---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-uk-base-ner
  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-uk-base-ner

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: 0.2510
- Precision: 0.5951
- Recall: 0.6256
- F1: 0.6100
- Accuracy: 0.9264

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7224        | 1.0   | 514  | 0.3856          | 0.4590    | 0.4581 | 0.4586 | 0.8996   |
| 0.3616        | 2.0   | 1028 | 0.2893          | 0.5528    | 0.5533 | 0.5531 | 0.9190   |
| 0.2783        | 3.0   | 1542 | 0.2652          | 0.5661    | 0.5965 | 0.5809 | 0.9227   |
| 0.2362        | 4.0   | 2056 | 0.2531          | 0.5882    | 0.6256 | 0.6063 | 0.9263   |
| 0.2124        | 5.0   | 2570 | 0.2510          | 0.5951    | 0.6256 | 0.6100 | 0.9264   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2