readme: ---
  license: apache-2.0
  ---
  from transformers import pipeline
  tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
  model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
  classifier = pipeline("ner", model=model, tokenizer=tokenizer)
  classifier("Alya told Jasmine that Andrew could pay with cash..")
  [{'end': 2,
    'entity': 'I-PER',
    'index': 1,
    'score': 0.9997861,
    'start': 0,
    'word': '▁Al'},
   {'end': 4,
    'entity': 'I-PER',
    'index': 2,
    'score': 0.9998591,
    'start': 2,
    'word': 'ya'},
   {'end': 16,
    'entity': 'I-PER',
    'index': 4,
    'score': 0.99995816,
    'start': 10,
    'word': '▁Jasmin'},
   {'end': 17,
    'entity': 'I-PER',
    'index': 5,
    'score': 0.9999584,
    'start': 16,
    'word': 'e'},
   {'end': 29,
    'entity': 'I-PER',
    'index': 7,
    'score': 0.99998057,
    'start': 23,
    'word': '▁Andrew'}]
  
  Recommendations
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
  
  Training
  See the following resources for training data and training procedure details:
  
  XLM-RoBERTa-large model card
  CoNLL-2003 data card
  Associated paper
  Evaluation
  See the associated paper for evaluation details.
  
  Environmental Impact
  Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
  
  Hardware Type: 500 32GB Nvidia V100 GPUs (from the associated paper)
  Hours used: More information needed
  Cloud Provider: More information needed
  Compute Region: More information needed
  Carbon Emitted: More information needed
  Technical Specifications
  See the associated paper for further details.
  
  Citation
  BibTeX:
  
  @article{conneau2019unsupervised,
    title={Unsupervised Cross-lingual Representation Learning at Scale},
    author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
    journal={arXiv preprint arXiv:1911.02116},
    year={2019}
  }
  
  APA:
  
  Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116.
  Model Card Authors
  This model card was written by the team at Hugging Face.
  
  How to Get Started with the Model
  Use the code below to get started with the model. You can use this model directly within a pipeline for NER.
  
  Click to expand
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
  model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
  classifier = pipeline("ner", model=model, tokenizer=tokenizer)
  classifier("Hello I'm Omar and I live in Zürich.")
  
  [{'end': 14,
    'entity': 'I-PER',
    'index': 5,
    'score': 0.9999175,
    'start': 10,
    'word': '▁Omar'},
   {'end': 35,
    'entity': 'I-LOC',
    'index': 10,
    'score': 0.9999906,
    'start': 29,
    'word': '▁Zürich'}]
    from transformers import pipeline
  tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
  model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
  classifier = pipeline("ner", model=model, tokenizer=tokenizer)
  classifier("Alya told Jasmine that Andrew could pay with cash..")
  [{'end': 2,
    'entity': 'I-PER',
    'index': 1,
    'score': 0.9997861,
    'start': 0,
    'word': '▁Al'},
   {'end': 4,
    'entity': 'I-PER',
    'index': 2,
    'score': 0.9998591,
    'start': 2,
    'word': 'ya'},
   {'end': 16,
    'entity': 'I-PER',
    'index': 4,
    'score': 0.99995816,
    'start': 10,
    'word': '▁Jasmin'},
   {'end': 17,
    'entity': 'I-PER',
    'index': 5,
    'score': 0.9999584,
    'start': 16,
    'word': 'e'},
   {'end': 29,
    'entity': 'I-PER',
    'index': 7,
    'score': 0.99998057,
    'start': 23,
    'word': '▁Andrew'}]
  
  Recommendations
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
  
  Training
  See the following resources for training data and training procedure details:
  
  XLM-RoBERTa-large model card
  CoNLL-2003 data card
  Associated paper
  Evaluation
  See the associated paper for evaluation details.
  
  Environmental Impact
  Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
  
  Hardware Type: 500 32GB Nvidia V100 GPUs (from the associated paper)
  Hours used: More information needed
  Cloud Provider: More information needed
  Compute Region: More information needed
  Carbon Emitted: More information needed
  Technical Specifications
  See the associated paper for further details.
  
  Citation
  BibTeX:
  
  @article{conneau2019unsupervised,
    title={Unsupervised Cross-lingual Representation Learning at Scale},
    author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudha