--
pipeline_tag: text-classification
library_name: turftopic
tags:
- turftopic
- topic-modelling
---

# kardosdrur/testing_s3

This repository contains a topic model trained with the [Turftopic](https://github.com/x-tabdeveloping/turftopic) Python library.

To load and use the model run the following piece of code:

```python
from turftopic import load_model

model = load_model(kardosdrur/testing_s3)
model.print_topics()
```

## Model Structure

The model is structured as follows:

```
SemanticSignalSeparation(decomposition=FastICA(n_components=10),
                         vectorizer=CountVectorizer(min_df=10,
                                                    stop_words='english'))
```

## Topics
The topics discovered by the model are the following:

| Topic ID | Highest Ranking | Lowest Ranking |
| - | - | - |
| 0 | goaltenders, nhl, bullpen, sabres, goaltender, puckett, leafs, braves, pitchers, canucks | accelerator, malaysia, automobile, accelerators, mazda, automobiles, automotive, vehicle, silicon, britain |
| 1 | saturn, suzuki, symptoms, jupiter, bmw, exhaust, volvo, engine, mazda, propulsion | wiretapping, wiretaps, nsa, spying, eavesdropping, wiretap, security, encryption, enforcement, safeguarding |
| 2 | drawbacks, advantages, productivity, efficiency, innovation, economical, disadvantages, proponents, competitiveness, economically | address, instructions, serial, arrived, codes, configured, 9591, 16550, contacting, recieved |
| 3 | publishes, archives, publisher, scholars, manuscripts, npr, affiliated, revelations, discusses, archive | motorcycling, motorcycles, speeding, motorcycle, driving, motorcyclist, riding, harleys, braking, vehicles |
| 4 | motherboard, ram, motherboards, processor, cmos, hardware, chipset, chipsets, amd, mb | yale, sunroof, damphousse, library, npr, billboards, balloon, schools, kerosene, nicholas |
| 5 | palestinians, palestinian, gazans, gaza, genocide, israelis, atrocities, israeli, hamas, holocaust | motorola, mastercard, technician, smartdrive, telephony, transmissions, phones, electronically, voyager, cruising |
| 6 | spectrometer, makefile, biochemistry, dblspace, bibliography, booklet, bookstores, circumference, nutritional, statistically | uh, um, em, yeah, oh, er, ah, yer, yo, ye |
| 7 | theology, theological, scripture, theologians, christianity, biblical, agnosticism, devout, agnostic, christians | missiles, munitions, soviets, artillery, bunker, missile, explosives, tactical, grenades, soviet |
| 8 | causes, metabolism, obstruction, bugging, xsession, disabling, debugger, behaviour, syndrome, occurs | prices, pricing, price, affordable, cheap, forsale, inexpensive, cost, purchases, priced |
| 9 | xcreatewindow, programmable, bitmap, bitmaps, colormaps, freeware, gui, imagewriter, colormap, adobe | discrepancy, inaccuracies, defective, debacle, unrecognized, faulty, misconception, sceptical, refutation, warranted |

## Package versions

The model in this repo was trained using the following package versions:

| Package | Version |
| - | - |
| scikit-learn | 1.3.2 |
| sentence-transformers | 3.2.0 |
| turftopic | 0.6.0 |
| joblib | 1.2.0 |

We recommend that you install the same, or compatible versions of these packages locally, before trying to load a model.