Datasets:

Process-Venue
/

Language_Indentification_v2

@@ -1,142 +1,149 @@
-# Dataset Card for Language Identification Dataset
-### Dataset Description
-- **Repository:** processvenue/language_identification
-- **Total Samples:** 135784
-- **Number of Languages:** 18
-- **Splits:**
-  - Train: 104849 samples (70%)
-  - Validation: 15467 samples (15%)
-  - Test: 15468 samples (15%)
-### Dataset Summary
-A comprehensive dataset for Indian language identification and text classification. The dataset contains text samples across 18 major Indian languages, making it suitable for developing language identification systems and multilingual NLP applications.
-### Languages and Distribution
-```
-Language Distribution:
-1. Punjabi      15075
-2. Odia         14258
-3. Konkani      14098
-4. Hindi        13469
-5. Sanskrit     11788
-6. Bengali      10036
-7. English       9819
-8. Sindhi        8838
-9. Nepali        8694
-10. Marathi       6625
-11. Gujarati      3788
-12. Telugu        3563
-13. Malayalam     3423
-14. Tamil         3195
-15. Kannada       2651
-16. Kashmiri      2282
-17. Urdu          2272
-18. Assamese      1910
-```
-### Language Details
-1. **Hindi (hi)**: Major language of India, written in Devanagari script
-2. **Urdu (ur)**: Written in Perso-Arabic script
-3. **Bengali (bn)**: Official language of Bangladesh and several Indian states
-4. **Gujarati (gu)**: Official language of Gujarat
-5. **Kannada (kn)**: Official language of Karnataka
-6. **Malayalam (ml)**: Official language of Kerala
-7. **Marathi (mr)**: Official language of Maharashtra
-8. **Odia (or)**: Official language of Odisha
-9. **Punjabi (pa)**: Official language of Punjab
-10. **Tamil (ta)**: Official language of Tamil Nadu and Singapore
-11. **Telugu (te)**: Official language of Telangana and Andhra Pradesh
-12. **Sanskrit (sa)**: Ancient language of India, written in Devanagari script
-13. **Konkani (kok)**: Official language of Goa
-14. **Sindhi (sd)**: Official language of Sindh province in Pakistan
-15. **Nepali (ne)**: Official language of Nepal
-16. **Assamese (as)**: Official language of Assam
-17. **Kashmiri (ks)**: Official language of Jammu and Kashmir
-18. **English (en)**: Official language of India
-### Data Fields
-- `Headline`: The input text sample
-- `Language`: The language label (one of the 18 languages listed above)
-### Usage Example
-```python
-from datasets import load_dataset
-# Load the dataset
-dataset = load_dataset("processvenue/language_identification")
-# Access splits
-train_data = dataset['train']
-validation_data = dataset['validation']
-test_data = dataset['test']
-# Example usage
-print(f"Sample text: {train_data[0]['Headline']}")
-print(f"Language: {train_data[0]['Language']}")
-```
-### Applications
-1. **Language Identification Systems**
-   - Automatic language detection
-   - Text routing in multilingual systems
-   - Content filtering by language
-2. **Machine Translation**
-   - Language-pair identification
-   - Translation system selection
-3. **Content Analysis**
-   - Multilingual content categorization
-   - Language-specific content analysis
-### Citation
-If you use this dataset in your research, please cite:
-```
-@dataset{language_identification_2025,
-  author = {ProcessVenue Team},
-  website = {https://processvenue.com},
-  title = {Multilingual Headlines Language Identification Dataset},
-  year = {2025},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/datasets/processvenue/language-identification},
-  version = {1.1}
-}
-```
-###reference
-  ```
-    1. @misc{disisbig_news_datasets,
-  author = {Gaurav},
-  title = {Indian Language News Datasets},
-  year = {2019},
-  publisher = {Kaggle},
-  url = {https://www.kaggle.com/datasets/disisbig/}
-  }
-```
-```
-    2. @misc{bhattarai_nepali_financial_news,
-  author = {Anuj Bhattarai},
-  title = {The Nepali Financial News Dataset},
-  year = {2024},
-  publisher = {Kaggle},
-  url = {https://www.kaggle.com/datasets/anujbhatrai/the-nepali-financial-news-dataset}
-    }
-```
-```
-    3. @misc{sourav_inshorts_hindi,
-  author = {Shivam Sourav},
-  title = {Inshorts-Hindi},
-  year = {2023},
-  publisher = {Kaggle},
-  url = {https://www.kaggle.com/datasets/shivamsourav2002/inshorts-hindi}
-    }
 ```

