label_studio.py

# coding=utf-8
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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 argparse
import copy
import json
import os
import random
import time
from decimal import Decimal

import numpy as np
import paddle
from utils import load_txt

from paddlenlp.trainer.argparser import strtobool
from paddlenlp.utils.log import logger


def set_seed(seed):
    paddle.seed(seed)
    random.seed(seed)
    np.random.seed(seed)


PROMPT_ITEMS = {
    "aspect_prompt_prefix": "评价维度",
    "opinion_prompt": "观点词",
    "sentiment_prompt_prefix": "情感倾向",
    "separator": "##",
    "not_mentioned_option": "未提及",
    "positive_option": "正向",
    "negative_option": "负向",
}


class Convertor(object):
    """Convertor to convert data export from annotation platform"""

    def __init__(self, negative_ratio=5):
        """Init Data Convertor"""
        self.negative_ratio = negative_ratio
        self.aspect_prompt_prefix = PROMPT_ITEMS["aspect_prompt_prefix"]
        self.opinion_prompt = PROMPT_ITEMS["opinion_prompt"]
        self.sentiment_prompt_prefix = PROMPT_ITEMS["sentiment_prompt_prefix"]
        self.separator = PROMPT_ITEMS["separator"]
        self.not_mentioned_option = PROMPT_ITEMS["not_mentioned_option"]
        self.options = PROMPT_ITEMS["options"]

    def process_text_tag(self, line, task_type="ext"):
        items = {}
        items["text"] = line["data"]["text"]
        if task_type == "ext":
            items["entities"] = []
            items["relations"] = []
            items["relation_ids"] = set()
            result_list = line["annotations"][0]["result"]
            for result in result_list:
                if result["type"] == "labels":
                    items["entities"].append(
                        {
                            "id": result["id"],
                            "start_offset": result["value"]["start"],
                            "end_offset": result["value"]["end"],
                            "label": result["value"]["labels"][0],
                        }
                    )
                else:
                    items["relations"].append(
                        {
                            "id": result["from_id"] + "-" + result["to_id"],
                            "from_id": result["from_id"],
                            "to_id": result["to_id"],
                            "type": result["labels"][0] if result["labels"] else self.opinion_prompt,
                        }
                    )
                    items["relation_ids"].add(result["from_id"])
                    items["relation_ids"].add(result["to_id"])

        elif task_type == "cls":
            items["label"] = line["annotations"][0]["result"][0]["value"]["choices"]
        return items

    def convert_cls_examples(self, raw_examples, data_flag="Data"):
        """
        Convert labeled data for classification task.
        """
        examples = []
        logger.info("{0:7} Start to convert annotation data.".format("[" + data_flag + "]"))
        for line in raw_examples:
            items = self.process_text_tag(line, task_type="cls")
            text, labels = items["text"], items["label"]
            example = self.generate_cls_example(text, labels, self.sentiment_prompt_prefix, self.options)
            examples.append(example)
        logger.info("{0:7} End to convert annotation data.\n".format(""))
        return examples

    def convert_ext_examples(
        self,
        raw_examples,
        synonyms=None,
        implicit_opinion_map=None,
        sentiment_map=None,
        with_negatives=True,
        task_type="ext_aso",
        data_flag="Data",
    ):
        """
        Convert labeled data for extraction task.
        """

        def _sep_cls_label(label, separator):
            label_list = label.split(separator)
            if len(label_list) == 1:
                return label_list[0], None
            return label_list[0], label_list[1:]

        texts = []
        # {"content": "", "result_list": [], "prompt": "X"}
        entity_examples = []
        # {"content": "", "result_list": [], "prompt": "X的Y"}
        relation_examples = []
        # {"content": "", "result_list": [], "prompt": "X的情感倾向[正向，负向]"}
        entity_cls_examples = []

        # entity label set: ["评价维度", "观点词", ... ]
        entity_label_set = []
        # predicate set: ["观点词", ... ]
        predicate_set = []
        # set of subject entity in relation: ["房间", "价格", ... ]
        subject_name_set = []

