[](https://pypi.org/project/bunkatopics/)
[](https://pypi.org/project/bunkatopics/)
[](https://pepy.tech/project/bunkatopics)
[](https://pepy.tech/project/bunkatopics)
Please read our Documentation: [The Origin of Bunka](https://charlesdedampierre.github.io/BunkaTopics)
# Bunkatopics
<img src="docs/images/logo.png" width="35%" height="35%" align="right" />
Bunkatopics is a package designed for Data Cleaning, Topic Modeling Visualization and Frame Analysis. Its primary goal is to assist developers in gaining insights from unstructured data, potentially facilitating data cleaning and optimizing LLMs through fine-tuning processes.
Bunkatopics is constructed using well-known libraries like sentence_transformers, langchain and transformers, enabling seamless integration into various environments.
Discover the different Use Case:
- **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.
- **Content Overview**: As an example, 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 exploration of data, they may not provide a granular overview. For instance, within the Technology category, what specific topics does Medium cover?
- **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 | [](https://colab.research.google.com/drive/11K5bKs4y6snEFEZhKFNuNVRRSAsq9b7p#scrollTo=uxQ5qCIVar1u) |
| Cleaning dataset for fine-tuning LLM using Bunka | [](https://colab.research.google.com/drive/1KedB5FqJ62sF7Gb5qSsA5kbeTeJDw2Mo#scrollTo=-bEv2hrvakW4) |
| Understanding a dataset using Frame Analysis with Bunka | [](https://colab.research.google.com/drive/14ubgffmKPiLBUdoV1j6EjfCnI4EFWxn4#scrollTo=7jHILnazA1Im) |
| Full Introduction to Topic Modeling, Data Cleaning and Frame Analysis with Bunka. | [](https://colab.research.google.com/drive/1M4xuJE782bDzVxDUmQv9SSQL0hdZIC8n#scrollTo=ySwyXC0bbVb8) |
## Installation via Pip
```bash
pip install bunkatopics
```
## Installation via Git Clone
```bash
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:
```python
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.
```python
# Load Embedding model
from sentence_transformers import SentenceTransformer
embedding_model = SentenceTransformer(model_name_or_path="all-MiniLM-L6-v2")
# Load Projection Model
import umap
projection_model = umap.UMAP(
n_components=2,
random_state=42)
from bunkatopics import Bunka
bunka = Bunka(embedding_model=embedding_model,
projection_model=projection_model) # the language is automatically detected, make sure the embedding model is adapted
# Fit Bunka to your text data
bunka.fit(docs)
```
```python
from sklearn.cluster import KMeans
clustering_model = KMeans(n_clusters=15)
>>> bunka.get_topics(name_length=5, custom_clustering_model=clustering_model)# 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:
```python
>>> bunka.visualize_topics(width=800, height=800, colorscale='YIGnBu')
```
<img src="docs/images/topic_modeling_raw_YlGnBu.png" width="70%" height="70%" align="center" />
## Topic Modeling with GenAI Summarization of Topics
Explore the power of Generative AI for summarizing topics!
```python
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key = 'OPEN_AI_KEY')
```
Note: It is recommended to use an Instruct model ie a model that has been fine-tuned on a discussion task. If not, the results might be meaningless.
```python
# Obtain clean topic names using Generative Model
bunka.get_clean_topic_name(generative_model=llm)
```
Check the top documents for every topic!
```python
>>> bunka.df_top_docs_per_topic_
```
Finally, let's visualize again the topics. We can chose from different colorscales.
```python
>>> bunka.visualize_topics(width=800, height=800)
```
YlGnBu | Portland
:-------------------------:|:-------------------------:
 | 
delta | Blues
:-------------------------:|:-------------------------:
 | 
We can now access the newly made topics
```python
>>> 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 |
## Visualise Dimensions on topics
```python
dataset = load_dataset("bunkalab/medium-sample-technology-tags")['train']
docs = list(dataset['title'])
ids = list(dataset['doc_id'])
tags = list(dataset['tags'])
metadata = {'tags':tags}
from bunkatopics import Bunka
bunka = Bunka()
# Fit Bunka to your text data
bunka.fit(docs=docs, ids=ids, metadata=metadata)
bunka.get_topics(n_clusters=10)
bunka.visualize_topics(color='tags', width=800, height=800) # Adjust the color
```
<img src="docs/images/bunka_color.png" width="70%" height="70%" align="center" />
## 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**.
```python
>>> bunka.manually_clean_topics()
```
<img src="docs/images/manually_change_topics.png" width="40%" height="20%" align="center" />
## 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.
```python
>>> bunka.clean_data_by_topics()
```
<img src="docs/images/fine_tuning_dataset.png" width="40%" height="20%" align="center" />
```python
>>> 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.
