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- ---
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- license: cc-by-sa-4.0
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- task_categories:
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- - text-classification
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- tags:
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- - language-identification
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- - multilingual
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- - indic-languages
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- - text-classification
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- pretty_name: Multilingual Headlines Language Identification
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- size_categories:
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- - 10K<n<100K
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- language:
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- - hi
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- - ur
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- - bn
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- - gu
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- - kn
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- - ml
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- - mr
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- - or
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- - pa
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- - ta
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- ---
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-
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- # Dataset Card for Language Identification Dataset
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-
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- ### Dataset Description
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-
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- - **Repository:** processvenue/language_identification
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- - **Total Samples:** 9627
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- - **Number of Languages:** 10
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- - **Splits:**
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- - Train: 6741 samples (70%)
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- - Validation: 1441 samples (15%)
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- - Test: 1445 samples (15%)
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-
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- ### Dataset Summary
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-
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- A comprehensive dataset for Indian language identification and text classification. The dataset contains text samples across 10 major Indian languages, making it suitable for developing language identification systems and multilingual NLP applications.
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-
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- ### Languages and Distribution
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-
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- ```
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- Language Distribution:
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- Urdu 1000
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- Hindi 1000
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- Odia 1000
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- Tamil 1000
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- Kannada 1000
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- Bengali 1000
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- Gujarati 1000
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- Malayalam 1000
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- Marathi 1000
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- Punjabi 627
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- ```
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-
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- ### Language Details
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-
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- 1. **Hindi (hi)**: Major language of India, written in Devanagari script
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- 2. **Urdu (ur)**: Written in Perso-Arabic script
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- 3. **Bengali (bn)**: Official language of Bangladesh and several Indian states
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- 4. **Gujarati (gu)**: Official language of Gujarat
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- 5. **Kannada (kn)**: Official language of Karnataka
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- 6. **Malayalam (ml)**: Official language of Kerala
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- 7. **Marathi (mr)**: Official language of Maharashtra
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- 8. **Odia (or)**: Official language of Odisha
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- 9. **Punjabi (pa)**: Official language of Punjab
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- 10. **Tamil (ta)**: Official language of Tamil Nadu and Singapore
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-
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- ### Data Fields
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-
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- - `text`: The input text sample
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- - `language`: The language label (one of the 10 languages listed above)
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-
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- ### Usage Example
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-
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- ```python
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- from datasets import load_dataset
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-
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- # Load the dataset
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- dataset = load_dataset("processvenue/language_identification")
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-
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- # Access splits
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- train_data = dataset['train']
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- validation_data = dataset['validation']
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- test_data = dataset['test']
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-
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- # Example usage
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- print(f"Sample text: {train_data[0]['text']}")
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- print(f"Language: {train_data[0]['language']}")
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- ```
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-
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- ### Applications
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-
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- 1. **Language Identification Systems**
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- - Automatic language detection
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- - Text routing in multilingual systems
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- - Content filtering by language
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-
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- 2. **Machine Translation**
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- - Language-pair identification
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- - Translation system selection
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-
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- 3. **Content Analysis**
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- - Multilingual content categorization
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- - Language-specific content analysis
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-
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- ### Citation
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-
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- If you use this dataset in your research, please cite:
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-
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- ```
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- @dataset{language_identification_2024,
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- author = {ML Technology Team},
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- title = {Multilingual Headlines Language Identification Dataset},
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- year = {2024},
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- publisher = {Hugging Face},
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- url = {https://huggingface.co/datasets/MLTechnology/language-identification}
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- }
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- ```
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-
 
 
 
 
 
 
 
 
 
 
 
