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---
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
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