Dslim
Models by this creator
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bert-base-NER
415
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.
Updated 5/28/2024
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bert-large-NER
127
bert-large-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition 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-large-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 smaller BERT model fine-tuned on the same dataset, a bert-base-NER version is also available from the same maintainer, dslim. Model inputs and outputs Inputs A text sequence to analyze for named entities Outputs A list of recognized entities, their type (LOC, ORG, PER, MISC), and their position in the input text Capabilities bert-large-NER can accurately identify and classify named entities in English text, such as people, organizations, locations, and miscellaneous entities. It outperforms previous state-of-the-art models on the CoNLL-2003 NER benchmark. What can I use it for? You can use bert-large-NER for a variety of applications that involve named entity recognition, such as: Information extraction from text documents Knowledge base population by identifying key entities Chatbots and virtual assistants to understand user queries Content analysis and categorization The high performance of this model makes it a great starting point for building NER-based applications. Things to try One interesting thing to try with bert-large-NER is analyzing text from different domains beyond news articles, which was the primary focus of the CoNLL-2003 dataset. The model may perform differently on text from social media, scientific publications, or other genres. Experimenting with fine-tuning or ensembling the model for specialized domains could lead to further performance improvements.
Updated 5/28/2024