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Closes #424 | Add Dataloader Bactrian-X #552

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153 changes: 153 additions & 0 deletions seacrowd/sea_datasets/bactrian_x/bactrian_x.py
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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
from pathlib import Path
from typing import Dict, List, Tuple

import datasets

from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks, Licenses, TASK_TO_SCHEMA, SCHEMA_TO_FEATURES

_CITATION = """\
@misc{li2023bactrianx,
title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation},
author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin},
year={2023},
eprint={2305.15011},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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Unsure if we should follow this and cite the other papers (seem unrelated to the dataset)

image

"""

_DATASETNAME = "bactrian_x"

_DESCRIPTION = """\
The Bactrain-X dataset is a collection of 3.4M instruction-response pairs in 52
languages, that are obtained by translating 67K English instructions (alpaca-52k
+ dolly-15k) into 51 languages using Google Translate API. The translated
instructions are then fed to ChatGPT (gpt-3.5-turbo) to obtain its natural
responses, resulting in 3.4M instruction-response pairs in 52 languages (52
languages x 67k instances = 3.4M instances). Human evaluations were conducted to
evaluate response quality for several languages, with those of interest to
SEACrowd being Burmese and Tagalog.
"""

_HOMEPAGE = "https://github.com/mbzuai-nlp/Bactrian-X"

_LANGUAGES = ["mya", "tgl", "ind", "khm", "tha", "vie"]

_LICENSE = Licenses.CC_BY_NC_4_0.value

_LOCAL = False

_BASE_URL = "https://huggingface.co/datasets/MBZUAI/Bactrian-X/resolve/main/data/{subset}.json.gz?download=true"
_SUBSETS = ["my", "tl", "id", "km", "th", "vi"]

_SUPPORTED_TASKS = [Tasks.INSTRUCTION_TUNING]
_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}" # t2t

_SOURCE_VERSION = "1.0.1"

_SEACROWD_VERSION = "1.0.0"


class BactrianXDataset(datasets.GeneratorBasedBuilder):
"""A collection of translated instruction-response pairs, evaluated with ChatGPT and human."""

SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)

BUILDER_CONFIGS = []
for subset in _SUBSETS:
BUILDER_CONFIGS += [
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} {subset} source schema",
schema="source",
subset_id=subset,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} {subset} SEACrowd schema",
schema=_SEACROWD_SCHEMA,
subset_id=subset,
),
]

DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_id_source"

def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"instruction": datasets.Value("string"),
"input": datasets.Value("string"),
"id": datasets.Value("string"),
"output": datasets.Value("string"),
}
)
elif self.config.schema == _SEACROWD_SCHEMA:
features = SCHEMA_TO_FEATURES[
TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]
] # text2text_features

return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)

def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
data_url = _BASE_URL.format(subset=self.config.name.split("_")[2])
data_path = Path(dl_manager.download_and_extract(data_url))

return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_path": data_path,
},
)
]

def _generate_examples(self, data_path: Path) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
with open(data_path, "r", encoding="utf-8") as file:
data = json.load(file)

if self.config.schema == "source":
for idx, example in enumerate(data):
yield idx, {
"instruction": example["instruction"],
"input": example["input"],
"id": example["id"],
"output": example["output"],
}
elif self.config.schema == _SEACROWD_SCHEMA:
for idx, example in enumerate(data):
yield idx, {
"id": example["id"],
"text_1": f"Instruction: {example['instruction']}\nInput: {example['input']}",
"text_2": example["output"],
"text_1_name": "instruction + input",
"text_2_name": "output",
}