topic-modeling - kmeans - fasttext - surabaya This repository contains a set of models for performing topic modeling on tweets from Surabaya. This repository contains two key components: 1. fasttext.model: A gensim FastText model trained on processed tweet text. It is used to generate semantic vector embeddings for documents. 2. kmeans.joblib: A scikit-learn K-Means model trained on the vectors produced by the FastText model. It contains the final topic cluster centroids. Load the models using gensim, joblib, and huggingfacehub. import joblib from gensim.models import FastText from huggingfacehub import hfhubdownload REPOID = "Kiuyha/surabaya-opinion-tweet-clusters" kmeanspath =…
Independent publisher
Ketut Shridhara
Kiuyha
Models in Library1
Datasets in Library0
Models on Hugging Face9
Followers1
Compare the published evidence
This profile connects the 1 model and dataset record shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.
Reported licenses: mit. Open a record to review its specific license and conditions.
Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.