bert-base-multilingual-cased-ner-hrl
Maintainer: Davlan
58
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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 bert-base-multilingual-cased-ner-hrl model is a Named Entity Recognition (NER) model fine-tuned on 10 high-resourced languages: Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese, and Chinese. It is based on the bert-base-multilingual-cased model and can recognize three types of entities: location (LOC), organization (ORG), and person (PER).
Similar models include the bert-large-NER and bert-base-NER models, which are fine-tuned on the English CoNLL-2003 dataset and can recognize four entity types. The distilbert-base-multilingual-cased model is a smaller, faster multilingual model that can be used for a variety of tasks.
Model inputs and outputs
Inputs
- Raw text in one of the 10 supported languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese, Chinese)
Outputs
- A list of named entities found in the input text, with the entity type (LOC, ORG, PER) and the start/end position of the entity in the text.
Capabilities
The bert-base-multilingual-cased-ner-hrl model can accurately detect and classify named entities in text across 10 different languages. It performs well on a variety of text types, including news articles, social media posts, and other real-world data. The model is particularly useful for tasks that require understanding the key entities mentioned in multilingual text, such as social media monitoring, content analysis, and business intelligence.
What can I use it for?
This model can be used for a variety of applications that involve named entity recognition in multiple languages, such as:
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Multilingual content analysis: Automatically extract and classify key entities from text across different languages to gain insights about topics, trends, and relationships.
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Social media monitoring: Monitor social media conversations in multiple languages and identify important people, organizations, and locations mentioned.
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Business intelligence: Analyze multilingual business documents, reports, and communications to extract key information about customers, partners, competitors, and market trends.
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Knowledge graph construction: Use the entity recognition capabilities to build comprehensive knowledge graphs from multilingual text data.
Things to try
One interesting aspect of the bert-base-multilingual-cased-ner-hrl model is its ability to accurately detect entities even when they do not start with an uppercase letter. This can be particularly useful for processing informal text, such as social media posts or chat messages, where capitalization is often inconsistent.
To test this, you could try feeding the model some text with a mix of capitalized and lowercase entity mentions and see how well it performs. Additionally, you could experiment with combining the outputs of this model with other NLP tasks, such as sentiment analysis or topic modeling, to gain deeper insights from multilingual text data.
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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