roberta-large-ner-english
Maintainer: Jean-Baptiste
66
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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
roberta-large-ner-english is an English named entity recognition (NER) model that was fine-tuned from the RoBERTa large model on the CoNLL2003 dataset. The model was developed by Jean-Baptiste and is capable of identifying entities such as persons, organizations, locations, and miscellaneous. It was validated on emails and chat data, and outperforms other models on this type of data, particularly for entities that do not start with an uppercase letter.
Model inputs and outputs
Inputs
- Raw text to be processed for named entity recognition
Outputs
- A list of identified entities, with the entity type (PER, ORG, LOC, MISC), the start and end positions in the input text, the text of the entity, and the confidence score.
Capabilities
The roberta-large-ner-english model can accurately identify a variety of named entities in English text, including people, organizations, locations, and miscellaneous entities. It has been shown to perform particularly well on informal text like emails and chat messages, where entities may not always start with an uppercase letter.
What can I use it for?
You can use the roberta-large-ner-english model for a variety of natural language processing tasks that require named entity recognition, such as information extraction, question answering, and content analysis. For example, you could use it to automatically extract the key people, organizations, and locations mentioned in a set of business documents or news articles.
Things to try
One interesting thing to try with the roberta-large-ner-english model is to see how it performs on your own custom text data, especially if it is in a more informal or conversational style. You could also experiment with combining the model's output with other natural language processing techniques, such as relation extraction or sentiment analysis, to gain deeper insights from your text data.
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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