Update README.md

README.md

@@ -2,6 +2,7 @@
 tags:
 - spacy
 - token-classification
+- ner
 language:
 - en
 license: mit
@@ -49,8 +50,19 @@ model-index:
     - name: Sentences F-Score
       type: f_score
       value: 0.907098331
+library_name: spacy
+pipeline_tag: text-classification
 ---
-English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
+## Custom spaCy NER Model for "Profession," "Facility," and "Experience" Entities
+
+#### Overview
+### Description
+This spaCy-based Named Entity Recognition (NER) model has been custom-trained to recognize and classify entities related to "profession," "facility," and "experience." It is designed to enhance your text analysis capabilities by identifying these specific entity types in unstructured text data.
+
+### Key Features
+Custom-trained for high accuracy in recognizing "profession," "facility," and "experience" entities.
+Suitable for various NLP tasks, such as information extraction, content categorization, and more.
+Can be easily integrated into your existing spaCy-based NLP pipelines.
 
 | Feature | Description |
 | --- | --- |