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. 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 German conll 2003 English conll 2003 Spanish conll 2002 French Europeana Newspapers Italian Italian I CAB Latvian Latvian NER Dutch conll 2002 Portuguese Paramopama + Second Harem Chinese MSRA The training dataset distinguis…
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