+---
+license: cc-by-sa-4.0
+task_categories:
+- text-classification
+- text2text-generation
+language:
+- hi
+- en
+- mr
+- pa
+- ne
+- sd
+- as
+- gu
+- ta
+- te
+- ur
+- or
+tags:
+- multilingual
+- language-identification
+- text-classification
+- indian
+pretty_name: Language Identification Dataset
+size_categories:
+- 100K<n<1M
+---
+# Dataset Card for Language Identification Dataset
+### Dataset Description
+- **Repository:** processvenue/language_identification
+- **Total Samples:** 135784
+- **Number of Languages:** 18
+- **Splits:**
+  - Train: 95048 samples
+  - Validation: 20367 samples
+  - Test: 20369 samples
+### Dataset Summary
+A comprehensive dataset for Indian language identification and text classification. The dataset contains text samples across 18 major Indian languages, making it suitable for developing language identification systems and multilingual NLP applications.
+### Languages and Distribution
+```
+Language Distribution:
+1. Punjabi      15075
+2. Odia         14258
+3. Konkani      14098
+4. Hindi        13469
+5. Sanskrit     11788
+6. Bengali      10036
+7. English       9819
+8. Sindhi        8838
+9. Nepali        8694
+10. Marathi       6625
+11. Gujarati      3788
+12. Telugu        3563
+13. Malayalam     3423
+14. Tamil         3195
+15. Kannada       2651
+16. Kashmiri      2282
+17. Urdu          2272
+18. Assamese      1910
+```
+### Data Fields
+- `Headline`: The input text sample
+- `Language`: The language label (one of the 18 languages listed above)
+### Usage Example
+```python
+from datasets import load_dataset
+# Load the dataset
+dataset = load_dataset("processvenue/language_identification")
+# Access splits
+train_data = dataset['train']
+validation_data = dataset['validation']
+test_data = dataset['test']
+# Example usage
+print(f"Sample text: {train_data[0]['Headline']}")
+print(f"Language: {train_data[0]['Language']}")
+```
+### Applications
+1. **Language Identification Systems**
+   - Automatic language detection
+   - Text routing in multilingual systems
+   - Content filtering by language
+2. **Machine Translation**
+   - Language-pair identification
+   - Translation system selection
+3. **Content Analysis**
+   - Multilingual content categorization
+   - Language-specific content analysis
+### Citation
+If you use this dataset in your research, please cite:
+```
+@dataset{language_identification_2025,
+  author = {ProcessVenue Team},
+  website = {https://processvenue.com},
+  title = {Multilingual Headlines Language Identification Dataset},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/processvenue/language-identification},
+  version = {1.1}
+}
+```
+###reference
+  ```
+    1. @misc{disisbig_news_datasets,
+  author = {Gaurav},
+  title = {Indian Language News Datasets},
+  year = {2019},
+  publisher = {Kaggle},
+  url = {https://www.kaggle.com/datasets/disisbig/}
+  }
+```
+```
+    2. @misc{bhattarai_nepali_financial_news,
+  author = {Anuj Bhattarai},
+  title = {The Nepali Financial News Dataset},
+  year = {2024},
+  publisher = {Kaggle},
+  url = {https://www.kaggle.com/datasets/anujbhatrai/the-nepali-financial-news-dataset}
+    }
+```
+```
+    3. @misc{sourav_inshorts_hindi,
+  author = {Shivam Sourav},
+  title = {Inshorts-Hindi},
+  year = {2023},
+  publisher = {Kaggle},
+  url = {https://www.kaggle.com/datasets/shivamsourav2002/inshorts-hindi}
+    }
 ```