```python
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,
min_docs_per_cluster = 5,
label_size_ratio_clusters=80)
```
```python
>>> bourdieu_fig.show()
```
positive/negative vs humans/machines | politics/business vs humans/machines
:-------------------------:|:-------------------------:
 | 
politics/business vs positive/negative | politics/business vs startups/governments
:-------------------------:|:-------------------------:
 | 
## Saving and loading Bunka
```python
bunka.save_bunka("bunka_dump")
...
from bunkatopics import Bunka
bunka = Bunka().load_bunka("bunka_dump")
>>> bunka.get_topics(n_clusters = 15)
```
## Loading customed embeddings (Beta)
```python
'''
ids = ['doc_1', 'doc_2'...., 'doc_n']
embeddings = [[0.05121125280857086,
-0.03985324501991272,
-0.05017390474677086,
-0.03173152357339859,
-0.07367539405822754,
0.0331297293305397,
-0.00685789855197072...]]
'''
pre_computed_embeddings = [{'doc_id': doc_id, 'embedding': embedding} for doc_id, embedding in zip(ids, embeddings)]
...
from bunkatopics import Bunka
bunka = Bunka()
bunka.fit(docs=docs, ids = ids, pre_computed_embeddings = pre_computed_embeddings)
from sklearn.cluster import KMeans
clustering_model = KMeans(n_clusters=15)
>>> bunka.get_topics(name_length=5,
custom_clustering_model=clustering_model)# Specify the number of terms to describe each topic
```
## Front-end (Beta)
This is a beta feature. First, git clone the repository
```bash
git clone https://github.com/charlesdedampierre/BunkaTopics.git
cd BunkaTopics
pip install -e .
cd web # got the web directory
npm install # install the needed React packages
```
```python
from bunkatopics import Bunka
from sentence_transformers import SentenceTransformer
embedding_model = SentenceTransformer(model_name_or_path="all-MiniLM-L6-v2")
# Initialize Bunka with your chosen model
bunka = Bunka(embedding_model=embedding_model)
# 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
```
```python
>>> bunka.start_server() # A serveur will open on your computer at http://localhost:3000/
```
<img src="docs/images/bunka_server.png" width="100%" height="100%" align="center" />
Raw data
{
"_id": null,
"home_page": "https://github.com/charlesdedampierre/BunkaTopics",
"name": "bunkatopics",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "AI Topic Modeling Visualization Exploration Fine-tuning",
"author": "Charles de Dampierre",
"author_email": "charlesdedampierre@gmail.com",
"download_url": null,
"platform": null,
"description": "[](https://pypi.org/project/bunkatopics/)\n[](https://pypi.org/project/bunkatopics/)\n[](https://pepy.tech/project/bunkatopics)\n[](https://pepy.tech/project/bunkatopics)\n\nPlease read our Documentation: [The Origin of Bunka](https://charlesdedampierre.github.io/BunkaTopics)\n\n# Bunkatopics\n\n<img src=\"docs/images/logo.png\" width=\"35%\" height=\"35%\" align=\"right\" />\n\nBunkatopics is a package designed for Data Cleaning, Topic Modeling Visualization and Frame Analysis. Its primary goal is to assist developers in gaining insights from unstructured data, potentially facilitating data cleaning and optimizing LLMs through fine-tuning processes.\nBunkatopics is constructed using well-known libraries like sentence_transformers, langchain and transformers, enabling seamless integration into various environments.\n\nDiscover the different Use Case:\n\n- **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.\n\n- **Content Overview**: As an example, 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 exploration of data, they may not provide a granular overview. For instance, within the Technology category, what specific topics does Medium cover?