Day: November 9, 2024

Decorating for the holidays can include more than putting up a tree and adding tinsel. Decorating can extend to the digital space as well with great Christmas desktop backgrounds and wallpapers.

We’ve collected dozens of great Christmas desktop backgrounds, so you can change your screen every day during the holiday season if you like. While many of these desktop backgrounds and wallpapers are free, others can be found using stock image services or graphic design asset tools or marketplaces. Happy holidays!

Frozen Grunge Christmas Backgrounds

Get into the holiday spirit with this creative frozen grunge Christmas background collection! This unique collection features 10 duotone frozen grunge backgrounds, each in high-resolution JPEG format (5000 x 3500px, 300dpi) perfect for any project.

Frozen Snowflakes Backgrounds

Experience the majestic essence of winter with this Christmas-themed snowflake background collection. This beautifully curated set features ten high-quality, 5000 x 3500px JPEG images, each showcasing a unique snowflake design trapped in icy majesty.

Christmas Bokeh Christmas Backgrounds

A charming collection of Christmas backgrounds with soft-focus winter scenes. This package offers 10 unique designs in a JPEG format, boasting a high resolution of 5000 x 3500px at 300 dpi. Drift into the festive spirit and add a touch of cozy winter sparkle to your projects with this bokeh-inspired set.

Icy Winter Christmas Backgrounds

A captivating array of 10 digitally crafted Christmas backgrounds with icy scenes. It offers a cool, icy take on the typical holiday ambiance with its frozen winter themes. Each high-quality JPEG (5000 x 3500px, 300dpi) transports you to a frosty wonderland, making it a perfect backdrop for various Christmas-themed projects or holiday-inspired aesthetics.

Snowy and Frosty Christmas Backgrounds

A collection of Christmas backgrounds that captures the mystique of winter. Its high-resolution, radiant gradient of frosty textures against a sparkling white snow pattern brings a winter wonderland to life. Versatile and print-ready, this quality backdrop offers pixel dimensions of 3000 x 2000 at 72 dpi.

Magical Christmas Snow Backgrounds

An enchanting set of four high-resolution (4096 x 2304 at 300dpi) Christmas backgrounds, perfect for ushering in the holiday spirit. Each featuring a festive, magical snow design, these backgrounds are versatile and ideal for both web and print media.

3D Christmas and New Years Backgrounds

This is a creative Christmas background that offers a ready-to-use, high-quality, 3D design of a festive scene complete with gift-wrapped boxes under a tree. This 4300 x 2750 px dimension, high-resolution (300 DPI) asset is perfect for enhancing your personal or commercial projects- whether it’s on your website, social media, greeting cards, or promotional materials.

Christmas Trees Backgrounds

A captivating collection of three abstract holiday-themed Christmas backgrounds. These high-quality JPEG files offer a sparkling x-mas tree design in 300 dpi, sized at 3840×2160 pixels. They’re versatile to use, making great additions to your prints, wallpapers, or brochures, bringing a merry touch to your festive season.

Christmas Holiday Backgrounds

This is a delightful collection of four uniquely Christmas background images reflecting the magic and joy of the season. Filled with elements like gifts, fir, snow, gingerbread, and golden decorations, these backgrounds are perfect for adding a festive touch to your website, social media, or blog.

3D Christmas Background with Ornaments

This is a festive, virtual stage to create Christmas backgrounds in a jolly spirit. This 3D asset features a lively, green backdrop adorned with classic Christmas ornaments and balls, and even includes a podium for showcasing your items. It’s the perfect 3D-rendered scene to display your products wrapped in holiday charm.

Free Christmas Backgrounds

Pine & Berries

A beautiful desktop depiction of pine, berries, and mistletoe that still leaves plenty of room for icons on your computer!

Red Christmas

A bold, red Christmas wallpaper that really puts you in the festive mood.

Minimal Christmas Decorations

A beautiful collage of Christmas decorations with a plain background. Stylish and minimal!

Christmas Tree Close-Up Wallpaper

Forget the traditional Christmas tree form, this is a close-up texture-style wallpaper of a holiday classic.

