NuNER-multilingual-v0.1
Maintainer: numind
57
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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-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.
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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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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