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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- en |
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model-index: |
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- name: en_chemner |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.9906542056 |
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- name: NER Recall |
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type: recall |
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value: 0.9636363636 |
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- name: NER F Score |
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type: f_score |
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value: 0.9769585253 |
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widget: |
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- text: >- |
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Cinammaldehyde is a fragrant compound found in cinammon. Icosanoic acid, is |
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a saturated fatty acid with a 20-carbon chain. Triptane is commonly used as |
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an anti-knock additive in aviation fuels. Benzophenone is a widely used |
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building block in organic chemistry, being the parent diarylketone. Geraniol |
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is a monoterpenoid and an alcohol. It is the primary component of citronella |
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oil and is a primary component of rose oil, palmarosa oil. |
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license: apache-2.0 |
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--- |
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# en_chemner: A spaCy Model for Chemical NER |
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## Model Description |
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The `en_chemner` model is a specialized Named Entity Recognition (NER) tool designed for the field of chemistry. Built using the spaCy framework, |
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it identifies and classifies chemical entities within English-language texts. |
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### Key Features |
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- **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. |
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- **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. |
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- **Optimized for spaCy**: Integrated seamlessly with spaCy (>=3.6.1,<3.7.0), allowing for easy incorporation into existing spaCy pipelines and applications. |
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- **Extensive Vector Library**: Comes with over 514,000 unique vectors, each with 300 dimensions, providing a rich foundation for understanding and classifying chemical entities. |
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### Use Cases |
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The `en_chemner` model is ideal for: |
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- **Chemical Literature Analysis**: Automatically extracting chemical entities from research papers, patents, and textbooks. |
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- **Data Annotation**: Assisting in the annotation of chemical databases or creating datasets for further machine learning tasks. |
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- **Educational Purposes**: Helping students in chemistry-related fields to identify and understand various chemical compounds and their classifications. |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_chemner` | |
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| **Version** | `1.0.0` | |
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| **spaCy** | `>=3.6.1,<3.7.0` | |
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| **Default Pipeline** | `tok2vec`, `ner` | |
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| **Components** | `tok2vec`, `ner` | |
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| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | |
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| **Sources** | n/a | |
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| **License** | n/a | |
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| **Author** | [n/a]() | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (7 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `ALCOHOL`, `ALDEHYDE`, `ALKANE`, `ALKENE`, `ALKYNE`, `C_ACID`, `KETONE` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `ENTS_F` | 97.70 | |
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| `ENTS_P` | 99.07 | |
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| `ENTS_R` | 96.36 | |
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| `TOK2VEC_LOSS` | 151.95 | |
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| `NER_LOSS` | 259.22 | |
