Instructions to use EthioNLP/EthioLLM-l-250K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EthioNLP/EthioLLM-l-250K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EthioNLP/EthioLLM-l-250K")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EthioNLP/EthioLLM-l-250K") model = AutoModelForMaskedLM.from_pretrained("EthioNLP/EthioLLM-l-250K") - Notebooks
- Google Colab
- Kaggle
EthioLLM-l-250K
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.0552
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: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
Citation Information
@article{tonja2024ethiollm, title={EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation}, author={Tonja, Atnafu Lambebo and Azime, Israel Abebe and Belay, Tadesse Destaw and Yigezu, Mesay Gemeda and Mehamed, Moges Ahmed and Ayele, Abinew Ali and Jibril, Ebrahim Chekol and Woldeyohannis, Michael Melese and Kolesnikova, Olga and Slusallek, Philipp and others}, journal={arXiv preprint arXiv:2403.13737}, year={2024} }
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Model tree for EthioNLP/EthioLLM-l-250K
Base model
FacebookAI/xlm-roberta-large