Instructions to use Davlan/xlm-roberta-base-finetuned-naija with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/xlm-roberta-base-finetuned-naija with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Davlan/xlm-roberta-base-finetuned-naija")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-finetuned-naija") model = AutoModelForMaskedLM.from_pretrained("Davlan/xlm-roberta-base-finetuned-naija", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Further info on pretraining
#1
by manueltonneau - opened
Hi @Davlan and thank you very much for this contribution. Could you please provide more info on the pretraining dataset, especially its size? Also, could you please say a bit more about how you pretrained (is it adaptive finetuning like AfroXLMR)?
manueltonneau changed discussion title from Size of pretraining dataset to Further info on pretraining
Finally, is there a paper I can cite if I want to reference this model in a paper? Thank you!
Yes, there is a paper, please cite our MasakhaNER paper https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00416/107614/MasakhaNER-Named-Entity-Recognition-for-African
Table 10 has the information on the monolingual finetuning corpus
Thank you :)
manueltonneau changed discussion status to closed