---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- udpos28
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 1a5e2b8e
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: udpos28
      type: udpos28
      config: te
      split: validation
      args: te
    metrics:
    - name: Precision
      type: precision
      value: 0.894336015358501
    - name: Recall
      type: recall
      value: 0.8576779328683283
    - name: F1
      type: f1
      value: 0.8680916339670367
    - name: Accuracy
      type: accuracy
      value: 0.947129909365559
---

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# 1a5e2b8e

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the udpos28 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3219
- Precision: 0.8943
- Recall: 0.8577
- F1: 0.8681
- Accuracy: 0.9471

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0423        | 7.58  | 1000 | 0.3219          | 0.8943    | 0.8577 | 0.8681 | 0.9471   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0