Urchade

Models by this creator

🛸

gliner_multi

urchade

Total Score

116

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.

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Updated 5/28/2024

🏷️

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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Updated 5/27/2024

🧠

gliner_multi-v2.1

urchade

Total Score

51

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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Updated 6/7/2024