\n\n- **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.\n\n## Discover different examples using our Google Colab Notebooks\n\n| Theme | Google Colab Link |\n|---|---|\n| Visual Topic Modeling with Bunka | [](https://colab.research.google.com/drive/11K5bKs4y6snEFEZhKFNuNVRRSAsq9b7p#scrollTo=uxQ5qCIVar1u) |\n| Cleaning dataset for fine-tuning LLM using Bunka | [](https://colab.research.google.com/drive/1KedB5FqJ62sF7Gb5qSsA5kbeTeJDw2Mo#scrollTo=-bEv2hrvakW4) |\n| Understanding a dataset using Frame Analysis with Bunka | [](https://colab.research.google.com/drive/14ubgffmKPiLBUdoV1j6EjfCnI4EFWxn4#scrollTo=7jHILnazA1Im) |\n| Full Introduction to Topic Modeling, Data Cleaning and Frame Analysis with Bunka. | [](https://colab.research.google.com/drive/1M4xuJE782bDzVxDUmQv9SSQL0hdZIC8n#scrollTo=ySwyXC0bbVb8) |\n\n## Installation via Pip\n\n```bash\npip install bunkatopics\n```\n\n## Installation via Git Clone\n\n```bash\ngit clone https://github.com/charlesdedampierre/BunkaTopics.git\ncd BunkaTopics\npip install -e .\n```\n\n## Quick Start\n\n### Uploading Sample Data\n\nTo get started, let's upload a sample of Medium Articles into Bunkatopics:\n\n```python\nfrom datasets import load_dataset\ndocs = load_dataset(\"bunkalab/medium-sample-technology\")[\"train\"][\"title\"] # 'docs' is a list of text [text1, text2, ..., textN]\n```\n\n### Choose Your Embedding Model\n\nBunkatopics 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.\n\n```python\n# Load Embedding model\nfrom sentence_transformers import SentenceTransformer\nembedding_model = SentenceTransformer(model_name_or_path=\"all-MiniLM-L6-v2\")\n\n# Load Projection Model\nimport umap\nprojection_model = umap.UMAP(\n n_components=2,\n random_state=42)\n\nfrom bunkatopics import Bunka\n\nbunka = Bunka(embedding_model=embedding_model, \n projection_model=projection_model) # the language is automatically detected, make sure the embedding model is adapted\n\n# Fit Bunka to your text data\n bunka.fit(docs)\n```\n\n```python\nfrom sklearn.cluster import KMeans\nclustering_model = KMeans(n_clusters=15)\n>>> bunka.get_topics(name_length=5, custom_clustering_model=clustering_model)# Specify the number of terms to describe each topic\n```\n\nTopics are described by the most specific terms belonging to the cluster.\n\n| topic_id | topic_name | size | percent |\n|:--------:|:-----------------------------|:----:|:-------:|\n| bt-12 | technology - Tech - Children - student - days | 322 | 10.73 |\n| bt-11 | blockchain - Cryptocurrency - sense - Cryptocurrencies - Impact | 283 | 9.43 |\n| bt-7 | gadgets - phone - Device - specifications - screen | 258 | 8.6 |\n| bt-8 | software - Kubernetes - ETL - REST - Salesforce | 258 | 8.6 |\n| bt-1 | hackathon - review - Recap - Predictions - Lessons | 257 | 8.57 |\n| bt-4 | Reality - world - cities - future - Lot | 246 | 8.2 |\n| bt-14 | Product - Sales - day - dream - routine | 241 | 8.03 |\n| bt-0 | Words - Robots - discount - NordVPN - humans | 208 | 6.93 |\n| bt-2 | Internet - Overview - security - Work - Development | 202 | 6.73 |\n| bt-13 | Course - Difference - Step - science - Point | 192 | 6.4 |\n| bt-6 | quantum - Cars - Way - Game - quest | 162 | 5.4 |\n| bt-3 | Objects - Strings - app - Programming - Functions | 119 | 3.97 |\n| bt-5 | supply - chain - revolution - Risk - community | 119 | 3.97 |\n| bt-9 | COVID - printing - Car - work - app | 89 | 2.97 |\n| bt-10 | Episode - HD - Secrets - TV | 44 | 1.47 |\n\n### Visualize Your Topics\n\nFinally, let's visualize the topics that Bunka has computed for your text data:\n\n```python\n>>> bunka.visualize_topics(width=800, height=800, colorscale='YIGnBu')\n```\n\n<img src=\"docs/images/topic_modeling_raw_YlGnBu.png\" width=\"70%\" height=\"70%\" align=\"center\" />\n\n## Topic Modeling with GenAI Summarization of Topics\n\nExplore the power of Generative AI for summarizing topics!