ConTextMining 0.0.1

Complementing topic models with few-shot in-context learning to generate interpretable topics

ConTextMining is a package generate interpretable topics labels from the keywords of topic models (e.g, LDA, BERTopic) through few-shot in-context learning.

Requirements

Required packages

The following packages are required for ConTextMining.

• torch (to learn how to install, please refer to pytorch.org/)

• transformers

• tokenizers

• huggingface-hub

• flash_attn

• accelerate

To install these packages, you can do the following:

pip install torch transformers tokenizers huggingface-hub flash_attn accelerate

GPU requirements

You require at least one GPU to use ConTextMining.

VRAM requirements depend on factors like number of keywords or topics used to topic labels you wish to generate.

However, at least 8GB of VRAM is recommended

huggingface API key

You will need a huggingface access token. To obtain one:

1. you'd first need to create a huggingface account if you do not have one.

2. Create and store a new access token. To learn more, please refer to huggingface.co/docs/hub/en/security-tokens.

3. Note: Some pre-trained large language models (LLMs) may require permissions. For more information, please refer to huggingface.co/docs/hub/en/models-gated.

Installation

To install in python, simply do the following:

pip install ConTextMining

Quick Start

Here we provide a quick example on how you can execute ConTextMining to conveniently generate interpretable topics labels from the keywords of topic models.

from ConTextMining import get_topic_labels



# specify your huggingface access token. To learn how to obtain one, refer to huggingface.co/docs/hub/en/security-tokens

hf_access_token="<your huggingface access token>" 



# specify the huggingface model id. Choose between "microsoft/Phi-3-mini-4k-instruct", "meta-llama/Meta-Llama-3.1-8B-Instruct" or "google/gemma-2-2b-it"

model_id="meta-llama/Meta-Llama-3.1-8B-Instruct"



# specify the keywords for the few-shot learning examples

keywords_examples = [

    "olympic, year, said, games, team",

    "mr, bush, president, white, house",

    "sadi, report, evidence, findings, defense",

    "french, union, germany, workers, paris",

    "japanese, year, tokyo, matsui, said"

]



# specify the labels CORRESPONDING TO THE INDEX of the keywords of 'keyword_examples' above. 

labels_examples = [

    "sports",

    "politics",

    "research",

    "france",

    "japan"

]



# specify your topic modeling keywords of wish to generate coherently topic labels. 

topic_modeling_keywords ='''Topic 1: [amazing, really, place, phenomenon, pleasant],

Topic 2: [loud, awful, sunday, like, slow],

Topic 3: [spinach, carrots, green, salad, dressing],

Topic 4: [mango, strawberry, vanilla, banana, peanut],

Topic 5: [fish, roll, salmon, fresh, good]'''





print(get_topic_labels(topic_modeling_keywords, keywords_examples, labels_examples, model_id, hf_access_token))

You will now get the interpretable topic model labels for all 5 topics!

Citation

C Alba "ConText Mining: Complementing topic models with few-shot in-context learning to generate interpretable topics" Working paper.

Questions?

Contact me at [e-mail]