模型:
Davlan/xlm-roberta-base-finetuned-swahili
language: sw datasets:
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.
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.
This model was fine-tuned on Swahili CC-100
This model was trained on a single NVIDIA V100 GPU
Dataset | XLM-R F1 | sw_roberta F1 |
---|---|---|
MasakhaNER | 87.55 | 89.46 |
By David Adelani