Welcome to Bunka Documentation

Welcome to the documentation for Bunka, an advanced library for natural language processing and data visualization.

Bunkatopics is a package designed for Topic Modeling Visualization, Frame Analysis, and Retrieval Augmented Generation (RAG) tasks, harnessing the power of Large Language Models (LLMs). Its primary goal is to assist developers in gaining insights from unstructured data, potentially facilitating data cleansing and optimizing LLMs through fine-tuning processes. Bunkatopics is constructed using well-known libraries like langchain, chroma, and transformers, enabling seamless integration into various environments.

Discover the different Use Case:

• Content Overview: The Medium website offers a wealth of content across various categories such as Data Science, Technology, Programming, Poetry, Cryptocurrency, Machine Learning, Life, and more. While these categories facilitate data searching, they may not provide a granular overview. For instance, within the Technology category, what specific topics does Medium cover?

• Fine-Tuning: To achieve precise fine-tuning, it's crucial to exercise control over the data, filtering what is relevant and discarding what isn't. Bunka is a valuable tool for accomplishing this task.

• Framing Analysis: Data can be analyzed in countless ways, contingent on your objectives and interests. We've developed a tool that enables you to visualize data by semantically customizing your own axes.

Discover different examples using our Google Colab Notebooks

Theme	Google Colab Link
Visual Topic Modeling with Bunka
Cleaning dataset for fine-tuning LLM using Bunka
Understanding a dataset using Frame Analysis with Bunka
Full Introduction to Topic Modeling, Data Cleaning and Frame Analysis with Bunka.

Installation via Pip

pip install bunkatopics

Installation via Git Clone

git clone https://github.com/charlesdedampierre/BunkaTopics.git
cd BunkaTopics
pip install -e .

Quick Start

Uploading Sample Data

To get started, let's upload a sample of Medium Articles into Bunkatopics:

from datasets import load_dataset
docs = load_dataset("bunkalab/medium-sample-technology")["train"]["title"] # 'docs' is a list of text [text1, text2, ..., textN]

Choose Your Embedding Model

Bunkatopics offers seamless integration with Huggingface's extensive collection of embedding models. You can select from a wide range of models, but be mindful of their size. Please refer to the langchain documentation for details on available models.

from bunkatopics import Bunka
from langchain_community.embeddings import HuggingFaceEmbeddings


embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2",# We recommend starting with a small model
                                        model_kwargs={"device": "cpu"}, # Or cuda if you have GPU
                                        encode_kwargs={"show_progress_bar": True}, # Show the progress of embeddings
                                        multi_process=False)  # set to True if you have mutliprocessing

# Initialize Bunka with your chosen model and language preference
bunka = Bunka(embedding_model=embedding_model, language='english') # You can choose any language you prefer

# Fit Bunka to your text data
bunka.fit(docs)

>>> bunka.get_topics(n_clusters=15, name_length=5)# Specify the number of terms to describe each topic

Topics are described by the most specific terms belonging to the cluster.

topic_id	topic_name	size	percent
bt-12	technology - Tech - Children - student - days	322	10.73
bt-11	blockchain - Cryptocurrency - sense - Cryptocurrencies - Impact	283	9.43
bt-7	gadgets - phone - Device - specifications - screen	258	8.6
bt-8	software - Kubernetes - ETL - REST - Salesforce	258	8.6
bt-1	hackathon - review - Recap - Predictions - Lessons	257	8.57
bt-4	Reality - world - cities - future - Lot	246	8.2
bt-14	Product - Sales - day - dream - routine	241	8.03
bt-0	Words - Robots - discount - NordVPN - humans	208	6.93
bt-2	Internet - Overview - security - Work - Development	202	6.73
bt-13	Course - Difference - Step - science - Point	192	6.4
bt-6	quantum - Cars - Way - Game - quest	162	5.4
bt-3	Objects - Strings - app - Programming - Functions	119	3.97
bt-5	supply - chain - revolution - Risk - community	119	3.97
bt-9	COVID - printing - Car - work - app	89	2.97
bt-10	Episode - HD - Secrets - TV	44	1.47

Visualize Your Topics

Finally, let's visualize the topics that Bunka has computed for your text data:

>>> bunka.visualize_topics(width=800, height=800, colorscale='YIGnBu')

Topic Modeling with GenAI Summarization of Topics

Explore the power of Generative AI for summarizing topics! We use the 7B-instruct model of Mistral AI from the huggingface hub using the langchain framework.

from langchain.llms import HuggingFaceHub

# Define the repository ID for Mistral-7B-v0.1
repo_id = 'mistralai/Mistral-7B-v0.1'

# Using Mistral AI to Summarize the Topics
llm = HuggingFaceHub(repo_id='mistralai/Mistral-7B-v0.1', huggingfacehub_api_token="HF_TOKEN")

# Obtain clean topic names using Generative Model
bunka.get_clean_topic_name(generative_model=llm, language='english')
bunka.visualize_topics( width=800, height=800, colorscale = 'Portland')

Finally, let's visualize again the topics. We can chose from different colorscales.

