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--- |
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dataset_info: |
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features: |
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- name: ml |
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dtype: string |
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- name: en |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1133730838 |
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num_examples: 27787044 |
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download_size: 520146321 |
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dataset_size: 1133730838 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: cc |
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language: |
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- ml |
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- en |
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size_categories: |
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- 1M<n<10M |
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--- |
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## English Malayalam Names |
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### Dataset Description |
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This dataset has 27787044 person names both in English and Malayalam. The source for this dataset is various election roles published by Government. |
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Derived From: https://huggingface.co/datasets/santhosh/english-malayalam-names |
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- **Curated by:** Bajiyo Baiju |
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- **License:** CC-BY-SA-4.0 |
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## Uses |
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- English <-> Malayalam name transliteration tasks |
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- Named entity recognition |
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- Person name recognition |
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## Dataset Curation |
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``` |
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# Assuming 'ml' is the column containing Malayalam names and 'en' is the English names column in your dataset |
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from datasets import load_dataset |
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data = load_dataset("santhosh/english-malayalam-names") |
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malayalam_names = data['ml'].tolist() |
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english_names = data['en'].tolist() |
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# Define a function to check if a name contains mostly English characters |
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def is_english_name(name): |
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english_char_count = sum(c.isalpha() and c.isascii() for c in name) |
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return english_char_count / len(name) > 0.5 # Adjust the threshold as needed |
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# Find and count names that are likely to be English in 'ml' column |
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english_names_ml_column = [name for name in malayalam_names if is_english_name(name)] |
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count_english_names_ml_column = len(english_names_ml_column) |
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# Find Malayalam words in the 'en' column |
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malayalam_words_en_column = [word for word in english_names if not any(c.isascii() for c in word)] |
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count_malayalam_words_en_column = len(malayalam_words_en_column) |
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# Print the results |
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print("Count of English-like Names in Malayalam Names Column:", count_english_names_ml_column) |
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#print("English-like Names in Malayalam Names Column:", english_names_ml_column) |
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print("\nCount of Malayalam Words in English Names Column:", count_malayalam_words_en_column) |
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print("Malayalam Words in English Names Column:", malayalam_words_en_column) |
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# Identify English-like names and remove them |
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english_names_mask = data['ml'].isin(english_names_ml_column) |
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data = data[~english_names_mask] |
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# Identify Malayalam words and remove them |
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malayalam_words_mask = data['en'].isin(malayalam_words_en_column) |
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data = data[~malayalam_words_mask] |
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# Remove empty rows |
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data = data[(data['ml'] != '') & (data['en'] != '')] |
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# Verify the changes |
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print("Updated 'ml' column after removing English-like Names:") |
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print(data['ml']) |
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print("\nUpdated 'en' column after removing Malayalam Words:") |
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print(data['en']) |
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``` |
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