NuNER-v0.1

Maintainer: numind

Last updated 5/28/2024

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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

The NuNER-v0.1 model is an English language entity recognition model fine-tuned from the RoBERTa-base model by the team at NuMind. This model provides strong token embeddings for entity recognition tasks in English. It was the prototype for the NuNER v1.0 model, which is the version reported in the paper introducing the model.

The NuNER-v0.1 model outperforms the base RoBERTa-base model on entity recognition, achieving an F1 macro score of 0.7500 compared to 0.7129 for RoBERTa-base. Combining the last and second-to-last hidden states further improves performance to 0.7686 F1 macro.

Other notable entity recognition models include bert-base-NER, a BERT-base model fine-tuned on the CoNLL-2003 dataset, and roberta-large-ner-english, a RoBERTa-large model fine-tuned for English NER.

Model inputs and outputs

Inputs

Text: The model takes in raw text as input, which it then tokenizes and encodes for processing.

Outputs

Entity predictions: The model outputs a sequence of entity predictions for the input text, classifying each token as belonging to one of the four entity types: location (LOC), organization (ORG), person (PER), or miscellaneous (MISC).
Token embeddings: The model can also be used to extract token-level embeddings, which can be useful for downstream tasks. The author suggests using the concatenation of the last and second-to-last hidden states for better quality embeddings.

Capabilities

The NuNER-v0.1 model is highly capable at recognizing entities in English text, surpassing the base RoBERTa model on the CoNLL-2003 NER dataset. It can accurately identify locations, organizations, people, and miscellaneous entities within input text. This makes it a powerful tool for applications that require understanding the entities mentioned in documents, such as information extraction, knowledge graph construction, or content analysis.

What can I use it for?

The NuNER-v0.1 model can be used for a variety of applications that involve identifying and extracting entities from English text. Some potential use cases include:

Information Extraction: The model can be used to automatically extract key entities (people, organizations, locations, etc.) from documents, articles, or other text-based data sources.
Knowledge Graph Construction: The entity predictions from the model can be used to populate a knowledge graph with structured information about the entities mentioned in a corpus.
Content Analysis: By understanding the entities present in text, the model can enable more sophisticated content analysis tasks, such as topic modeling, sentiment analysis, or text summarization.
Chatbots and Virtual Assistants: The entity recognition capabilities of the model can be leveraged to improve the natural language understanding of chatbots and virtual assistants, allowing them to better comprehend user queries and respond appropriately.

Things to try

One interesting aspect of the NuNER-v0.1 model is its ability to produce high-quality token embeddings by concatenating the last and second-to-last hidden states. These embeddings could be used as input features for a wide range of downstream NLP tasks, such as text classification, named entity recognition, or relation extraction. Experimenting with different ways of utilizing these embeddings, such as fine-tuning on domain-specific datasets or combining them with other model architectures, could lead to exciting new applications and performance improvements.

Another avenue to explore would be comparing the NuNER-v0.1 model's performance on different types of text data, beyond the news-based CoNLL-2003 dataset used for evaluation. Trying the model on more informal, conversational text (e.g., social media, emails, chat logs) could uncover interesting insights about its generalization capabilities and potential areas for improvement.

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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NuNER-multilingual-v0.1

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The NuNER-multilingual-v0.1 model is a powerful multilingual entity recognition foundation model developed by NuMind. It is built on top of the Multilingual BERT (mBERT) model and has been fine-tuned on an artificially annotated subset of the OSCAR dataset. This model provides domain and language-independent embeddings for the entity recognition task, supporting over 9 languages. Compared to the base mBERT model, the NuNER-multilingual-v0.1 model demonstrates superior performance, with an F1 macro score of 0.5892 versus 0.5206 for mBERT. Additionally, by using a "two emb trick" technique, the model's performance can be further improved to an F1 macro score of 0.6231. Model inputs and outputs Inputs Textual data in one of the supported languages Outputs Embeddings that can be used for downstream entity recognition tasks Capabilities The NuNER-multilingual-v0.1 model excels at providing high-quality embeddings for the entity recognition task, with the ability to generalize across different languages and domains. This makes it a valuable tool for a wide range of natural language processing applications, including named entity recognition, knowledge extraction, and information retrieval. What can I use it for? The NuNER-multilingual-v0.1 model can be leveraged in various use cases, such as: Developing multilingual information extraction systems Building knowledge graphs and knowledge bases from unstructured text Enhancing search and recommendation engines with entity-based features Improving chatbots and virtual assistants with better understanding of named entities Things to try One interesting aspect of the NuNER-multilingual-v0.1 model is the "two emb trick" technique, which can be used to improve the quality of the embeddings. By concatenating the hidden states from the last and second-to-last layers of the model, you can obtain embeddings with even better performance for your entity recognition tasks.

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NuNER_Zero

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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.

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The bert-base-NER model is a fine-tuned BERT model that is ready to use for Named Entity Recognition (NER) and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset. If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available. The maintainer, dslim, has also provided several other NER models including distilbert-NER, bert-large-NER, and both cased and uncased versions of bert-base-NER. Model inputs and outputs Inputs Text**: The model takes a text sequence as input and predicts the named entities within that text. Outputs Named entities**: The model outputs the recognized named entities, along with their type (LOC, ORG, PER, MISC) and the start/end position within the input text. Capabilities The bert-base-NER model is capable of accurately identifying a variety of named entities within text, including locations, organizations, persons, and miscellaneous entities. This can be useful for applications such as information extraction, content analysis, and knowledge graph construction. What can I use it for? The bert-base-NER model can be used for a variety of text processing tasks that involve identifying and extracting named entities. For example, you could use it to build a search engine that allows users to find information about specific people, organizations, or locations mentioned in a large corpus of text. You could also use it to automatically extract key entities from customer service logs or social media posts, which could be valuable for market research or customer sentiment analysis. Things to try One interesting thing to try with the bert-base-NER model is to experiment with incorporating it into a larger natural language processing pipeline. For example, you could use it to first identify the named entities in a piece of text, and then use a different model to classify the sentiment or topic of the text, focusing on the identified entities. This could lead to more accurate and nuanced text analysis. Another idea is to fine-tune the model further on a domain-specific dataset, which could help it perform better on specialized text. For instance, if you're working with legal documents, you could fine-tune the model on a corpus of legal text to improve its ability to recognize legal entities and terminology.

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