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