https://github.com/asrot0/spacy_ner
SpaCy-based NER🧠 implementation for extracting and classifying entities from text✨
https://github.com/asrot0/spacy_ner
machine-learning ner nlp spacy textclassification
Last synced: 2 months ago
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SpaCy-based NER🧠 implementation for extracting and classifying entities from text✨
- Host: GitHub
- URL: https://github.com/asrot0/spacy_ner
- Owner: asRot0
- Created: 2025-02-15T12:40:49.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-15T13:14:57.000Z (3 months ago)
- Last Synced: 2025-02-15T13:34:45.953Z (3 months ago)
- Topics: machine-learning, ner, nlp, spacy, textclassification
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚀 Named Entity Recognition (NER) with spaCy
This project performs **Named Entity Recognition (NER)** using **spaCy**, web scrapes text from Wikipedia, and visualizes extracted entities using **displaCy**, Matplotlib, and Rich tables.
---
## 📌 Features
- 🔹 **NER on Sample Text** using `en_core_web_sm`
- 🔹 **Web Scraping Wikipedia** for real-world text
- 🔹 **Organizing Extracted Entities** into a dictionary
- 🔹 **Visualizing Entity Frequencies** with Matplotlib
- 🔹 **Beautiful Rich Table Output** for structured display
- 🔹 **Entity Rendering with displaCy** (see below)
---
## 📥 Installation
```bash
pip install spacy beautifulsoup4 requests matplotlib rich
python -m spacy download en_core_web_sm
```
## 📝 Code Overview
### 1️⃣ Load Pre-trained Model & Run NER
```bash
import spacy
NER = spacy.load("en_core_web_sm")
text = "Apple is looking at buying a U.K. startup for $1 billion."
doc = NER(text)
for ent in doc.ents:
print(f"{ent.text} -> {ent.label_}")
```
### 2️⃣ Web Scrape a Wikipedia Article
```bash
import requests
from bs4 import BeautifulSoup
url = 'https://en.wikipedia.org/wiki/Wikipedia'
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
article_text = " ".join([p.text for p in soup.find_all("p")])
```
### 3️⃣ Perform NER on Scraped Text
```bash
doc = NER(article_text)
```
### 4️⃣ Visualize Named Entities with displaCy
```bash
from spacy import displacy
displacy.render(doc, style='ent', jupyter=True)
```
### 5️⃣ Entity Frequency Visualization
```bash
import matplotlib.pyplot as plt
entity_counts = {key: len(set(value)) for key, value in entity_dict.items()}
plt.figure(figsize=(10, 5))
plt.bar(entity_counts.keys(), entity_counts.values(), color='cornflowerblue')
plt.xlabel("Entity Type", fontsize=12, fontweight='bold')
plt.ylabel("Count", fontsize=12, fontweight='bold')
plt.title("NER Distribution", fontsize=14, fontweight='bold')
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
```
### 6️⃣ Rich Table Output
```bash
from rich.console import Console
from rich.table import Table
console = Console()
table = Table(title="🔵 Named Entity Recognition Results", title_style="bold cyan")
table.add_column("🔹 Entity Type", style="bold deep_sky_blue3", justify="center")
table.add_column("🔸 Entities", style="bold light_slate_grey", justify="left")
for entity_type, values in entity_dict.items():
table.add_row(f"[bold bright_white]{entity_type}[/bold bright_white]", f"[italic dark_sea_green3]{', '.join(set(values))}[/italic dark_sea_green3]")
console.print(table)
```
### 🎨 displaCy Visualization Example
displaCy is a built-in visualization tool in spaCy that renders named entities in a user-friendly format.
`Apple` **ORG** is looking at buying a `U.K.` **GPE** startup for `$1 billion` **MONEY**.
#### 🔗 Try it in Jupyter Notebook with:
```bash
displacy.render(doc, style='ent', jupyter=True)
```