Update README.md

README.md

@@ -63,23 +63,11 @@ Custom-trained for high accuracy in recognizing "profession," "facility," and "e
 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 |
-| --- | --- |
-| **Name** | `en_ner_job_postings` |
-| **Version** | `3.6.0` |
-| **spaCy** | `>=3.6.0,<3.7.0` |
-| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
-| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
-| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
-| **License** | `MIT` |
-
-
-
 ### Usage
 #### Installation
 ##### You can install the custom spaCy NER model using pip:
 
-```python
+```bash
 pip install https://huggingface.co/DaFull/en_ner_job_postings/resolve/main/en_ner_job_postings-any-py3-none-any.whl
 
 ```
@@ -110,6 +98,17 @@ The model recognizes the following entity types:
 - FACILITY: Denotes facilities, buildings, or locations.
 - EXPERIENCE: Identifies mentions of work experience, durations, or qualifications.
 
+| Feature | Description |
+| --- | --- |
+| **Name** | `en_ner_job_postings` |
+| **Version** | `3.6.0` |
+| **spaCy** | `>=3.6.0,<3.7.0` |
+| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
+| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
+| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
+| **License** | `MIT` |
+
+
 ### Label Scheme
 
 <details>