---
library_name: transformers
license: mit
base_model: Davlan/afro-xlmr-large-76L
tags:
- kinyarwanda
- topic-classification
- sib200
- afroxlmr
- african-nlp
- generated_from_trainer
metrics:
- f1
- precision
- recall
- accuracy
model-index:
- name: kinyarwanda-topic-afroxlmr-large
  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. -->

# kinyarwanda-topic-afroxlmr-large

This model is a fine-tuned version of [Davlan/afro-xlmr-large-76L](https://huggingface.co/Davlan/afro-xlmr-large-76L) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7530
- F1: 0.8560
- Precision: 0.8622
- Recall: 0.8578
- Accuracy: 0.8578

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 3.6525        | 1.0   | 22   | 1.5583          | 0.5944 | 0.6642    | 0.6566 | 0.6566   |
| 2.3638        | 2.0   | 44   | 0.8003          | 0.7966 | 0.8184    | 0.7980 | 0.7980   |
| 1.2553        | 3.0   | 66   | 0.5707          | 0.8248 | 0.8414    | 0.8283 | 0.8283   |
| 0.7472        | 4.0   | 88   | 0.4984          | 0.8482 | 0.8565    | 0.8485 | 0.8485   |
| 0.2923        | 5.0   | 110  | 0.6913          | 0.7861 | 0.7907    | 0.7879 | 0.7879   |
| 0.2096        | 6.0   | 132  | 0.7531          | 0.7972 | 0.8264    | 0.8081 | 0.8081   |
| 0.1107        | 7.0   | 154  | 0.7435          | 0.8384 | 0.8570    | 0.8384 | 0.8384   |
| 0.0742        | 8.0   | 176  | 0.8058          | 0.8591 | 0.8671    | 0.8586 | 0.8586   |
| 0.0077        | 9.0   | 198  | 0.9892          | 0.8086 | 0.8165    | 0.8081 | 0.8081   |
| 0.0278        | 10.0  | 220  | 0.9835          | 0.8478 | 0.8644    | 0.8485 | 0.8485   |
| 0.0022        | 11.0  | 242  | 1.2416          | 0.8191 | 0.8364    | 0.8182 | 0.8182   |
| 0.0028        | 12.0  | 264  | 1.0595          | 0.8386 | 0.8552    | 0.8384 | 0.8384   |
| 0.0017        | 13.0  | 286  | 1.1092          | 0.8470 | 0.8596    | 0.8485 | 0.8485   |


### Framework versions

- Transformers 5.5.4
- Pytorch 2.11.0+cu130
- Datasets 2.21.0
- Tokenizers 0.22.2