Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
pipeline_tag: zero-shot-classification
|
3 |
+
---
|
4 |
+
# Install transformers library
|
5 |
+
!pip install transformers
|
6 |
+
|
7 |
+
# Import necessary libraries
|
8 |
+
import pandas as pd
|
9 |
+
from transformers import pipeline
|
10 |
+
from google.colab import files
|
11 |
+
|
12 |
+
# Upload the file
|
13 |
+
uploaded = files.upload()
|
14 |
+
|
15 |
+
# Load the dataset
|
16 |
+
file_name = 'publications.csv' # Use the file name as uploaded
|
17 |
+
df = pd.read_csv(file_name)
|
18 |
+
|
19 |
+
# Display the first few rows of the dataset
|
20 |
+
df.head()
|
21 |
+
|
22 |
+
# Load the zero-shot classification pipeline
|
23 |
+
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
|
24 |
+
|
25 |
+
# Define the categories
|
26 |
+
candidate_labels = ["World", "Sports", "Business", "Science and Technology", "Entertainment", "Lifestyle"]
|
27 |
+
|
28 |
+
# Classify each news article
|
29 |
+
results = []
|
30 |
+
|
31 |
+
for article in df['headline']:
|
32 |
+
result = classifier(article, candidate_labels)
|
33 |
+
category = result['labels'][0]
|
34 |
+
results.append(category)
|
35 |
+
|
36 |
+
# Add the results to the DataFrame
|
37 |
+
df['category'] = results
|
38 |
+
|
39 |
+
# Display the categorized DataFrame
|
40 |
+
df.head(10)
|