en_chemner

en_chemner: A spaCy Model for Chemical NER

Model Description

The en_chemner model is a specialized Named Entity Recognition (NER) tool designed for the field of chemistry. Built using the spaCy framework, it identifies and classifies chemical entities within English-language texts.

Key Features

• High Precision and Recall: With a precision of 99.07% and a recall of 96.36%, the model offers highly accurate entity recognition, minimizing both false positives and false negatives.
• Rich Label Scheme: The model can identify a variety of chemical entities such as alcohols, aldehydes, alkanes, and more, making it versatile for different chemical analysis tasks.
• Optimized for spaCy: Integrated seamlessly with spaCy (>=3.6.1,<3.7.0), allowing for easy incorporation into existing spaCy pipelines and applications.
• Extensive Vector Library: Comes with over 514,000 unique vectors, each with 300 dimensions, providing a rich foundation for understanding and classifying chemical entities.

Use Cases

The en_chemner model is ideal for:

• Chemical Literature Analysis: Automatically extracting chemical entities from research papers, patents, and textbooks.
• Data Annotation: Assisting in the annotation of chemical databases or creating datasets for further machine learning tasks.
• Educational Purposes: Helping students in chemistry-related fields to identify and understand various chemical compounds and their classifications.

Label Scheme

<details> <summary>View label scheme (7 labels for 1 components)</summary> </details>

Accuracy