---
license: mit
base_model: vonewman/xlm-roberta-base-finetuned-wolof
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: wolof-finetuned-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. -->

# wolof-finetuned-ner

This model is a fine-tuned version of [vonewman/xlm-roberta-base-finetuned-wolof](https://huggingface.co/vonewman/xlm-roberta-base-finetuned-wolof) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3539
- Precision: 0.7798
- Recall: 0.8912
- F1: 0.8317
- Accuracy: 0.9850

## 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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 226  | 0.3879          | 0.7412    | 0.8571 | 0.7950 | 0.9834   |
| No log        | 2.0   | 452  | 0.3595          | 0.7378    | 0.8707 | 0.7988 | 0.9833   |
| 0.5119        | 3.0   | 678  | 0.3539          | 0.7798    | 0.8912 | 0.8317 | 0.9850   |


### Framework versions

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