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---
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 |