wikineural-multilingual-ner
Maintainer: Babelscape
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The wikineural-multilingual-ner model is a multilingual Named Entity Recognition (NER) model developed by Babelscape. It was fine-tuned on the WikiNEuRal dataset, which was created using a combination of neural and knowledge-based techniques to generate high-quality silver data for NER. The model supports 9 languages: German, English, Spanish, French, Italian, Dutch, Polish, Portuguese, and Russian.
Similar models include bert-base-multilingual-cased-ner-hrl, distilbert-base-multilingual-cased-ner-hrl, and mDeBERTa-v3-base-xnli-multilingual-nli-2mil7, all of which are multilingual models fine-tuned for NER or natural language inference tasks.
Model inputs and outputs
Inputs
- Text: The
wikineural-multilingual-nermodel accepts natural language text as input and performs Named Entity Recognition on it.
Outputs
- Named Entities: The model outputs a list of named entities detected in the input text, including the entity type (e.g. person, organization, location) and the start/end character offsets.
Capabilities
The wikineural-multilingual-ner model is capable of performing high-quality Named Entity Recognition on text in 9 different languages, including European languages like German, French, and Spanish, as well as Slavic languages like Russian and Polish. By leveraging a combination of neural and knowledge-based techniques, the model can accurately identify a wide range of entities across these diverse languages.
What can I use it for?
The wikineural-multilingual-ner model can be a valuable tool for a variety of natural language processing tasks, such as:
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Information Extraction: By detecting named entities in text, the model can help extract structured information from unstructured data sources like news articles, social media, or enterprise documents.
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Content Analysis: Identifying key named entities in text can provide valuable insights for applications like media monitoring, customer support, or market research.
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Machine Translation: The multilingual capabilities of the model can aid in improving the quality of machine translation systems by helping to preserve important named entities across languages.
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Knowledge Graph Construction: The extracted named entities can be used to populate knowledge graphs, enabling more sophisticated semantic understanding and reasoning.
Things to try
One interesting aspect of the wikineural-multilingual-ner model is its ability to handle a diverse set of languages. Developers could experiment with using the model to perform cross-lingual entity recognition, where the input text is in one language and the model identifies entities in another language. This could be particularly useful for applications that need to process multilingual content, such as international news or social media.
Additionally, the model's performance could be further enhanced by fine-tuning it on domain-specific datasets or incorporating it into larger natural language processing pipelines. Researchers and practitioners may want to explore these avenues to optimize the model for their particular use cases.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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