| Hugging Face's logo | |
| --- | |
| language: ha | |
| 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. | |
| ```python | |
| >>> 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](http://data.statmt.org/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](https://github.com/masakhane-io/masakhane-ner) | 87.37 | 89.74 | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |