Update README.md

README.md

@@ -54,7 +54,7 @@ dataset_info:
     num_bytes: 4225989.067784654
     num_examples: 17863
   download_size: 20360216
-  dataset_size: 28172393.0
+  dataset_size: 28172393
 configs:
 - config_name: default
   data_files:
@@ -64,4 +64,52 @@ configs:
     path: data/validation-*
   - split: test
     path: data/test-*
+license: mit
+task_categories:
+- text-classification
+pretty_name: multilang-detect
 ---
+# Dataset Card for Multilingual Language Detection
+
+## Dataset Details
+
+
+This dataset is a comprehensive resource for **multilingual text classification**, specifically designed for language identification. It contains over 100,000 text samples from 36 different languages, sourced from various public datasets and meticulously cleaned for machine learning applications.
+
+The primary goal of this dataset is to train models that can accurately predict the language of a given text snippet. It is pre-split into training (70%), validation (15%), and test (15%) sets, with stratification on the `Language` column to ensure a balanced distribution of languages across all splits.
+
+- **Language(s) (NLP):** Arabic, Bulgarian, Chinese, Danish, Dutch, English, Estonian, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Kannada, Korean, Latin, Malayalam, Modern Greek, Persian, Polish, Portuguese, Pushto, Romanian, Russian, Spanish, Swahili, Swedish, Tamil, Thai, Turkish, Urdu, Vietnamese.
+- **License:** `mit`
+
+- **Repository:** [minhleduc/multilang-classify-dataset-02](https://huggingface.co/datasets/minhleduc/multilang-classify-dataset-02)
+
+## Uses
+
+### Direct Use
+
+This dataset is ideal for:
+- Training and fine-tuning language identification models.
+- Benchmarking multilingual text classifiers.
+- Research in multilingual representation learning.
+
+### Out-of-Scope Use
+
+This dataset is not suitable for:
+- Evaluating the grammatical correctness or fluency of a text.
+- Training models for tasks that require pristine, uncleaned text with original punctuation and casing.
+- Making judgments about the author of the text. The data has been anonymized and processed, and may contain biases from its original sources.
+
+## Dataset Structure
+
+The dataset is provided in a `DatasetDict` containing `train`, `validation`, and `test` splits. Each sample has two fields:
+
+- `Text` (`string`): The cleaned text sample.
+- `Language` (`ClassLabel`): The language of the text, encoded as an integer. The mapping from integer to language name is stored in the feature metadata.
+
+**Example:**
+```json
+{
+  "Text": "this is an example of english text",
+  "Language": English
+}
+```