readme: Hugging Face's logo
  ---
  language: 
  - ar
  - de
  - en
  - es
  - fr
  - it
  - lv
  - nl
  - pt
  - zh
  - multilingual
  
  ---
  # xlm-roberta-base-ner-hrl
  ## Model description
  **xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). 
  Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages
  ## 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-ner-hrl")
  model = AutoModelForTokenClassification.from_pretrained("Davlan/xlm-roberta-base-ner-hrl")
  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
  example = "Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute."
  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
  The training data for the 10 languages are from: 
  
  Language|Dataset
  -|-
  Arabic | [ANERcorp](https://camel.abudhabi.nyu.edu/anercorp/)
  German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
  English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
  Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
  French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
  Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
  Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
  Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
  Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
  Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)
  
  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
  ## Training procedure
  This model was trained on NVIDIA V100 GPU with recommended hyperparameters from HuggingFace code.
  
  
  