>>> bunka.visualize_topics(width=800, height=800)

YlGnBu	Portland

delta	Blues

We can now access the newly made topics

>>> bunka.df_topics_

topic_id	topic_name	size	percent
bt-1	Cryptocurrency Impact	345	12.32
bt-3	Data Management Technologies	243	8.68
bt-14	Everyday Life	230	8.21
bt-0	Digital Learning Campaign	225	8.04
bt-12	Business Development	223	7.96
bt-2	Technology Devices	212	7.57
bt-10	Market Predictions Recap	201	7.18
bt-4	Comprehensive Learning Journey	187	6.68
bt-6	Future of Work	185	6.61
bt-11	Internet Discounts	175	6.25
bt-5	Technological Urban Water Management	172	6.14
bt-9	Electric Vehicle Technology	145	5.18
bt-8	Programming Concepts	116	4.14
bt-13	Quantum Technology Industries	105	3.75
bt-7	High Definition Television (HDTV)	36	1.29

Manually Cleaning the topics

If you are not happy with the resulting topics, you can change them manually. Click on Apply changes when you are done. In the example, we changed the topic Cryptocurrency Impact to Cryptocurrency and Internet Discounts to Advertising.

>>> bunka.manually_clean_topics()

Removing Data based on topics for fine-tuning purposes

You have the flexibility to construct a customized dataset by excluding topics that do not align with your interests. For instance, in the provided example, we omitted topics associated with Advertising and High-Definition television, as these clusters primarily contain promotional content that we prefer not to include in our model's training data.

>>> bunka.clean_data_by_topics()

>>> bunka.df_cleaned_

doc_id	content	topic_id	topic_name
873ba315	Invisibilize Data With JavaScript	bt-8	Programming Concepts
1243d58f	Why End-to-End Testing is Important for Your Team	bt-3	Data Management Technologies
45fb8166	This Tiny Wearable Device Uses Your Body Heat...	bt-2	Technology Devices
a122d1d2	Digital Policy Salon: The Next Frontier	bt-0	Digital Learning Campaign
1bbcfc1c	Preparing Hardware for Outdoor Creative Technology Installations	bt-5	Technological Urban Water Management
79580c34	Angular Or React ?	bt-8	Programming Concepts
af0b08a2	Ed-Tech Startups Are Cashing in on Parents’ Insecurities	bt-0	Digital Learning Campaign
2255c350	Former Google CEO Wants to Create a Government-Funded University to Train A.I. Coders	bt-6	Future of Work
d2bc4b33	Applying Action & The Importance of Ideas	bt-12	Business Development
5219675e	Why You Should (not?) Use Signal	bt-2	Technology Devices
...	...	...	...

Bourdieu Map

The Bourdieu map provides a 2-Dimensional unsupervised scale to visualize various texts. Each region on the map represents a distinct topic, characterized by its most specific terms. Clusters are formed, and their names are succinctly summarized using Generative AI.

The significance of this visualization lies in its ability to define axes, thereby creating continuums that reveal data distribution patterns. This concept draws inspiration from the work of the renowned French sociologist Bourdieu, who employed 2-Dimensional maps to project items and gain insights.

from langchain.llms import HuggingFaceHub

# Define the HuggingFaceHub instance with the repository ID and API token
llm = HuggingFaceHub(
    repo_id='mistralai/Mistral-7B-v0.1',
    huggingfacehub_api_token="HF_TOKEN"
)

## Bourdieu Fig
bourdieu_fig = bunka.visualize_bourdieu(
        llm=llm,
        x_left_words=["This is about business"],
        x_right_words=["This is about politics"],
        y_top_words=["this is about startups"],
        y_bottom_words=["This is about governments"],
        height=800,
        width=800,
        clustering=True,
        topic_n_clusters=10,
        density=False,
        convex_hull=True,
        radius_size=0.2,
        label_size_ratio_clusters=80)

>>> bourdieu_fig.show()

positive/negative vs humans/machines	politics/business vs humans/machines

politics/business vs positive/negative	politics/business vs startups/governments

Front-end

This is a beta feature. First, git clone the repository

git clone https://github.com/charlesdedampierre/BunkaTopics.git
cd BunkaTopics
pip install -e .

Then carry out a Topic Modeling and launch the serveur:

from bunkatopics import Bunka
from langchain_community.embeddings import HuggingFaceEmbeddings

# Choose your embedding model
embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") # We recommend starting with a small model

# Initialize Bunka with your chosen model and language preference
bunka = Bunka(embedding_model=embedding_model, language='english') # You can choose any language you prefer

# Fit Bunka to your text data
bunka.fit(docs)
bunka.get_topics(n_clusters=15, name_length=3)# Specify the number of terms to describe each topic

>>> bunka.start_server() # A serveur will open on your computer at http://localhost:3000/