--- dataset_info: features: - name: text dtype: string - name: lang dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 2651248 num_examples: 18657 - name: validation num_bytes: 378709 num_examples: 2665 - name: test num_bytes: 757560 num_examples: 5331 download_size: 2646591 dataset_size: 3787517 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 | | Arabic | `arb` | 3 | | Hausa | `hau` | 4 | | Urdu | `urd` | 5 | | Spanish | `spa` | 6 | | English | `eng` | 7 | This mapping is stored internally in the dataset and can be used to decode model predictions or remap outputs. ## 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 id2label = { 0: "deu", 1: "zho", 2: "amh", 3: "arb", 4: "hau", 5: "urd", 6: "spa", 7: "eng" } ``` ## 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} } ```