The XLM-RoBERTa model was proposed in
Unsupervised Cross-lingual Representation Learning at Scale
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
fine-tuned with the
conll2003
dataset in English.
Language(s) (NLP) or Countries (images):
XLM-RoBERTa is a multilingual model trained on 100 different languages; see
GitHub Repo
for full list; model is fine-tuned on a dataset in English
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
.
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)
and
Bender et al. (2021)
). In the context of tasks relevant to this model,
Mishra et al. (2020)
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)
:
@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'}]
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