readme: 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
  
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