| Hugging Face's logo | |
| --- | |
| language: | |
| - om | |
| - am | |
| - rw | |
| - rn | |
| - ha | |
| - ig | |
| - pcm | |
| - so | |
| - sw | |
| - ti | |
| - yo | |
| - multilingual | |
| --- | |
| # afriberta_base | |
| ## Model description | |
| AfriBERTa base is a pretrained multilingual language model with around 111 million parameters. | |
| The model has 8 layers, 6 attention heads, 768 hidden units and 3072 feed forward size. | |
| The model was pretrained on 11 African languages namely - Afaan Oromoo (also called Oromo), Amharic, Gahuza (a mixed language containing Kinyarwanda and Kirundi), Hausa, Igbo, Nigerian Pidgin, Somali, Swahili, Tigrinya and Yorùbá. | |
| The model has been shown to obtain competitive downstream performances on text classification and Named Entity Recognition on several African languages, including those it was not pretrained on. | |
| ## Intended uses & limitations | |
| #### How to use | |
| You can use this model with Transformers for any downstream task. | |
| For example, assuming we want to finetune this model on a token classification task, we do the following: | |
| ```python | |
| >>> from transformers import AutoTokenizer, AutoModelForTokenClassification | |
| >>> model = AutoModelForTokenClassification.from_pretrained("castorini/afriberta_base") | |
| >>> tokenizer = AutoTokenizer.from_pretrained("castorini/afriberta_base") | |
| # we have to manually set the model max length because it is an imported sentencepiece model, which huggingface does not properly support right now | |
| >>> tokenizer.model_max_length = 512 | |
| ``` | |
| #### Limitations and bias | |
| - This model is possibly limited by its training dataset which are majorly obtained from news articles from a specific span of time. Thus, it may not generalize well. | |
| - This model is trained on very little data (less than 1 GB), hence it may not have seen enough data to learn very complex linguistic relations. | |
| ## Training data | |
| The model was trained on an aggregation of datasets from the BBC news website and Common Crawl. | |
| ## Training procedure | |
| For information on training procedures, please refer to the AfriBERTa [paper]() or [repository](https://github.com/keleog/afriberta) | |
| ### BibTeX entry and citation info | |
| ``` | |
| @inproceedings{ogueji-etal-2021-small, | |
| title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages", | |
| author = "Ogueji, Kelechi and | |
| Zhu, Yuxin and | |
| Lin, Jimmy", | |
| booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning", | |
| month = nov, | |
| year = "2021", | |
| address = "Punta Cana, Dominican Republic", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2021.mrl-1.11", | |
| pages = "116--126", | |
| } | |
| ``` | |