---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: lang
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 4512776
    num_examples: 25942
  - name: validation
    num_bytes: 644682
    num_examples: 3706
  - name: test
    num_bytes: 1289538
    num_examples: 7413
  download_size: 4254592
  dataset_size: 6446996
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- text-classification
language:
- vi
---



# Multilingual Text Classification Dataset

This dataset is designed for **multilingual text classification** tasks.
It includes labeled text samples across **8 languages**, making it ideal for training and evaluating models on **cross-lingual transfer**, **language identification**, and **multilingual understanding**.


## Dataset Overview

| Split      | # Examples | Size (bytes)  |
| ---------- | ---------- | ------------- |
| Train      | 18,657     | 2,651,248     |
| Validation | 2,665      | 378,709       |
| Test       | 5,331      | 757,560       |
| **Total**  | **26,653** | **3,787,517** |

**Total Download Size:** 2.6 MB
**Total Dataset Size:** 3.8 MB
**Task Type:** Text Classification


## Data Fields

| Field   | Type     | Description                                        |
| ------- | -------- | -------------------------------------------------- |
| `text`  | `string` | The input text sample.                             |
| `lang`  | `string` | The ISO 639-3 language code of the text.           |
| `label` | `int64`  | The integer label representing the language class. |


## Language Labels

| Language        | Code  | Label ID |
| --------------- | ----- | -------- |
| German          | `deu` | 0        |
| Chinese         | `zho` | 1        |
| Amharic         | `amh` | 2        |
| Hindi           | `hin` | 3        |
| Arabic          | `arb` | 4        |
| Hausa           | `hau` | 5        |
| Turkish         | `tur` | 6        |
| Urdu            | `urd` | 7        |
| Spanish         | `spa` | 8        |
| Persian (Farsi) | `fas` | 9        |
| English         | `eng` | 10       |
| Nepali          | `nep` | 11       |




## Intended Uses

* Multilingual language classification
* Cross-lingual and zero-shot evaluation
* Benchmarking multilingual embeddings (e.g., mBERT, XLM-R, LaBSE)
* Studying language similarity and confusion patterns


## Usage Example

You can easily load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("8Opt/multilingual-classification-0001")

example = dataset["train"][0]
print(example)
```

Output:

```python
{
  "text": "Das ist ein Beispielsatz.",
  "lang": "deu",
  "label": 0
}
```

Label mapping:

```python
label2idx = {
  'deu': 0,
  'zho': 1,
  'amh': 2,
  'hin': 3,
  'arb': 4,
  'hau': 5,
  'tur': 6,
  'urd': 7,
  'spa': 8,
  'fas': 9,
  'eng': 10,
  'nep': 11
}

```


## Configurations

**Configuration name:** `default`

Each split is stored under `data/`:

```
data/
 ├── train-*
 ├── validation-*
 └── test-*
```

---

## Citation

If you use this dataset in your work, please cite it as:

```
@dataset{8Opt,
  title={Multilingual Text Classification Dataset},
  author={8Opt},
  year={2025},
  url={https://huggingface.co/datasets/8Opt/multilingual-classification-0001}
}
```