---
license: mit
language:
- en
tags:
- topic-modeling
datasets:
- CCRss/arxiv_papers_cs
---




# Top2Vec Scientific Texts Model

![MindMap](markmap-main.png)

This repository hosts the `top2vec_scientific_texts` model, a specialized Top2Vec model trained on scientific texts for topic modeling and semantic search.

## Model Overview

The `top2vec_scientific_texts` model is built for analyzing scientific literature. It leverages the Universal Sentence Encoder for embedding texts and uses Top2Vec for topic modeling.

### Key Features:

- **Domain-Specific:** Tailored for scientific texts.
- **Base Model:** Utilizes the Universal Sentence Encoder for effective text embeddings.
- **Topic Modeling:** Employs Top2Vec for discovering topics in scientific documents.

## Installation

To use the model, you need to install the following dependencies:

```bash
pip install top2vec
pip install top2vec[sentence_encoders]
pip install tensorflow==2.8.0
pip install tensorflow-probability==0.16.0
```

## Usage

Here's an example of how to use the model for topic modeling:

```bash
from top2vec import Top2Vec

# Load your documents
docs = ["Document 1 text", "Document 2 text", ...]

# Initialize the Top2Vec model
model = Top2Vec(
    documents=docs,
    speed='learn',
    workers=80,
    embedding_model='universal-sentence-encoder',
    umap_args={'n_neighbors': 15, 'n_components': 5, 'metric': 'cosine', 'min_dist': 0.0, 'random_state': 42},
    hdbscan_args={'min_cluster_size': 15, 'metric': 'euclidean', 'cluster_selection_method': 'eom'}
)
```

# Save the model

```bash
model.save('top2vec_scientific_texts_model')
```

## Dataset

The model was trained on a dataset of scientific abstracts sourced from [arXiv](https://arxiv.org/). The dataset covers a range of topics within the field of computer science from 2010 to 2024.

You can access the dataset [arxiv_papers_cs](https://huggingface.co/datasets/CCRss/arxiv_papers_cs).

## Use Cases

The `top2vec_scientific_texts` model can be used for various purposes, including:

- **Topic Discovery:** Identify the main topics within a collection of scientific texts.
- **Semantic Search:** Find documents that are semantically similar to a query text.
- **Trend Analysis:** Analyze the evolution of topics over time.

## Examples

Here are some examples of the model's output for the thematic group "UAV in Disasters and Emergency":

### Trend Analysis for "UAV in Disasters and Emergency"

![Trend Analysis](disasters_and_emergency_plot.png)

This graph shows the trend of interest in the use of UAVs in disaster and emergency situations over time.

### Key Metrics Table

Analysis for Thematic Group: Disasters & Emergency
|   Year |   Number of Publications |   Growth Acceleration |   Change in Number of Publications | Relative Growth   |
|-------:|-------------------------:|----------------------:|-----------------------------------:|:------------------|
|   2010 |                       19 |                     0 |                                  0 | 0.0%              |
|   2011 |                       15 |                    -4 |                                 -4 | -21.05%           |
|   2012 |                       28 |                    17 |                                 13 | 86.67%            |
|   2013 |                       38 |                    -3 |                                 10 | 35.71%            |
|   2014 |                       28 |                   -20 |                                -10 | -26.32%           |
|   2015 |                       47 |                    29 |                                 19 | 67.86%            |
|   2016 |                       63 |                    -3 |                                 16 | 34.04%            |
|   2017 |                       94 |                    15 |                                 31 | 49.21%            |
|   2018 |                      173 |                    48 |                                 79 | 84.04%            |
|   2019 |                      266 |                    14 |                                 93 | 53.76%            |
|   2020 |                      337 |                   -22 |                                 71 | 26.69%            |
|   2021 |                      380 |                   -28 |                                 43 | 12.76%            |
|   2022 |                      453 |                    30 |                                 73 | 19.21%            |
|   2023 |                      509 |                   -17 |                                 56 | 12.36%            |

## Contributions

We welcome contributions to the top2vec_scientific_texts model. If you have suggestions, improvements, or encounter any issues, please feel free to open an issue or submit a pull request.

## License

This project is licensed under the MIT License