Bringing the Christmas Tree Home

A beautiful tilt-shift image that shows off a Christmas tree on top of a camper van. At a novel scale!

Christmas Garland Desktop

A string of holiday decorations set against a red background.

Star Pattern + Blank Space

A red string and a collection of star decorations, set against a plain dark background.

Single Christmas Ornament

Just a single Christmas ornament, with a stylish depth of field. Wallpaper simplicity!

Snowflake Macro

A super-close-up of one of the most beautiful things in nature —  a snowflake. Perfect for your desktop wallpaper.

Mini Christmas Scene

A beautiful little Christmas scene for your desktop wallpaper, with a few Christmas trees and a festive church!

Minimal Snowy Tree

A simple snowy tree – even undecorated – can get you in the holiday spirit. This Christmas desktop background is simple and leaves plenty of room for you to actually see desktop icons.

Santa Flying

The artistic style of this Santa flying through the sky with his reindeer is a grown-up interpretation of the childhood scene. The dark style is beautiful and not too busy for a desktop wallpaper.

Christmas Tree

If asymmetrical balance is your style, this Christmas tree wallpaper features a fun style with lights on a dark background.

Blue Christmas

Not every Christmas decoration has to be red and green. This simple blue holiday ornament emanates holiday sparkle.

Gift Delivery

Gift Delivery is a fun representation of how much time it seems to take for the holiday season to arrive. The desktop wallpaper is sure to be a conversation starts if anyone glimpses your screen.

Christmas Letters

This Christmas desktop background should appeal to all the wordsmiths out there.

Snowman Wallpaper

Not every Christmas desktop background has to be just for the holiday, a fun winter scene – such as this lifelike snowman – can be equally nice.

Christmas Garland

Simple garland on a wood background is eye-appealing and features just a hint of the holiday season for your desktop wallpaper.

Snowman

This cool snowman desktop wallpaper features a fun design with plenty of space around it. Great for people that prefer a background without too much going on.

Christmas Volcano

An explosion of gifts is every child’s dream. This representation shows exactly that.

Happy Holidays Sign

The texture of this Christmas wall paper is nice and provides plenty of room for visible icons on the screen. It has a bit of a trendy and retro vibe.

Christmas Lights

Move a design trend to a holiday trend. This gradient Christmas desktop wallpaper includes fun color, holiday snowflakes and a cheerful design.

Holiday Glitter

A characteristic of the season is certainly sparkle. This glitter desktop background fits the bill.

Happy New Year

Here’s a little holiday greeting for the devs. Happy New Year is a fun desktop wallpaper to end the holiday season.

In one of my previous articles, you saw a comparison of GPT-4o vs. Claude 3.5 sonnet for zero-shot text classification. In that article; we performed multi-class text classification where input tweets belonged to one of the three categories.

In this article, we will go a step further and perform zero-shot multi-label text classification with GPT-4o and Claude 3.5 sonnet models. We will compare the two models using accuracy and hamming loss criteria and see which model is suited for zero-shot multi-label text classification.

So, let's begin without ado.

We will call the Claude 3.5 sonnet and GPT-4o models using the Anthropic and OpenAI Python libraries.

The following script installs these libraries.

!pip install anthropic
!pip install openai

The script below imports the required libraries into your Python application.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from itertools import combinations
from collections import Counter
from sklearn.metrics import hamming_loss, accuracy_score

import anthropic
from openai import OpenAI

from google.colab import userdata
OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')
ANTHROPIC_API_KEY = userdata.get('ANTHROPIC_API_KEY')

We will use a multi-label text classification dataset from Kaggle containing research paper titles and abstracts that can belong to one or more of the six output categories: Computer Science, Physics, Mathematics, Statistics, Quantitative Biology, and Quantitative Finance.

The following script imports the training set from the dataset and plots the dataset header.

