Update README.md

README.md

@@ -1,3 +1,62 @@
 ---
 license: apache-2.0
 ---
+
+Hugging Face's logo
+---
+language: 
+- af
+- nr
+- nso
+- ss
+- st
+- tn
+- ts
+- ve
+- xh
+- zu
+- multilingual
+
+
+datasets:
+- masakhaner
+---
+# xlm-roberta-base-sadilar-ner
+## Model description
+**xlm-roberta-base-sadilar-ner** is the first **Named Entity Recognition** model for 10 South African languages (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) based on a fine-tuned  XLM-RoBERTa large model.  It achieves the **state-of-the-art performance** for the NER task. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). 
+Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of South African languages datasets obtained from  [SADILAR](https://www.sadilar.org/index.php/en/) dataset. 
+## Intended uses & limitations
+#### How to use
+You can use this model with Transformers *pipeline* for NER.
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+tokenizer = AutoTokenizer.from_pretrained("Davlan/xlm-roberta-base-sadilar-ner")
+model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-sadilar-ner")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Kuchaza kona ukuthi uMengameli uMnuz Cyril Ramaphosa, usebatshelile ukuthi uzosikhipha maduze isitifiketi."
+ner_results = nlp(example)
+print(ner_results)
+```
+#### 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 10 African NER datasets (Afrikaans, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, siSwati, Tshivenda and Xitsonga) [SADILAR](https://www.sadilar.org/index.php/en/) dataset
+
+The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-PER |Beginning of a person’s name right after another person’s name
+I-PER |Person’s name
+B-ORG |Beginning of an organisation right after another organisation
+I-ORG |Organisation
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
+
+### BibTeX entry and citation info
+
+```
+
+
+