---
library_name: transformers
base_model: mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mbeukman-finetuned
  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. -->

# mbeukman-finetuned

This model is a fine-tuned version of [mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-yoruba) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1239
- Precision: 0.7778
- Recall: 0.7799
- F1: 0.7789
- Accuracy: 0.9612

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 125  | 0.1634          | 0.7278    | 0.7521 | 0.7397 | 0.9539   |
| No log        | 2.0   | 250  | 0.1287          | 0.7837    | 0.7772 | 0.7804 | 0.9630   |
| No log        | 3.0   | 375  | 0.1264          | 0.7609    | 0.7799 | 0.7703 | 0.9598   |
| 0.1504        | 4.0   | 500  | 0.1209          | 0.7560    | 0.7939 | 0.7745 | 0.9622   |
| 0.1504        | 5.0   | 625  | 0.1239          | 0.7778    | 0.7799 | 0.7789 | 0.9612   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1