---
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- accuracy
model-index:
- name: kinyarwanda_finetuned_model
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: common_voice rw
      type: common_voice
      config: rw
      split: validation
      args: rw
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.512215461897924
language:
- rw
---

<!-- 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. -->

# Paper and Citation
Paper: [Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages](https://arxiv.org/abs/2403.06018)
```
@misc{toukmaji2024fewshot,
      title={Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages}, 
      author={Christopher Toukmaji},
      year={2024},
      eprint={2403.06018},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

# kinyarwanda_finetuned_model

This model is a fine-tuned version of [HF_llama](https://huggingface.co/HF_llama) on the common_voice rw dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2024
- Accuracy: 0.5122

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3