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.
| Feature |
Description |
| Name |
en_chemner |
| Version |
1.0.0 |
| spaCy |
>=3.6.1,<3.7.0 |
| Default Pipeline |
tok2vec, ner |
| Components |
tok2vec, ner |
| Vectors |
514157 keys, 514157 unique vectors (300 dimensions) |
| Sources |
n/a |
| License |
n/a |
| Author |
n/a |
Label Scheme
View label scheme (7 labels for 1 components)
| Component |
Labels |
ner |
ALCOHOL, ALDEHYDE, ALKANE, ALKENE, ALKYNE, C_ACID, KETONE |
Accuracy
| Type |
Score |
ENTS_F |
97.70 |
ENTS_P |
99.07 |
ENTS_R |
96.36 |
TOK2VEC_LOSS |
151.95 |
NER_LOSS |
259.22 |
