Update README.md

README.md

@@ -21,8 +21,29 @@ model-index:
       type: f_score
       value: 0.9769585253
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-- text: "Cinammaldehyde is a fragrant compound found in cinammon. Arachidic acid, also known as icosanoic acid, is a saturated fatty acid with a 20-carbon chain. Triptane is commonly used as an anti-knock additive in aviation fuels. Benzophenone is a widely used building block in organic chemistry, being the parent diarylketone. Geraniol is a monoterpenoid and an alcohol. It is the primary component of citronella oil and is a primary component of rose oil, palmarosa oil."
+- text: "Cinammaldehyde is a fragrant compound found in cinammon. Icosanoic acid, is a saturated fatty acid with a 20-carbon chain. Triptane is commonly used as an anti-knock additive in aviation fuels. Benzophenone is a widely used building block in organic chemistry, being the parent diarylketone. Geraniol is a monoterpenoid and an alcohol. It is the primary component of citronella oil and is a primary component of rose oil, palmarosa oil."
 ---
+# 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` |