        # List[List[str]]
        # List of entity prompt for each example
        entity_prompt_list = []
        # Golden subject label for each example
        subject_golden_list = []
        # List of inverse relation for each example
        inverse_relation_list = []
        # List of predicate for each example
        predicate_list = []

        logger.info("{0:7} Start to convert annotation data.".format("[" + data_flag + "]"))
        logger.info("{0:7} Trying to generate positive examples...".format(""))
        for line in raw_examples:
            items = self.process_text_tag(line, task_type="ext")

            text, relations, entities, relation_ids = (
                items["text"],
                items["relations"],
                items["entities"],
                items["relation_ids"],
            )
            texts.append(text)

            entity_example = []
            entity_prompt = []
            entity_example_map = {}
            implict_example_map = {}
            entity_map = {}
            subject_golden = []
            for entity in entities:
                entity_name = text[entity["start_offset"] : entity["end_offset"]]
                entity_map[entity["id"]] = {
                    "name": entity_name,
                    "start": entity["start_offset"],
                    "end": entity["end_offset"],
                }

                entity_label, entity_cls_label = _sep_cls_label(entity["label"], self.separator)

                # generate examples for entity-level sentiment classification
                if entity_cls_label is not None:
                    entity_cls_prompt_prefix = entity_name + "的" + self.sentiment_prompt_prefix
                    entity_cls_example = self.generate_cls_example(
                        text, entity_cls_label, entity_cls_prompt_prefix, self.options
                    )

                    entity_cls_examples.append(entity_cls_example)

                # generate examples for entity extraction
                result = {"text": entity_name, "start": entity["start_offset"], "end": entity["end_offset"]}
                if entity_label not in entity_example_map.keys():
                    entity_example_map[entity_label] = {
                        "content": text,
                        "result_list": [result],
                        "prompt": entity_label,
                    }
                else:
                    entity_example_map[entity_label]["result_list"].append(result)

                if entity_label not in entity_label_set:
                    entity_label_set.append(entity_label)
                entity_prompt.append(entity_label)

                if implicit_opinion_map and entity["id"] not in relation_ids:
                    maped_entity = entity_map[entity["id"]]
                    if maped_entity["name"] not in implicit_opinion_map:
                        continue

                    result = {
                        "text": maped_entity["name"],
                        "start": maped_entity["start"],
                        "end": maped_entity["end"],
                    }
                    aspect = implicit_opinion_map[maped_entity["name"]]
                    if aspect not in implict_example_map:
                        implict_example_map[aspect] = [result]
                    else:
                        implict_example_map[aspect].append(result)

                if entity_label.startswith(self.aspect_prompt_prefix):
                    if entity_name not in subject_golden:
                        if synonyms and entity_name in synonyms:
                            subject_synonyms = synonyms[entity_name]
                            subject_golden.extend(subject_synonyms)
                        else:
                            subject_golden.append(entity_name)

                    if entity_name not in subject_name_set:
                        subject_name_set.append(entity_name)

            for v in entity_example_map.values():
                entity_example.append(v)
            entity_examples.append(entity_example)
            entity_prompt_list.append(entity_prompt)

            # generate examples for classification of implicit opinion
            if task_type == "ext_as" or task_type == "ext_aso":
                for entity_name in implict_example_map.keys():
                    prompt = entity_name + "的" + self.sentiment_prompt_prefix
                    opinions = implict_example_map[entity_name]
                    sentiment = None
                    for opinion in opinions:
                        if opinion["text"] in sentiment_map:
                            sentiment = sentiment_map[opinion["text"]]
                            break
                    if sentiment is None:
                        continue
                    implicit_example = self.generate_cls_example(text, [sentiment], prompt, self.options)
                    entity_cls_examples.append(implicit_example)

            # generate examples for relation extraction
            # Golden entity inputs, initializing with implicit subject and it's synonyms
            for implicit_subject in implict_example_map.keys():
                subject_golden.append(implicit_subject)
                if synonyms and implicit_subject in synonyms:
                    subject_golden.extend(synonyms[implicit_subject])
            relation_example = []
            relation_example_map = {}
            inverse_relation = []
            predicates = []

            # generate examples for extraction of implicit opinion
            for entity_name in implict_example_map.keys():
                prompt = entity_name + "的" + self.opinion_prompt
                implicit_example = {
                    "content": text,
                    "result_list": implict_example_map[entity_name],
                    "prompt": prompt,
                }
                relation_example.append(implicit_example)

