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

# AmharicNewsCharacterNormalized

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2054
- Accuracy: 0.9538
- Precision: 0.9539
- Recall: 0.9538
- F1: 0.9538

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2737        | 1.0   | 945  | 0.1984          | 0.9175   | 0.9197    | 0.9175 | 0.9172 |
| 0.2651        | 2.0   | 1890 | 0.2313          | 0.9409   | 0.9408    | 0.9409 | 0.9407 |
| 0.2155        | 3.0   | 2835 | 0.1687          | 0.9480   | 0.9480    | 0.9480 | 0.9479 |
| 0.4065        | 4.0   | 3780 | 0.2072          | 0.9439   | 0.9483    | 0.9439 | 0.9443 |
| 0.0785        | 5.0   | 4725 | 0.2054          | 0.9538   | 0.9539    | 0.9538 | 0.9538 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1