\n\n```python\nfrom langchain.llms import OpenAI\n\nllm = OpenAI(openai_api_key = 'OPEN_AI_KEY')\n```\n\nNote: It is recommended to use an Instruct model ie a model that has been fine-tuned on a discussion task. If not, the results might be meaningless.\n\n```python\n# Obtain clean topic names using Generative Model\nbunka.get_clean_topic_name(generative_model=llm)\n```\n\nCheck the top documents for every topic!\n\n```python\n>>> bunka.df_top_docs_per_topic_\n```\n\nFinally, let's visualize again the topics. We can chose from different colorscales.\n\n```python\n>>> bunka.visualize_topics(width=800, height=800)\n```\n\nYlGnBu | Portland\n:-------------------------:|:-------------------------:\n | \n\ndelta | Blues\n:-------------------------:|:-------------------------:\n | \n\nWe can now access the newly made topics\n\n```python\n>>> bunka.df_topics_\n```\n\n| topic_id | topic_name | size | percent |\n|:--------:|:--------------------------------------|:----:|:-------:|\n| bt-1 | Cryptocurrency Impact | 345 | 12.32 |\n| bt-3 | Data Management Technologies | 243 | 8.68 |\n| bt-14 | Everyday Life | 230 | 8.21 |\n| bt-0 | Digital Learning Campaign | 225 | 8.04 |\n| bt-12 | Business Development | 223 | 7.96 |\n| bt-2 | Technology Devices | 212 | 7.57 |\n| bt-10 | Market Predictions Recap | 201 | 7.18 |\n| bt-4 | Comprehensive Learning Journey | 187 | 6.68 |\n| bt-6 | Future of Work | 185 | 6.61 |\n| bt-11 | Internet Discounts | 175 | 6.25 |\n| bt-5 | Technological Urban Water Management | 172 | 6.14 |\n| bt-9 | Electric Vehicle Technology | 145 | 5.18 |\n| bt-8 | Programming Concepts | 116 | 4.14 |\n| bt-13 | Quantum Technology Industries | 105 | 3.75 |\n| bt-7 | High Definition Television (HDTV) | 36 | 1.29 |\n\n## Visualise Dimensions on topics\n\n```python\ndataset = load_dataset(\"bunkalab/medium-sample-technology-tags\")['train']\ndocs = list(dataset['title'])\nids = list(dataset['doc_id'])\ntags = list(dataset['tags'])\n\nmetadata = {'tags':tags}\n\nfrom bunkatopics import Bunka\n\nbunka = Bunka()\n\n# Fit Bunka to your text data\nbunka.fit(docs=docs, ids=ids, metadata=metadata)\nbunka.get_topics(n_clusters=10)\nbunka.visualize_topics(color='tags', width=800, height=800) # Adjust the color\n```\n\n<img src=\"docs/images/bunka_color.png\" width=\"70%\" height=\"70%\" align=\"center\" />\n\n## Manually Cleaning the topics\n\nIf 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**.\n\n```python\n>>> bunka.manually_clean_topics()\n```\n\n<img src=\"docs/images/manually_change_topics.png\" width=\"40%\" height=\"20%\" align=\"center\" />\n\n## Removing Data based on topics for fine-tuning purposes\n\nYou 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.\n\n```python\n>>> bunka.clean_data_by_topics()\n```\n\n<img src=\"docs/images/fine_tuning_dataset.png\" width=\"40%\" height=\"20%\" align=\"center\" />\n\n```python\n>>> bunka.df_cleaned_\n```\n\n| doc_id | content | topic_id | topic_name |\n|:---------:|:-----------------------------------------------------:|:--------:|:--------------------------------------:|\n| 873ba315 | Invisibilize Data With JavaScript | bt-8 | Programming Concepts |\n| 1243d58f | Why End-to-End Testing is Important for Your Team | bt-3 | Data Management Technologies |\n| 45fb8166 | This Tiny Wearable Device Uses Your Body Heat... | bt-2 | Technology Devices |\n| a122d1d2 | Digital Policy Salon: The Next Frontier | bt-0 | Digital Learning Campaign |\n| 1bbcfc1c | Preparing Hardware for Outdoor Creative