Edit model card
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/model-cards#model-card-metadata)

Hugging Face's logo


  • af
  • nr
  • nso
  • ss
  • st
  • tn
  • ts
  • ve
  • xh
  • zu
  • multilingual


  • masakhaner


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 dataset.

Intended uses & limitations

How to use

You can use this model with Transformers pipeline for NER.

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)

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 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

Downloads last month
Hosted inference API
Token Classification
This model can be loaded on the Inference API on-demand.