---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label_categories
    dtype: int64
  - name: lang
    dtype: string
  splits:
  - name: train
    num_bytes: 12397135
    num_examples: 17501
  - name: validation
    num_bytes: 2689677
    num_examples: 3751
  - name: test
    num_bytes: 2670478
    num_examples: 3751
  download_size: 10961920
  dataset_size: 17757290
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
task_categories:
- text-classification
language:
- en
- ca
- es
tags:
- science
---


# Multilingual Scientific Text Classification Dataset (MAG FoS L1)

## Overview
This dataset contains multilingual scientific text samples (Catalan, Spanish, and English) extracted from scientific publications.  
Each sample is labeled using **Microsoft Academic Graph (MAG) Field of Study — Level 1** categories.

For each publication, the `text` field is a **random selection** of:

- the title  
- the abstract  
- the title followed by the abstract (`title + ". " + abstract`)

This introduces natural variation and improves model robustness for text classification tasks.

---

## Dataset Structure

### Features
| Feature   | Type     | Description                                                  |
|-----------|----------|--------------------------------------------------------------|
| `text`    | string   | Randomly selected title, abstract, or title + abstract       |
| `label`   | string   | MAG Field of Study (FoS) Level 1 category                    |
| `language`| string   | ISO code of publication language (`ca`, `es`, `en`)          |

### Splits
| Split       | Samples |
|-------------|---------|
| Train       | 20,059  |
| Validation  | 2,507   |
| Test        | 2,508   |
| **Total**   | 25,074  |

Splits follow an **80/10/10 ratio**.

---

## Languages
The dataset includes scientific publications written in:

- **Catalan (`ca`)**
- **Spanish (`es`)**
- **English (`en`)**

---

## Task
**Multiclass Scientific Text Classification**

Your model should predict the **Field of Study (FoS) Level 1** category from a scientific text snippet.  
This dataset is suitable for:

- multilingual text classification  
- scientific-domain NLP  
- domain adaptation  
- benchmarking multilingual LLMs (mBERT, XLM-R, LLaMA, etc.)  
- zero-shot or few-shot evaluation  

---

## Source
The labels correspond to **Level 1 Fields of Study from the Microsoft Academic Graph (MAG)** ontology.

Typical categories include (examples):

- Chemistry  
- Physics  
- Biology  
- Computer Science  
- Mathematics  
- Medicine  
- Social Sciences  
- Engineering  
- Earth Sciences  
- Environmental Science  

The exact label set matches the categories present in the processed data.

---

## Creation Process
1. Load publication metadata (title, abstract, language, FoS).
2. Clean and normalize text fields.
3. Randomly choose one of:
   - title  
   - abstract  
   - title + abstract  
4. Assign the MAG FoS L1 label.
5. Perform an 80/10/10 train-validation-test split using HuggingFace `datasets`.

---

## Usage

### Load the dataset
```python
from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/YOUR_DATASET_NAME")
print(dataset["train"][0])
Example record
json
Copy code
{
  "text": "Reactividad de CHI3 con radicales O... Las vías de abstracción...",
  "label": "Physical chemistry",
  "language": "es"
}