gliner_base

Maintainer: urchade

Total Score

59

Last updated 5/27/2024

🏷️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

The gliner_base model is a Named Entity Recognition (NER) model developed by Urchade Zaratiana. It is capable of identifying any entity type using a bidirectional transformer encoder, providing a practical alternative to traditional NER models with predefined entities or large language models (LLMs) that can be costly and large for resource-constrained scenarios. The GLiNER-multi model is a similar version trained on the Pile-NER dataset for research purposes, while commercially licensed versions are also available.

The gliner_base model was trained on the CoNLL-2003 Named Entity Recognition dataset, which contains 14,987 training examples and distinguishes between the beginning and continuation of entities. It can identify four types of entities: location (LOC), organization (ORG), person (PER), and miscellaneous (MISC). In terms of performance, the model achieves an F1 score of 91.7 on the test set.

Model Inputs and Outputs

Inputs

  • Plain text to be analyzed for named entities

Outputs

  • A list of identified entities, including the entity text, entity type, and position in the input text

Capabilities

The gliner_base model can be used to perform Named Entity Recognition (NER) on natural language text. It is capable of identifying a wide range of entity types, going beyond the traditional predefined set of entities. This flexibility makes it a practical alternative to traditional NER models or large language models that can be costly and unwieldy.

What Can I Use It For?

The gliner_base model can be useful in a variety of applications that require named entity extraction, such as information extraction, data mining, content analysis, and knowledge graph construction. For example, you could use it to automatically extract entities like people, organizations, locations, and miscellaneous information from text documents, news articles, or social media posts. This information could then be used to power search, recommendation, or analytics systems.

Things to Try

One interesting thing to try with the gliner_base model is to compare its performance on different types of text. Since it was trained on news articles, it may perform better on formal, journalistic text than on more conversational or domain-specific language. You could experiment with applying the model to different genres or domains and analyze the results to better understand its strengths and limitations.

Another idea is to use the model as part of a larger NLP pipeline, combining it with other models or components to tackle more complex text understanding tasks. For example, you could use the gliner_base model to extract entities, then use a relation extraction model to identify the relationships between those entities, or a sentiment analysis model to understand the overall sentiment expressed in the text.



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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Total Score

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Total Score

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Total Score

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