gliner_multi

Maintainer: urchade

Total Score

116

Last updated 5/28/2024

🛸

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_multi model is a Named Entity Recognition (NER) model capable of identifying any entity type, providing a practical alternative to traditional NER models that are limited to predefined entities. Unlike Large Language Models (LLMs) that can be costly and large, this model is designed for resource-constrained scenarios. It uses a bidirectional transformer encoder (BERT-like) architecture and has been trained on the Pile-NER dataset.

Similar models include mDeBERTa-v3-base-xnli-multilingual-nli-2mil7, a multilingual model that can perform natural language inference on 100 languages, and bert-base-NER and bert-large-NER, which are fine-tuned BERT models for named entity recognition.

Model inputs and outputs

Inputs

  • Text: The gliner_multi model takes in arbitrary text as input and can identify entities within that text.

Outputs

  • Named entities: The model outputs a list of named entities found in the input text, along with their type (e.g., person, location, organization).

Capabilities

The gliner_multi model is capable of identifying a wide range of entity types, going beyond the predefined categories typical of traditional NER models. This makes it a versatile tool for analyzing and understanding text content. The model's use of a BERT-like architecture also allows it to capture contextual information, improving the accuracy of its entity recognition.

What can I use it for?

The gliner_multi model can be useful in a variety of applications that require understanding and analyzing textual data, such as:

  • Content analysis: Identifying key entities in news articles, social media posts, or other text-based content to gain insights.
  • Information extraction: Extracting specific types of entities (e.g., people, organizations, locations) from large corpora of text.
  • Knowledge graph construction: Building knowledge graphs by connecting entities and their relationships extracted from text.
  • Recommendation systems: Improving the accuracy of recommendations by understanding the entities mentioned in user-generated content.

Things to try

One interesting aspect of the gliner_multi model is its ability to handle a wide range of entity types, going beyond the traditional categories. Try experimenting with different types of text, such as technical documents, social media posts, or literature, to see how the model performs in identifying less common or domain-specific entities. This can provide insights into the model's versatility and potential applications in various industries and 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!

Related Models

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gliner_multi-v2.1

urchade

Total Score

57

The gliner_multi-v2.1 model is a Named Entity Recognition (NER) model developed by urchade that can identify any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that are costly and large for resource-constrained scenarios. The model is part of the GLiNER family of NER models developed by urchade. The gliner_multi-v2.1 model is a multilingual version of the GLiNER model, trained on the Pile-NER dataset. Commercially licensed versions are also available, such as gliner_small-v2.1, gliner_medium-v2.1, and gliner_large-v2.1. Model inputs and outputs Inputs Text**: The gliner_multi-v2.1 model takes in text as input and can process multilingual text. Outputs Entities**: The model outputs a list of entities identified in the input text, along with their corresponding entity types. Capabilities The gliner_multi-v2.1 model can identify a wide range of entity types, unlike traditional NER models that are limited to predefined entities. It can handle both English and multilingual text, making it a flexible choice for various natural language processing tasks. What can I use it for? The gliner_multi-v2.1 model can be used in a variety of applications that require named entity recognition, such as information extraction, content analysis, and knowledge graph construction. Its ability to handle multilingual text makes it particularly useful for global or international use cases. Things to try You can try using the gliner_multi-v2.1 model to extract entities from text in different languages and compare the results to traditional NER models. You can also experiment with different entity types and see how the model performs on your specific use case.

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gliner_base

urchade

Total Score

59

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.

