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  ---
  
  # xlm-roberta-large-finetuned-conll03-english
  
  #  Table of Contents
  
  1. [Model Details](#model-details)
  2. [Uses](#uses)
  3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  4. [Training](#training)
  5. [Evaluation](#evaluation)
  6. [Environmental Impact](#environmental-impact)
  7. [Technical Specifications](#technical-specifications)
  8. [Citation](#citation)
  9. [Model Card Authors](#model-card-authors)
  10. [How To Get Started With the Model](#how-to-get-started-with-the-model)
  
  
  # Model Details
  
  ## Model Description
  
  The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) fine-tuned with the [conll2003](https://huggingface.co/datasets/conll2003) dataset in English.
  
  - **Developed by:** See [associated paper](https://arxiv.org/abs/1911.02116)
  - **Model type:** Multi-lingual language model
  - **Language(s) (NLP) or Countries (images):** XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in English
  - **License:** More information needed
  - **Related Models:** [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm)
      - **Parent Model:** [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large)
  - **Resources for more information:** 
    -[GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr)
    -[Associated Paper](https://arxiv.org/abs/1911.02116)
  
  # Uses
  
  ## Direct Use
  
  The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text. 
  
  ## Downstream Use
  
  Potential downstream use cases include Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. To learn more about token classification and other potential downstream use cases, see the Hugging Face [token classification docs](https://huggingface.co/tasks/token-classification).
  
  ## Out-of-Scope Use
  
  The model should not be used to intentionally create hostile or alienating environments for people. 
  
  # Bias, Risks, and Limitations
  
  **CONTENT WARNING: Readers should be made aware that language generated by this model may be disturbing or offensive to some and may propagate historical and current stereotypes.**
  
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). In the context of tasks relevant to this model, [Mishra et al. (2020)](https://arxiv.org/pdf/2008.03415.pdf) explore social biases in NER systems for English and find that there is systematic bias in existing NER systems in that they fail to identify named entities from different demographic groups (though this paper did not look at BERT). For example, using a sample sentence from [Mishra et al. (2020)](https://arxiv.org/pdf/2008.03415.pdf):
  
  ```python
  >>> 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 awar