readme: Hugging Face's logo
  ---
  language: 
  - ar
  - as
  - bn
  - ca
  - en
  - es
  - eu
  - fr
  - gu
  - hi
  - id
  - ig
  - mr
  - pa
  - pt
  - sw
  - ur
  - vi
  - yo
  - zh
  - multilingual
  
  
  datasets:
  - wikiann
  ---
  # xlm-roberta-base-wikiann-ner
  ## Model description
  **xlm-roberta-base-wikiann-ner** is the first **Named Entity Recognition** model for 20 languages (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi,  Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese) 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  languages datasets obtained from  [WikiANN](https://huggingface.co/datasets/wikiann) 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("xlm-roberta-base-wikiann-ner")
  model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-base-wikiann-ner")
  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
  example = "Ìbọn ń ró kù kù gẹ́gẹ́ bí ọwọ́ ọ̀pọ̀ aráàlù ṣe tẹ ìbọn ní Kyiv láti dojú kọ Russia"
  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 20 NER datasets (Arabic, Assamese, Bengali, Catalan, English, Spanish, Basque, French, Gujarati, Hindi, Indonesia, Igbo, Marathi, Punjabi,  Portugues and Swahili, Urdu, Vietnamese, Yoruba, Chinese)[wikiann](https://huggingface.co/datasets/wikiann). 
  
  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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