added model card

README.md

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+Hugging Face's logo
+---
+language: 
+- om
+- am
+- rw
+- rn
+- ha
+- ig
+- pcm
+- so
+- sw
+- ti
+- yo
+- multilingual
+datasets:
+
+---
+# AfriBERTa_small
+## Model description
+AfriBERTa small is a pretrained multilingual language model with around 97 million parameters.
+The model has 4 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
+>>> tokenizer = AutoTokenizer.from_pretrained("castorini/afriberta_small")
+>>> model = AutoModelForTokenClassification.from_pretrained("castorini/afriberta_small")
+```
+
+#### 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.
+
+## 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
+```
+Kelechi Ogueji, Yuxin Zhu, Jimmy Lin.
+Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages
+Proceedings of the 1st workshop on Multilingual Representation Learning at EMNLP 2021
+```
+
+