---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- masakhaner2
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-wolof
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: masakhaner2
      type: masakhaner2
      config: wol
      split: validation
      args: wol
    metrics:
    - name: F1
      type: f1
      value: 0.8217446270543616
---

<!-- 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-finetuned-wolof

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the masakhaner2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0589
- F1: 0.8217

## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2113        | 1.0   | 247  | 0.0823          | 0.7443 |
| 0.0749        | 2.0   | 494  | 0.0635          | 0.8097 |
| 0.0445        | 3.0   | 741  | 0.0589          | 0.8217 |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3