tensorboard safetensors generated_from_trainer base_model:Davlan/xlm-roberta-base-finetuned-yoruba region:us
---
base_model: Davlan/xlm-roberta-base-finetuned-yoruba
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: xlm-roberta-base-finetuned-yoruba-lora-text-classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-base-finetuned-yoruba-lora-text-classification

This model is a fine-tuned version of [Davlan/xlm-roberta-base-finetuned-yoruba](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-yoruba) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5843
- Precision: 0.6850
- Recall: 1.0
- F1 and accuracy: {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327}

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 and accuracy                                            |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------------------------------------------------------:|
| No log        | 1.0   | 388  | 0.6314          | 0.6710    | 1.0    | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6474        | 2.0   | 776  | 0.6289          | 0.6710    | 1.0    | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.652         | 3.0   | 1164 | 0.6270          | 0.6710    | 1.0    | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.646         | 4.0   | 1552 | 0.6235          | 0.6710    | 1.0    | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.646         | 5.0   | 1940 | 0.6188          | 0.6710    | 1.0    | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6459        | 6.0   | 2328 | 0.6092          | 0.6710    | 1.0    | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6281        | 7.0   | 2716 | 0.5958          | 0.6710    | 1.0    | {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} |
| 0.6268        | 8.0   | 3104 | 0.5912          | 0.6727    | 1.0    | {'accuracy': 0.6735218508997429, 'f1': 0.8043143297380586} |
| 0.6268        | 9.0   | 3492 | 0.5861          | 0.6850    | 1.0    | {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} |
| 0.6105        | 10.0  | 3880 | 0.5843          | 0.6850    | 1.0    | {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
