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import json
import random
import uuid
from dataclasses import dataclass
import datasets
import iso639
import language_names
import language_paraphrase
import language_translate
import pandas as pd
random.seed(42)
class DataProcess:
# list of random quotes
random_quote = [("'", "'"), ("“", "”"), ("῎", "῏"), ("`", "´"), ("«", "»"), ('"', '"')]
# provide instruction with a text; process of randomization of a text
def randomize_text(self, text, original_lang=None, target_lang=None):
templates = (
language_translate.random_templates_translate.get(original_lang, {})
if not ((original_lang == target_lang) and (original_lang is not None) and (target_lang is not None))
else language_paraphrase.random_templates_paraphrase.get(original_lang, {})
)
template = random.choice(list(templates.values()))
quote_pair = random.choice(DataProcess().random_quote)
opening_quote, closing_quote = quote_pair
original_lang_name = DataProcess.language_name(None, original_lang, original_lang)
target_lang_name = DataProcess.language_name(None, target_lang, original_lang)
return template.format(
text=text,
lang1=target_lang_name,
lang2=original_lang_name,
opening_quote=opening_quote,
closing_quote=closing_quote,
)
# convert to iso639_1
def convert_code(self, code):
mapped_code = iso639.to_iso639_1(code)
return mapped_code
# return language #1 name in language #2
def language_name(self, lang1, lang2):
name = language_names.language_names.get(lang1, {}).get(lang2)
if name is not None:
return name
# just in case
elif lang1 == lang2:
iso_name = iso639.to_native(lang1)
return iso_name
else:
return None
converter = DataProcess()
"""
EXAMPLES:
# get language name; iso639_1 code
print(converter.language_name('ru', 'en')) # Output: Russian
print(converter.convert_code("eng")) # Output: en
# convert into INSTRUCTION format: text; to; from
text = "test"
print(converter.randomize_text(text, "uk", "fr")) # Ти можеш перекласти цей вислів: 'test'?
print(converter.randomize_text(text, "uk", "de")) # Переклади наступний текст "test" з мови "німецька мова"
"""
@dataclass
class QnA:
INSTRUCTION: str
RESPONSE: str
SOURCE: str
METADATA: str
# format to QnA
def create_qna(row):
# get rows; create uuid based on texts
text = row["Text"]
text_length = len(text)
translation = row["Translated text"]
lang_from = converter.convert_code(row["Original lang"])
lang_to = converter.convert_code(row["Target lang"])
uuid_val = uuid.uuid3(uuid.NAMESPACE_OID, str(text + translation))
# json with language, original text length, uuid and langs-pair
METADATA = {
"language": f"{lang_to}",
"length": f"{text_length}",
"uuid": f"{uuid_val}",
"langs-pair": f"{lang_from}-{lang_to}",
}
metadata_str = json.dumps(METADATA)
source = "tatoeba"
# randomizing INSTRUCTION
instruction = converter.randomize_text(text, lang_to, lang_from)
response = translation
return QnA(instruction, response, source, metadata_str)
# load the dataset from Hugging Face
hf_dataset = datasets.load_dataset("0x22almostEvil/tatoeba-mt-llama-only", split="train")
# original is ~3M; with num_shards=30 it'll be ~120K
hf_dataset = hf_dataset.shard(num_shards=30, index=0)
print(hf_dataset)
# convert the dataset to a pandas dataframe
df = pd.DataFrame(hf_dataset)
# apply the create_qna function to each row of the dataframe to create QnA objects
qna_list = df.apply(create_qna, axis=1).tolist()
# save the QnA objects as a parquet file
qna_df = pd.DataFrame(qna_list, columns=["INSTRUCTION", "RESPONSE", "SOURCE", "METADATA"])
qna_df.to_parquet("translation-taboeba-qna-120k-oa.parquet", row_group_size=100, engine="pyarrow", index=False)
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