pytorch xlm-roberta token-classification af nr nso ss st tn ts ve xh zu multilingual dataset:masakhaner transformers autotrain_compatible
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---
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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