Update README.md

README.md

@@ -18,7 +18,7 @@ dataset_info:
     num_bytes: 2255170.8145489353
     num_examples: 2508
   download_size: 13625805
-  dataset_size: 22546313.0
+  dataset_size: 22546313
 configs:
 - config_name: default
   data_files:
@@ -28,4 +28,122 @@ configs:
     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"
+}