willwade/txt-sms-abbreviations
Dataset Card for TXT/SMS Abbreviations 1539 sms/text speak abbreviations. Note we have parsed through NLTK to give a field of whether its generally recognised a whole word. NB: Numbers are NOT noted as words. Be careful with numbers like 4 2 etc in your training data Dataset Details Dataset Description A curated list of abbreviations commonly used in text messages and gaming chat and other places. We have tried to remove explanations and explicitly… See the full description on the dataset page: https://huggingface.co/datasets/willwade/txt-sms-abbreviations.
Dataset Card for TXT/SMS Abbreviations
1539 sms/text speak abbreviations. Note we have parsed through NLTK to give a field of whether its generally recognised a whole word. NB: Numbers are NOT noted as words. Be careful with numbers like 4 2 etc in your training data
Dataset Details
Dataset Description
A curated list of abbreviations commonly used in text messages and gaming chat and other places. We have tried to remove explanations and explicitly put a full expansion of the abbreviation. Be careful - some items are whole words already.
- Curated by: Will Wade
- Language(s) (NLP): English
- License: Apache-2
Uses
Use as a training set for abbreviations or as part of a GEC task
import csv
# Load abbreviations and their expansions from sms.csv into a dictionary
abbreviations_dict = {}
with open('sms.csv', mode='r', encoding='utf-8') as infile:
reader = csv.reader(infile, delimiter=',')
next(reader, None) # Skip the header if present
for row in reader:
if len(row) >= 3:
abbreviation = row[0].strip().upper()
expansion = row[1].strip()
is_word = row[2].strip().lower() in ['true', 'yes', '1'] # Convert to boolean
abbreviations_dict[abbreviation] = (expansion, is_word)
else:
print(f"Skipping malformed row: {row}")