Technology Installations | bt-5 | Technological Urban Water Management |\n| 79580c34 | Angular Or React ? | bt-8 | Programming Concepts |\n| af0b08a2 | Ed-Tech Startups Are Cashing in on Parents\u2019 Insecurities | bt-0 | Digital Learning Campaign |\n| 2255c350 | Former Google CEO Wants to Create a Government-Funded University to Train A.I. Coders | bt-6 | Future of Work |\n| d2bc4b33 | Applying Action & The Importance of Ideas | bt-12 | Business Development |\n| 5219675e | Why You Should (not?) Use Signal | bt-2 | Technology Devices |\n| ... | ... | ... | ... |\n\n## Bourdieu Map\n\nThe 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.\n\nThe 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.\n\n```python\nfrom langchain.llms import HuggingFaceHub\n\n# Define the HuggingFaceHub instance with the repository ID and API token\nllm = HuggingFaceHub(\n repo_id='mistralai/Mistral-7B-v0.1',\n huggingfacehub_api_token=\"HF_TOKEN\"\n)\n\n## Bourdieu Fig\nbourdieu_fig = bunka.visualize_bourdieu(\n llm=llm,\n x_left_words=[\"This is about business\"],\n x_right_words=[\"This is about politics\"],\n y_top_words=[\"this is about startups\"],\n y_bottom_words=[\"This is about governments\"],\n height=800,\n width=800,\n clustering=True,\n topic_n_clusters=10,\n density=False,\n convex_hull=True,\n radius_size=0.2,\n min_docs_per_cluster = 5, \n label_size_ratio_clusters=80)\n```\n\n```python\n>>> bourdieu_fig.show()\n```\n\npositive/negative vs humans/machines | politics/business vs humans/machines \n:-------------------------:|:-------------------------:\n | \n\npolitics/business vs positive/negative | politics/business vs startups/governments\n:-------------------------:|:-------------------------:\n | \n\n## Saving and loading Bunka\n\n```python\nbunka.save_bunka(\"bunka_dump\")\n...\n\nfrom bunkatopics import Bunka\nbunka = Bunka().load_bunka(\"bunka_dump\")\n>>> bunka.get_topics(n_clusters = 15)\n```\n\n## Loading customed embeddings (Beta)\n\n```python\n'''\nids = ['doc_1', 'doc_2'...., 'doc_n']\nembeddings = [[0.05121125280857086,\n -0.03985324501991272,\n -0.05017390474677086,\n -0.03173152357339859,\n -0.07367539405822754,\n 0.0331297293305397,\n -0.00685789855197072...]]\n\n'''\n\npre_computed_embeddings = [{'doc_id': doc_id, 'embedding': embedding} for doc_id, embedding in zip(ids, embeddings)]\n...\n\nfrom bunkatopics import Bunka\nbunka = Bunka()\nbunka.fit(docs=docs, ids = ids, pre_computed_embeddings = pre_computed_embeddings)\n\n\nfrom sklearn.cluster import KMeans\nclustering_model = KMeans(n_clusters=15)\n>>> bunka.get_topics(name_length=5, \n custom_clustering_model=clustering_model)# Specify the number of terms to describe each topic\n```\n\n## Front-end (Beta)\n\nThis is a beta feature. First, git clone the repository\n\n```bash\ngit clone https://github.com/charlesdedampierre/BunkaTopics.git\ncd BunkaTopics\npip install -e .\n\ncd web # got the web directory\nnpm install # install the needed React packages\n```\n\n```python\nfrom bunkatopics import Bunka\n\nfrom sentence_transformers import SentenceTransformer\nembedding_model = SentenceTransformer(model_name_or_path=\"all-MiniLM-L6-v2\")\n\n# Initialize Bunka with your chosen model\nbunka = Bunka(embedding_model=embedding_model) \n\n# Fit Bunka to your text data\nbunka.fit(docs)\nbunka.get_topics(n_clusters=15, name_length=3) # Specify the number of terms to describe each topic\n```\n\n```python\n>>> bunka.start_server() # A serveur will open on your computer at http://localhost:3000/ \n```\n\n<img src=\"docs/images/bunka_server.png\" width=\"100%\" height=\"100%\" align=\"center\" />\n",
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