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gliner-multitask-large-v0.5

knowledgator

Total Score

53

The gliner-multitask-large-v0.5 model is a powerful and versatile AI model developed by knowledgator that can handle a wide range of natural language processing tasks. This model is based on a bidirectional transformer encoder similar to BERT, which ensures both high generalization and compute efficiency despite its compact size. The gliner-multitask-large variant of the model has achieved state-of-the-art performance on zero-shot Named Entity Recognition (NER) benchmarks, demonstrating its robustness and flexibility. In addition to NER, the model can also tackle various other information extraction tasks such as relation extraction, summarization, sentiment analysis, key phrase extraction, question answering, and open-ended information extraction. Compared to other similar models like gliner_multi-v2.1, gliner_multi, and gliner_base, the gliner-multitask-large-v0.5 model stands out for its ability to handle a broader range of tasks with high accuracy, making it a powerful tool for diverse natural language processing applications. Model inputs and outputs Inputs Text**: The model takes plain text as input, which can be a sentence, paragraph, or longer document. Task Prompt**: The model requires a user-provided prompt to specify the particular task to be performed on the input text, such as named entity recognition, relation extraction, or sentiment analysis. Outputs Task-specific Annotations**: Depending on the specified task, the model will output annotations or extracted information from the input text. For example, for named entity recognition, the model will identify and categorize entities such as names, organizations, dates, and other specific items. Capabilities The gliner-multitask-large-v0.5 model can handle a wide range of natural language processing tasks, including: Named Entity Recognition (NER)**: Identifying and categorizing entities such as names, organizations, dates, and other specific items in the text. Relation Extraction**: Detecting and classifying relationships between entities within the text. Summarization**: Extracting the most important sentences that summarize the input text, capturing the essential information. Sentiment Extraction**: Identifying parts of the text that signalize a positive, negative, or neutral sentiment. Key-Phrase Extraction**: Identifying and extracting important phrases and keywords from the text. Question-answering**: Finding an answer in the text given a question. Open Information Extraction**: Extracting pieces of text given an open prompt from a user, such as product description extraction. What can I use it for? The versatile gliner-multitask-large-v0.5 model can be leveraged for a wide range of natural language processing applications, making it a valuable tool for researchers, developers, and businesses. Some potential use cases include: Content Moderation**: The model's ability to perform named entity recognition and sentiment analysis can be used to identify and filter potentially harmful or inappropriate content in user-generated text. Customer Service Automation**: The model's question-answering and open information extraction capabilities can be used to build chatbots and virtual assistants that can provide accurate and relevant responses to customer inquiries. Business Intelligence**: The model's ability to perform relation extraction and summarization can be used to extract insights and key information from large volumes of text data, such as customer reviews, financial reports, or market research. Academic Research**: Researchers in fields like linguistics, cognitive science, and natural language processing can use the model to explore various aspects of human language and communication. Things to try One interesting aspect of the gliner-multitask-large-v0.5 model is its ability to perform zero-shot learning, where it can handle tasks without any prior training on specific datasets. This makes the model highly versatile and adaptable to a wide range of applications. To explore the model's capabilities, you could try experimenting with different types of task prompts and input text, and see how the model performs on a variety of natural language processing tasks. Additionally, you could try fine-tuning the model on domain-specific datasets to see if it can further improve its performance on certain tasks. Another interesting area to investigate is the model's ability to handle multilingual text. While the model was primarily trained on English, it may also be able to perform well on text in other languages, which could open up even more applications for the model.

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UniNER-7B-all

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Total Score

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The UniNER-7B-all model is the best model from the Universal NER project. It is a large language model trained on a combination of three data sources: (1) Pile-NER-type data and Pile-NER-definition data generated by ChatGPT, and (2) 40 supervised datasets in the Universal NER benchmark. This robust model outperforms similar NER models like wikineural-multilingual-ner and bert-base-NER, making it a powerful tool for named entity recognition tasks. Model inputs and outputs The UniNER-7B-all model is a text-to-text AI model that can be used for named entity recognition (NER) tasks. It takes in a text input and outputs the entities identified in the text, along with their corresponding types. Inputs Text**: The input text that the model will analyze to identify named entities. Outputs Entity predictions**: The model's predictions of the named entities present in the input text, along with their entity types (e.g. person, location, organization). Capabilities The UniNER-7B-all model is capable of accurately identifying a wide range of named entities within text, including person, location, organization, and more. Its robust training on diverse datasets allows it to perform well on a variety of text types and genres, making it a versatile tool for NER tasks. What can I use it for? The UniNER-7B-all model can be used for a variety of applications that require named entity recognition, such as: Content analysis**: Analyze news articles, social media posts, or other text-based content to identify key entities and track mentions over time. Knowledge extraction**: Extract structured information about entities (e.g. people, companies, locations) from unstructured text. Chatbots and virtual assistants**: Integrate the model into conversational AI systems to better understand user queries and provide more relevant responses. Things to try One interesting thing to try with the UniNER-7B-all model is to use it to analyze text across different domains and genres, such as news articles, academic papers, and social media posts. This can help you understand the model's performance and limitations in different contexts, and identify areas where it excels or struggles. Another idea is to experiment with different prompting techniques to see how they affect the model's entity predictions. For example, you could try providing additional context or framing the task in different ways to see if it impacts the model's outputs.

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