Russian Named Entity Recognition Model Model description This model is a fine tuned version of bert base multilingual cased for Named Entity Recognition (NER) in Russian text. It can identify various entity types such as person first names, middle names, last names, cities, districts, etc using the BIOLU tagging format. Intended uses & limitations The model is designed to identify named entities in Russian text. It can be used for tasks such as information extraction, content analysis, and text preprocessing for downstream NLP tasks. How to use Here's a simple example of how to use the model: Limitations and bias The model's performance may vary depending on the domain and style of the input text. It may struggle with rare or complex entity names not seen during training. The model might exhibit biases present in the training data. Training data The model was trained on Detailed NER Dataset RU by AlexKly. Check it out, the dataset is pretty good! Label Information The dataset is labeled using the BIOLU format, where: B: Beginning token of an entity I: Inner token of an entity O: Other (non entity) token L: Last token of an entity U: Unit token (single token entity) The following en…
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