Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- sw
 ---
+# SwahBERT: Language model of Swahili
+
+The model and all credits belong to the original authors. The model was uploaded to the HuggingFace Hub for convenience.
+For  more details, please refer the **[original repository](https://github.com/gatimartin/SwahBERT)**.
+
+Is a pretrained monolingual language model for Swahili. <br>
+The model was trained for 800K steps using a corpus of 105MB that was collected from news sites, online discussion, and Wikipedia. <br>
+The evaluation was perfomed on several downstream tasks such as emotion classification, news classification, sentiment classification, and Named entity recognition.
+
+```ruby
+import torch
+from transformers import BertTokenizer
+
+tokenizer = BertTokenizer.from_pretrained("swahbert-base-uncased")
+
+# Tokenized input
+text = "Mlima Kilimanjaro unapatikana Tanzania"
+tokenized_text = tokenizer.tokenize(text)
+
+SwahBERT => ['mlima', 'kilimanjaro', 'unapatikana', 'tanzania']
+mBERT => ['ml', '##ima', 'ki', '##lima', '##nja', '##ro', 'una', '##patikana', 'tan', '##zania']
+
+```
+
+## Pre-training data
+The text was extracted from different sorces;<br>
+ - News sites: `United Nations news, Voice of America (VoA), Deutsche Welle (DW) and taifaleo`<br>
+ - Forums: `JaiiForum`<br>
+ - ``Wikipedia``.
+
+
+## Pre-trained Models
+Download the models here:<br>
+- **[`SwahBERT-Base, Uncased`](https://drive.google.com/drive/folders/1HZTCqxt93F5NcvgAWcbrXZammBPizdxF?usp=sharing)**:12-layer, 768-hidden, 12-heads , 124M parameters
+- **[`SwahBERT-Base, Cased`](https://drive.google.com/drive/folders/1cCcPopqTyzY6AnH9quKcT9kG5zH7tgEZ?usp=sharing)**:12-layer, 768-hidden, 12-heads , 111M parameters
+
+Steps | vocab size | MLM acc | NSP acc | loss |
+--- | --- | --- | --- | --- |
+**800K** | **50K (uncased)** | **76.54** | **99.67** | **1.0667** |
+**800K** | **32K (cased)** | **76.94** | **99.33** | **1.0562** | 
+
+
+## Emotion Dataset
+We released the **[`Swahili emotion dataset`](https://github.com/gatimartin/SwahBERT/tree/main/emotion_dataset)**.<br>
+The data consists of ~13K emotion annotated comments from social media platforms and translated English dataset. <br>
+The data is multi-label with six Ekman’s emotions:  happy, surprise, sadness, fear, anger, and disgust or neutral.
+
+## Evaluation
+The model was tested on four downstream tasks including our new emotion dataset                                                                                                                                                                                         
+
+F1-score of language models on downstream tasks 
+Tasks | SwahBERT | SwahBERT_cased | mBERT |                        
+--- | --- | --- | --- |
+Emotion | 64.46 | 64.77 | 60.52 | 
+News | 90.90 | 89.90 | 89.73 |
+Sentiment | 70.94 | 71.12 | 67.20 |
+NER | 88.50 | 88.60 | 89.36 |
+
+
+## Citation
+Please use the following citation if you use the model or dataset:
+
+``` 
+@inproceedings{martin-etal-2022-swahbert,
+    title = "{S}wah{BERT}: Language Model of {S}wahili",
+    author = "Martin, Gati  and Mswahili, Medard Edmund  and Jeong, Young-Seob  and Woo, Jiyoung",
+    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.naacl-main.23",
+    pages = "303--313"
+    }
+```
+
+