NuNER_Zero
Maintainer: numind
57
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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
NuNER Zero is a zero-shot Named Entity Recognition (NER) model developed by numind. It uses the GLiNER architecture, which takes a concatenation of entity types and text as input. Unlike GLiNER, NuNER Zero is a token classifier, allowing it to detect arbitrary long entities.
The model was trained on the NuNER v2.0 dataset, which combines subsets of Pile and C4 annotated using Large Language Models (LLMs). At the time of its release, NuNER Zero was the best compact zero-shot NER model, outperforming GLiNER-large-v2.1 by 3.1% token-level F1-Score on GLiNERS's benchmark.
Model inputs and outputs
Inputs
- Text: The input text for named entity recognition.
- Entity types: The set of entity types to detect in the input text.
Outputs
- Entities: A list of detected entities, where each entity contains the following information:
text: The text of the detected entity.label: The entity type of the detected entity.start: The start index of the entity in the input text.end: The end index of the entity in the input text.
Capabilities
NuNER Zero can detect a wide range of entity types in text, including organizations, initiatives, projects, and more. It achieves this through its zero-shot capabilities, which allow it to identify entities without being trained on a specific set of predefined types.
The model's token-level classification approach also enables it to detect long entities that span multiple tokens, which is a limitation of traditional NER models.
What can I use it for?
NuNER Zero can be a valuable tool for a variety of natural language processing tasks, such as:
- Content analysis: Extracting relevant entities from text, such as news articles, research papers, or social media posts, to gain insights and understand the key topics and concepts.
- Knowledge graph construction: Building knowledge graphs by identifying and linking entities in large text corpora, which can be used for tasks like question answering and recommendation systems.
- Business intelligence: Automating the extraction of relevant entities from customer support tickets, financial reports, or product descriptions to support decision-making and process optimization.
Things to try
One interesting aspect of NuNER Zero is its ability to detect entities without being trained on a predefined set of types. This makes it a versatile tool that can be applied to a wide range of domains and use cases.
To get the most out of the model, you could experiment with different entity types and see how it performs on your specific data and requirements. Additionally, you could explore ways to combine NuNER Zero with other natural language processing models, such as relation extraction or sentiment analysis, to build more comprehensive text understanding pipelines.
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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