            # generate examples for labeled relations
            for relation in relations:
                predicate = relation["type"]
                subject_id = relation["from_id"]
                object_id = relation["to_id"]

                prompt = entity_map[subject_id]["name"] + "的" + predicate
                inverse_negative = entity_map[object_id]["name"] + "的" + predicate

                result = {
                    "text": entity_map[object_id]["name"],
                    "start": entity_map[object_id]["start"],
                    "end": entity_map[object_id]["end"],
                }

                inverse_relation.append(inverse_negative)
                predicates.append(predicate)

                if prompt not in relation_example_map.keys():
                    relation_example_map[prompt] = {"content": text, "result_list": [result], "prompt": prompt}
                else:
                    relation_example_map[prompt]["result_list"].append(result)

                if predicate not in predicate_set:
                    predicate_set.append(predicate)

            for v in relation_example_map.values():
                relation_example.append(v)

            relation_examples.append(relation_example)
            subject_golden_list.append(subject_golden)
            inverse_relation_list.append(inverse_relation)
            predicate_list.append(predicates)

        # start to generate negative examples
        if with_negatives and task_type in ["ext_as", "ext_ao", "ext_aso"]:
            logger.info("{0:7} Trying to generate negative examples...".format(""))

        # generate negative examples according to entity
        all_entity_examples = []
        if with_negatives:
            positive_examples, negative_examples = self.add_entity_negative_example(
                entity_examples, texts, entity_prompt_list, entity_label_set
            )
            if len(positive_examples) != 0:
                all_entity_examples = positive_examples + negative_examples
        else:
            for i in range(len(entity_examples)):
                all_entity_examples.extend(entity_examples[i])

        # generate negative examples according to relation
        all_relation_examples = []
        if with_negatives:
            if len(predicate_set) != 0:
                positive_examples = []
                negative_examples = []
                per_n_ratio = self.negative_ratio // 3

                for i, text in enumerate(texts):
                    negative_example = []
                    collects = []

                    # 1. inverse_relation_list
                    redundants1 = inverse_relation_list[i]

                    # 2. subject_name_set - subject_golden_list[i]
                    redundants2 = []
                    if len(predicate_list[i]) != 0:
                        nonentity_list = list(set(subject_name_set) - set(subject_golden_list[i]))
                        nonentity_list.sort()

                        redundants2 = [
                            nonentity + "的" + predicate_list[i][random.randrange(len(predicate_list[i]))]
                            for nonentity in nonentity_list
                        ]

                    # 3. entity_label_set - entity_prompt_list[i]
                    redundants3 = []
                    if len(subject_golden_list[i]) != 0:
                        non_ent_label_list = list(set(entity_label_set) - set(entity_prompt_list[i]))
                        non_ent_label_list.sort()

                        redundants3 = [
                            subject_golden_list[i][random.randrange(len(subject_golden_list[i]))] + "的" + non_ent_label
                            for non_ent_label in non_ent_label_list
                        ]

                    redundants_list = [redundants1, redundants2, redundants3]

                    for redundants in redundants_list:
                        added, rest = self.add_relation_negative_example(redundants, texts[i], per_n_ratio)
                        negative_example.extend(added)
                        collects.extend(rest)
                    num_sup = self.negative_ratio - len(negative_example)
                    if num_sup > 0 and collects:
                        if num_sup > len(collects):
                            idxs = [k for k in range(len(collects))]
                        else:
                            idxs = random.sample(range(0, len(collects)), num_sup)
                        for idx in idxs:
                            negative_example.append(collects[idx])
                    positive_examples.extend(relation_examples[i])
                    negative_examples.extend(negative_example)

                all_relation_examples = positive_examples + negative_examples
        else:
            for i in range(len(relation_examples)):
                all_relation_examples.extend(relation_examples[i])

        # generate negative examples according to sentiment polarity
        all_cls_examples = entity_cls_examples
        if with_negatives:
            if task_type == "ext_aso" or task_type == "ext_as" and self.not_mentioned_option in self.options:
                cls_negatives_examples = self.add_cls_negative_example(texts, subject_name_set, subject_golden_list)
                all_cls_examples += cls_negatives_examples

