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language: sw datasets:
xlm-roberta-base-finetuned-swahili
Model description
xlm-roberta-base-finetuned-swahili is a Swahili RoBERTa model obtained by fine-tuning xlm-roberta-base model on Swahili language texts. It provides better performance than the XLM-RoBERTa on text classification and named entity recognition datasets.
Specifically, this model is a xlm-roberta-base model that was fine-tuned on Swahili corpus.
Intended uses & limitations
How to use
You can use this model with Transformers pipeline for masked token prediction.
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-swahili')
>>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko <mask> kwamba hakuna uhalifu ulitendwa")
[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Ufaransa kwamba hakuna uhalifu ulitendwa',
'score': 0.5077782273292542,
'token': 190096,
'token_str': 'Ufaransa'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa',
'score': 0.3657738268375397,
'token': 7270,
'token_str': 'Paris'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Gabon kwamba hakuna uhalifu ulitendwa',
'score': 0.01592041552066803,
'token': 176392,
'token_str': 'Gabon'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa',
'score': 0.010881908237934113,
'token': 9942,
'token_str': 'France'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Marseille kwamba hakuna uhalifu ulitendwa',
'score': 0.009554869495332241,
'token': 185918,
'token_str': 'Marseille'}]
Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
Training data
This model was fine-tuned on Swahili CC-100
Training procedure
This model was trained on a single NVIDIA V100 GPU
Eval results on Test set (F-score, average over 5 runs)
Dataset | XLM-R F1 | sw_roberta F1 |
---|---|---|
MasakhaNER | 87.55 | 89.46 |
BibTeX entry and citation info
By David Adelani
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