VersoVector
VersoVector is a public NLP/MLOps repository for exploring emotional-semantic patterns in poetic and lyrical language.
The project explores whether computational language models can detect emotional, thematic, and semantic relationships in poems in a way that supports human literary interpretation.
It combines two complementary learning approaches:
- Unsupervised learning: clustering poems by style, tone, topic, or semantic proximity.
- Supervised learning: classifying poems by emotion, theme, or poetic tone.
The central research question is:
Can a language model perceive the emotion behind a poem, as a human reader does?
What this documentation is for
This documentation helps a reader:
- clone the public repository;
- prepare the local environment;
- understand the notebook sequence;
- understand the model topology;
- reproduce the main analytical pipeline;
- inspect generated outputs;
- understand how model artifacts are packaged;
- run the local API/frontend foundation when the model bundle is available.
Repository focus
The public repository demonstrates:
- text cleaning and preprocessing;
FeatureUnion-based feature representation;CountVectorizer,TfidfVectorizer, andDictVectorizer;- supervised multilabel tag prediction;
- unsupervised similarity, topic modeling, clustering, and projections;
- integration of supervised and unsupervised outputs;
- model bundle generation;
- Python inference abstractions;
- FastAPI serving foundation;
- Gradio frontend foundation;
- Docker-based local services;
- tests and reproducibility-oriented structure.
Recommended learning path
Start here if you want to clone the repository, reproduce the notebooks, and understand how the model pipeline is assembled.
Source repository
- GitHub: HubertRonald/VersoVector
- Original model topology source: docs/model_topology.md
Future direction
This public repository may later support a hosted product demo, but this documentation focuses on the reproducible public project: how to run it, inspect it, and understand its results.