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+ ---
2
+ license: cc-by-sa-4.0
3
+ task_categories:
4
+ - text-classification
5
+ tags:
6
+ - language-identification
7
+ - multilingual
8
+ - indic-languages
9
+ - text-classification
10
+ pretty_name: Multilingual Headlines Language Identification
11
+ size_categories:
12
+ - 10K<n<100K
13
+ language:
14
+ - hi
15
+ - ur
16
+ - bn
17
+ - gu
18
+ - kn
19
+ - ml
20
+ - mr
21
+ - or
22
+ - pa
23
+ - ta
24
+ ---
25
+
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+ # Dataset Card for Language Identification Dataset
27
+
28
+ ### Dataset Description
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+
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+ - **Repository:** processvenue/language_identification
31
+ - **Total Samples:** 9627
32
+ - **Number of Languages:** 10
33
+ - **Splits:**
34
+ - Train: 6741 samples (70%)
35
+ - Validation: 1441 samples (15%)
36
+ - Test: 1445 samples (15%)
37
+
38
+ ### Dataset Summary
39
+
40
+ A comprehensive dataset for Indian language identification and text classification. The dataset contains text samples across 10 major Indian languages, making it suitable for developing language identification systems and multilingual NLP applications.
41
+
42
+ ### Languages and Distribution
43
+
44
+ ```
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+ Language Distribution:
46
+ Urdu 1000
47
+ Hindi 1000
48
+ Odia 1000
49
+ Tamil 1000
50
+ Kannada 1000
51
+ Bengali 1000
52
+ Gujarati 1000
53
+ Malayalam 1000
54
+ Marathi 1000
55
+ Punjabi 627
56
+ ```
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+
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+ ### Language Details
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+
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+ 1. **Hindi (hi)**: Major language of India, written in Devanagari script
61
+ 2. **Urdu (ur)**: Written in Perso-Arabic script
62
+ 3. **Bengali (bn)**: Official language of Bangladesh and several Indian states
63
+ 4. **Gujarati (gu)**: Official language of Gujarat
64
+ 5. **Kannada (kn)**: Official language of Karnataka
65
+ 6. **Malayalam (ml)**: Official language of Kerala
66
+ 7. **Marathi (mr)**: Official language of Maharashtra
67
+ 8. **Odia (or)**: Official language of Odisha
68
+ 9. **Punjabi (pa)**: Official language of Punjab
69
+ 10. **Tamil (ta)**: Official language of Tamil Nadu and Singapore
70
+
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+ ### Data Fields
72
+
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+ - `text`: The input text sample
74
+ - `language`: The language label (one of the 10 languages listed above)
75
+
76
+ ### Usage Example
77
+
78
+ ```python
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+ from datasets import load_dataset
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+
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+ # Load the dataset
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+ dataset = load_dataset("processvenue/language_identification")
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+
84
+ # Access splits
85
+ train_data = dataset['train']
86
+ validation_data = dataset['validation']
87
+ test_data = dataset['test']
88
+
89
+ # Example usage
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+ print(f"Sample text: {train_data[0]['text']}")
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+ print(f"Language: {train_data[0]['language']}")
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+ ```
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+
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+ ### Applications
95
+
96
+ 1. **Language Identification Systems**
97
+ - Automatic language detection
98
+ - Text routing in multilingual systems
99
+ - Content filtering by language
100
+
101
+ 2. **Machine Translation**
102
+ - Language-pair identification
103
+ - Translation system selection
104
+
105
+ 3. **Content Analysis**
106
+ - Multilingual content categorization
107
+ - Language-specific content analysis
108
+
109
+ ### Citation
110
+
111
+ If you use this dataset in your research, please cite:
112
+
113
+ ```
114
+ @dataset{language_identification_2024,
115
+ author = {ML Technology Team},
116
+ title = {Multilingual Headlines Language Identification Dataset},
117
+ year = {2024},
118
+ publisher = {Hugging Face},
119
+ url = {https://huggingface.co/datasets/MLTechnology/language-identification}
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+ }
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+ ```
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+ ###reference
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+ ```
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+ @misc{disisbig_news_datasets,
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+ author = {Gaurav},
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+ title = {Indian Language News Datasets},
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+ year = {2019},
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+ publisher = {Kaggle},
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+ url = {https://www.kaggle.com/datasets/disisbig/}
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+ }
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+ ```
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+