Urchade
Models by this creator
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gliner_multi
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.
Updated 5/28/2024
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gliner_multi-v2.1
67
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.
Updated 7/2/2024
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gliner_base
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.
Updated 5/27/2024
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gliner_multi_pii-v1
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The gliner_multi_pii-v1 model is a named entity recognition (NER) model developed by Urchade Zaratiana that can identify a wide range of personally identifiable information (PII) in text. This model is based on the GLiNER architecture, which uses a bidirectional transformer encoder (similar to BERT) to provide a practical alternative to traditional NER models and large language models. Compared to other GLiNER models, the gliner_multi_pii-v1 version has been fine-tuned on the urchade/synthetic-pii-ner-mistral-v1 dataset to specialize in detecting PII entities. This includes common entity types like person, organization, phone number, and email address, as well as more specialized PII like passport numbers, credit card information, social security numbers, and medical data. Model inputs and outputs Inputs Text**: The input to the model is plain text. Outputs Entities**: The model outputs a list of detected entities, with each entity containing the following information: text: The text span of the detected entity. label: The entity type, such as "person", "phone number", "email", etc. Capabilities The gliner_multi_pii-v1 model excels at identifying a wide range of personally identifiable information within text. It can detect common PII like names, contact details, and identification numbers, as well as more specialized PII such as medical conditions, insurance information, and financial data. This capability makes the model useful for a variety of applications that require sensitive data extraction, such as: Compliance and regulatory monitoring Customer onboarding and identity verification Data anonymization and redaction Fraud detection and prevention What can I use it for? The gliner_multi_pii-v1 model is well-suited for any project that involves identifying and extracting personally identifiable information from text. Some potential use cases include: Compliance and Regulatory Monitoring**: Use the model to scan documents and communications for PII that may need to be protected or redacted to meet regulatory requirements. Customer Onboarding and Identity Verification**: Leverage the model to automatically extract relevant PII from customer documents and forms, streamlining the onboarding process. Data Anonymization and Redaction**: Apply the model to identify sensitive information that should be removed or obfuscated before sharing or publishing data. Fraud Detection and Prevention**: Integrate the model into fraud detection systems to identify suspicious patterns or anomalies in PII. Things to try One interesting aspect of the gliner_multi_pii-v1 model is its ability to recognize a diverse range of PII entity types. Instead of being limited to a predefined set of entities, the model can dynamically identify and classify a wide variety of personally identifiable information. To explore this capability, you could try providing the model with text that contains a mix of different PII elements, such as a resume or a customer support ticket. Observe how the model is able to accurately locate and categorize the various PII entities, ranging from names and contact details to more specialized information like medical conditions or financial data. Another interesting experiment would be to compare the performance of the gliner_multi_pii-v1 model to traditional, rule-based PII detection approaches. By testing the model on a diverse set of real-world data, you can assess its robustness and flexibility compared to more rigid, predefined systems.
Updated 9/19/2024
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gliner_large-v2
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The gliner_large-v2 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, similar to BERT. This provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that can be costly and large for resource-constrained scenarios. The model has been trained on the NuNER dataset, which is commercially permissive. It is available in several versions, including gliner_base, gliner_multi, and gliner_multi-v2.1, each with varying model sizes and languages supported. Model inputs and outputs Inputs Text**: The input text for which entities should be identified. Labels**: A list of entity types that the model should recognize, such as "person", "organization", "date", etc. Outputs Entities**: A list of identified entities, with each entity represented as a dictionary containing the following fields: text: The text of the identified entity label: The type of the identified entity (e.g., "person", "organization") score: The model's confidence score for the identified entity Capabilities The gliner_large-v2 model is capable of identifying a wide range of entity types, making it a versatile tool for various natural language processing tasks. It can be used to extract information from text, such as the names of people, organizations, locations, dates, and more. One of the key advantages of this model is its ability to handle any entity type, unlike traditional NER models that are limited to predefined entities. This flexibility allows the model to be used in a variety of applications, from content analysis to knowledge extraction. What can I use it for? The gliner_large-v2 model can be used in a variety of applications that require named entity recognition, such as: Content analysis**: Extracting key entities from text to gain insights into the topic, sentiment, or structure of the content. Knowledge extraction**: Identifying important entities and their relationships in text, which can be used to build knowledge graphs or populate databases. Information retrieval**: Improving search and document retrieval by focusing on the most relevant entities in the text. Conversational AI**: Enhancing chatbots and virtual assistants by understanding the entities mentioned in user queries or dialog. Things to try One interesting aspect of the gliner_large-v2 model is its ability to handle a wide range of entity types. You could try experimenting with different sets of labels to see how the model performs on various domains or types of text. For example, you could try using industry-specific entity types or a more diverse set of entity categories to see how the model adapts. Another interesting thing to try would be to compare the performance of the gliner_large-v2 model to other NER models, such as the gliner_base or gliner_multi-v2.1 versions, on a specific task or dataset. This could help you understand the tradeoffs between model size, language support, and performance for your use case.
Updated 9/6/2024