## dataset download link
## https://www.kaggle.com/datasets/shivanandmn/multilabel-classification-dataset?select=train.csv

dataset = pd.read_csv("/content/train.csv")
print(f"Dataset Shape: {dataset.shape}")
dataset.head()

Output:

The dataset consists of 9 columns. The ID column contains the paper ID. The TITLE and ABSTRACT columns contain research papers' titles and abstracts, respectively. In the rest of the columns, a one signifies that the paper belongs to this category. A zero means that the paper does not belong to this category.

Let's filter papers that belong to at least two categories since we want to perform multi-label classification.

subjects = ["Computer Science", "Physics", "Mathematics", "Statistics", "Quantitative Biology", "Quantitative Finance"]
filtered_dataset = dataset[(dataset[subjects] == 1).sum(axis=1) >= 2]
print(f"Filtered Dataset Shape: {filtered_dataset.shape}")
filtered_dataset.head()

Output:

Next, we will plot a heatmap that shows the co-occurrences of research papers in various categories.

filtered_dataset[subjects] = filtered_dataset[subjects].astype(int)

pair_counts = {pair: 0 for pair in combinations(subjects, 2)}

# Count occurrences where both subjects in each pair have a value of 1
for subject1, subject2 in pair_counts:
    pair_counts[(subject1, subject2)] = ((filtered_dataset[subject1] == 1) & (filtered_dataset[subject2] == 1)).sum()

# Convert the pair counts to a DataFrame suitable for a heatmap
pair_counts_df = pd.DataFrame(0, index=subjects, columns=subjects)
for (subject1, subject2), count in pair_counts.items():
    pair_counts_df.loc[subject1, subject2] = count
    pair_counts_df.loc[subject2, subject1] = count  # Ensure symmetry

# Plotting the heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(pair_counts_df,
            annot=True,
            fmt="d",
            cmap="Blues",
            square=True,
            cbar=True)

plt.title("Pairing Count of Columns with Both Values as 1")
plt.show()

Output:

The above output shows that most research papers in the dataset are grouped in the Computer ScienceStatistics and MathematicsStatistics categories.

In the next section, we will use the Claude 3.5 Sonnet and GPT-4o models to classify the research papers in the dataset using multi-label classification.

We will define the find_research_category() method that takes as parameters the Anthropic or OpenAI API client, the model name from the API, and the dataset.

Inside the method, we iterate through all the rows and extract the paper titles and abstracts. Next, we will write a prompt that tells the models that using the research paper title and abstract, they have to classify the paper into two or more of the predefined subject categories.

Next, depending on the model type, we send the prompt to Anthropic or OpenAI and retrieve the comma-separated list of subject types for the research paper.

def find_research_category(client, model, dataset):

    outputs = []
    i = 0

    for _, row in sampled_df.iterrows():
        title = row['TITLE']
        abstract = row['ABSTRACT']

        content = """You are an expert in various scientific domains.
                     Given the following research paper title and abstract, classify the research paper into at least two or more of the following categories:
                    - Computer Science
                    - Physics
                    - Mathematics
                    - Statistics
                    - Quantitative Biology
                    - Quantitative Finance

                    Return only a comma-separated list of the categories (e.g., [Computer Science,Physics] or [Computer Science,Physics,Mathematics]).
                    Use the exact case sensitivity and spelling of the categories provided above.

                    text: Title: {}\nAbstract: {}""".format(title, abstract)


        research_category = ""

        if model == "gpt-4o":

          research_category = client.chat.completions.create(
                                model= model,
                                temperature = 0,
                                max_tokens = 100,
                                messages=[
                                      {"role": "user", "content": content}
                                  ]
                              ).choices[0].message.content


        if model == "claude-3-5-sonnet-20241022":

          research_category = client.messages.create(
                                model= model,
                                max_tokens=10,
                                temperature=0.0,
                                messages=[
                                    {"role": "user", "content": content}
                                ]
                            ).content[0].text

        outputs.append(research_category)
        print(i + 1, research_category)
        i += 1

    return outputs

The response from the find_research_category() method is a list of lists where each internal list contains subject categories. We will convert these subject categories into a Pandas dataframe to compare the model outputs with target labels.

