Datasets:
File size: 2,736 Bytes
c6dcf22 afbf725 c6dcf22 afbf725 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
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'])
```
|