Add Tatoeba QnA dataset (#3114)

Multilingual Tatoeba Q&A Translation Dataset
============================================

120K entries

This dataset contains a list of instructions to translate or paraphrase
in multiple languages. It is available in Parquet format and includes
the following columns:

* INSTRUCTION: The instruction of text to be translated or paraphrased.
* RESPONSE: The corresponding response or answer in target language.
* SOURCE (tatoeba): The original source text from the Tatoeba database.
* METADATA (json): Additional information about each entry, including
the target language, UUID, and pair of languages (source and target):
`"{"language": "lang", "length": "length of original text","uuid": "uuid
(original text + translated text)", "langs-pair": "from_lang-to_lang"}"`

The data in this dataset was collected through crowdsourcing efforts and
includes translations of various types of content, such as sentences,
phrases, idioms, and proverbs.

You can find it here:
https://huggingface.co/datasets/0x22almostEvil/tatoeba-mt-qna-oa
Original dataset is available here:
https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt

---

Resolves #3115

---------

Co-authored-by: 0x22almostEvil <0x22almostEvil>

data/datasets/__init__.py

@@ -27,6 +27,7 @@
     "oa_stackexchange": "donfu/oa-stackexchange",
     "stable_diffusion_instructional_dataset": "MadVoyager/stable_diffusion_instructional_dataset",
     "ru_riddles_337": "0x22almostEvil/ru-riddles-377",
+    "tatoeba_mt_qna_oa": "0x22almostEvil/tatoeba-mt-qna-oa",
 }
 
 SAFETY_DATASETS = {

data/datasets/tatoeba_mt_qna_oa/README.MD

@@ -0,0 +1,24 @@
+# Multilingual Tatoeba Q&A Translation Dataset
+
+120K entries
+
+This dataset contains a list of instructions to translate or paraphrase in
+multiple languages. It is available in Parquet format and includes the following
+columns:
+
+- INSTRUCTION: The instruction of text to be translated or paraphrased.
+- RESPONSE: The corresponding response or answer in target language.
+- SOURCE (tatoeba): The original source text from the Tatoeba database.
+- METADATA (json): Additional information about each entry, including the target
+  language, UUID, and pair of languages (source and target). "{"language":
+  "lang", "length": "length of original text","uuid": "uuid (original text +
+  translated text)", "langs-pair": "from_lang-to_lang"}"
+
+The data in this dataset was collected through crowdsourcing efforts and
+includes translations of various types of content, such as sentences, phrases,
+idioms, and proverbs.
+
+You can find it here:
+https://huggingface.co/datasets/0x22almostEvil/tatoeba-mt-qna-oa Original
+dataset is available here:
+https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt

data/datasets/tatoeba_mt_qna_oa/data_process.py

@@ -0,0 +1,121 @@
+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)