def parse_outputs_to_dataframe(outputs):

    subjects = ["Computer Science", "Physics", "Mathematics", "Statistics", "Quantitative Biology", "Quantitative Finance"]
    # Remove square brackets and split the subjects for each entry in outputs
    parsed_data = [item.strip('[]').split(',') for item in outputs]

    # Create an empty DataFrame with columns for each subject, initializing with 0s
    df = pd.DataFrame(0, index=range(len(parsed_data)), columns=subjects)

    # Populate the DataFrame with 1s based on the presence of each subject in each row
    for i, subjects_list in enumerate(parsed_data):
        for subject in subjects_list:
            if subject in subjects:
                df.loc[i, subject] = 1

    return df

We will randomly select the first 100 records from the dataset and use these records to make predictions. You can select more records. However, it can cost more since the OpenAI and Claude APIs are not free.

sampled_df = filtered_dataset.sample(n=100, random_state=42)

Let's first do multi-label classification with GPT-4o. To do so, we will create an OpenAI client object and pass it the OpenAI API key.

We will pass the client object and the gpt-4o model ID to the find_research_category() method, which returns model predictions.

client = OpenAI(api_key = OPENAI_API_KEY,)
model = "gpt-4o"

outputs = find_research_category(client, model, sampled_df)

Output:

We will convert model predictions into Pandas dataframe using the parse_outputs_to_dataframe() method.

Finally, we calculate the hamming loss and model accuracy using the hamming_loss and accuracy_score functions from the sklearn library.

predictions = parse_outputs_to_dataframe(outputs)
targets = sampled_df[subjects]

# Calculate Hamming Loss
hamming = hamming_loss(targets, predictions)
print(f"Hamming Loss: {hamming}")

# Calculate Subset Accuracy (Exact Match Ratio)
subset_accuracy = accuracy_score(targets, predictions)
print(f"Subset Accuracy: {subset_accuracy}")

Output:

Hamming Loss: 0.16
Subset Accuracy: 0.4

We achieved a hamming loss of 0.16, which shows that 16% of labels for each record were incorrectly predicted. The lower the hamming loss, the better the model performs.

Similarly, we achieved an accuracy of 40%, which means that the model correctly predicted all the categories in 40% of the records.

Next, we will make predictions with the Claude 3.5 sonnet. We will create an object of the Anthropic class and pass it the Anthropic API key.

To retrieve model responses, we pass the Anthropic client and the Claude 3.5 sonnet model ID to the find_research_category() method.

client = anthropic.Anthropic(api_key = ANTHROPIC_API_KEY)
model = "claude-3-5-sonnet-20241022"

outputs = find_research_category(client, model, sampled_df)

Output:

Next, we parse the model outputs to a dataframe using the parse_outputs_to_dataframe() method and print the hamming loss and model accuracy for the Claude 3.5 sonnet model.

predictions = parse_outputs_to_dataframe(outputs)
targets = sampled_df[subjects]

# Calculate Hamming Loss
hamming = hamming_loss(targets, predictions)
print(f"Hamming Loss: {hamming}")

# Calculate Subset Accuracy (Exact Match Ratio)
subset_accuracy = accuracy_score(targets, predictions)
print(f"Subset Accuracy: {subset_accuracy}")

Output:

Hamming Loss: 0.17166666666666666
Subset Accuracy: 0.29

The output shows that the Claude 3.5 sonnet model performs slightly better than GPT-4o regarding hamming loss, which means that the Claude model predicted fewer labels incorrectly. However, Claude returned an accuracy of 29% for an exact label match, significantly less than GPT-4o.

Claude 3.5 sonnet and OpenAI GPT-4o models achieve brilliant zero-shot multi-label text classification results. In this article, you saw how to use these models for multi-label text classification and convert the model predictions into Pandas dataframe to evaluate model performance.

The results show that while Claude 3.5 makes fewer mistakes for predicted individual labels, the GPT-4o is significantly better if you look for an exact multi-label match for your input text.