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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