Paper and Citation
Paper: Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages
@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}
}
hausa_finetuned_model
This model is a fine-tuned version of HF_llama on the mc4 ha dataset. It achieves the following results on the evaluation set:
- Loss: 1.4357
- Accuracy: 0.6728
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
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