RoBERTa for Multilingual Named Entity Recognition Model Description This model detects entities by classifying every token according to the IOB format: You can find the code in this GitHub repository. Training Data This model was fine tuned on a portion of the wikiann dataset corresponding to the following languages: The model was fine tuned on 375.100 sentences in the training set, with a validation set of 173.100 examples. Performance metrics reported are based on additional 173.100 examples. The complete WikiANN dataset includes training examples for 282 languages and was constructed from Wikipedia. Training examples are extracted in an automated manner, exploiting entities mentioned in Wikipedia articles, often are formatted as hyperlinks to the source article. Provided NER tags are in the IOB2 format. Named entities are classified as location (LOC), person (PER), or organization (ORG). Evaluation Results This model achieves the following results (meassured using the test split of the wikiann dataset): Usage You can load this model by using the AutoTokenize and AutoModelForTokenClassification classes: About RoBERTa This model is a fine tuned version of XLM RoBERTa. The original…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy