0.1552          | 0.8131    | 0.8585 | 0.8352 | 0.9527   |
| 0.2473        | 3.0   | 525  | 0.1334          | 0.8433    | 0.8718 | 0.8573 | 0.9611   |
| 0.2473        | 4.0   | 700  | 0.1305          | 0.8429    | 0.8784 | 0.8603 | 0.9615   |
| 0.2473        | 5.0   | 875  | 0.1293          | 0.8541    | 0.8788 | 0.8663 | 0.9626   |
| 0.0833        | 6.0   | 1050 | 0.1346          | 0.8449    | 0.8828 | 0.8634 | 0.9621   |
| 0.0833        | 7.0   | 1225 | 0.1386          | 0.8449    | 0.8827 | 0.8634 | 0.9624   |
| 0.0833        | 8.0   | 1400 | 0.1474          | 0.8548    | 0.8851 | 0.8697 | 0.9632   |
| 0.0558        | 9.0   | 1575 | 0.1496          | 0.8485    | 0.8830 | 0.8654 | 0.9622   |
| 0.0558        | 10.0  | 1750 | 0.1495          | 0.8533    | 0.8819 | 0.8674 | 0.9625   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2