        # generate examples with synonyms to support aspect aggregation
        if synonyms is not None:
            synonym_map = {}
            for k, vs in synonyms.items():
                for v in vs:
                    synonym_map[v] = k
            relation_synonym_examples = self.change_aspect_with_synonyms(all_relation_examples, synonyms, synonym_map)
            all_relation_examples += relation_synonym_examples
            cls_synonym_examples = self.change_aspect_with_synonyms(all_cls_examples, synonyms, synonym_map)
            all_cls_examples += cls_synonym_examples

        logger.info("{0:7} End to convert annotation data.\n".format(""))
        return all_entity_examples + all_relation_examples + all_cls_examples

    def change_aspect_with_synonyms(self, examples, synonyms, synonym_map):
        synonym_examples = []
        for example in examples:
            prompt = example["prompt"]
            aspect, suffix = prompt.split("的", maxsplit=1)
            if aspect not in synonym_map.keys():
                continue
            synonym_cluster = synonyms[synonym_map[aspect]]
            for syn_aspect in synonym_cluster:
                if syn_aspect == aspect:
                    continue
                syn_prompt = syn_aspect + "的" + suffix
                syn_example = copy.deepcopy(example)
                syn_example["prompt"] = syn_prompt
                synonym_examples.append(syn_example)
        return synonym_examples

    def generate_cls_example(self, text, labels, prompt_prefix, options):
        random.shuffle(self.options)
        cls_options = ",".join(self.options)
        prompt = prompt_prefix + "[" + cls_options + "]"

        result_list = []
        example = {"content": text, "result_list": result_list, "prompt": prompt}

        for label in labels:
            start = prompt.rfind(label) - len(prompt) - 1
            end = start + len(label)
            result = {"text": label, "start": start, "end": end}
            example["result_list"].append(result)
        return example

    def add_entity_negative_example(self, examples, texts, prompts, label_set):
        negative_examples = []
        positive_examples = []
        for i, prompt in enumerate(prompts):
            redundants = list(set(label_set) - set(prompt))
            redundants.sort()

            ratio = self.negative_ratio
            if ratio > len(redundants):
                ratio = len(redundants)
            idxs = random.sample(range(0, len(redundants)), ratio)

            for idx in idxs:
                negative_result = {"content": texts[i], "result_list": [], "prompt": redundants[idx]}
                negative_examples.append(negative_result)
            positive_examples.extend(examples[i])
        return positive_examples, negative_examples

    def add_relation_negative_example(self, redundants, text, ratio):
        added_example = []
        rest_example = []

        if ratio > len(redundants):
            ratio = len(redundants)

        all_idxs = [k for k in range(len(redundants))]
        idxs = random.sample(range(0, len(redundants)), ratio)
        rest_idxs = list(set(all_idxs) - set(idxs))

        for idx in idxs:
            negative_result = {"content": text, "result_list": [], "prompt": redundants[idx]}
            added_example.append(negative_result)

        for rest_idx in rest_idxs:
            negative_result = {"content": text, "result_list": [], "prompt": redundants[rest_idx]}
            rest_example.append(negative_result)

        return added_example, rest_example

    def add_cls_negative_example(self, texts, subject_name_set, subject_golden_list):
        negative_examples = []
        for i, text in enumerate(texts):
            redundants = list(set(subject_name_set) - set(subject_golden_list[i]))
            redundants.sort()

            ratio = self.negative_ratio
            if ratio > len(redundants):
                ratio = len(redundants)
            idxs = random.sample(range(0, len(redundants)), ratio)

            for idx in idxs:
                subject_name = redundants[idx]
                prompt_prefix = subject_name + "的" + self.sentiment_prompt_prefix
                negative_example = self.generate_cls_example(text, ["未提及"], prompt_prefix, self.options)
                negative_examples.append(negative_example)
        return negative_examples


def load_synonym(synonym_path):
    synonyms = {}
    lines = load_txt(synonym_path)
    for line in lines:
        items = line.split()
        synonyms[items[0]] = items
    return synonyms


def load_implicit_opinion(implicit_opinion_path):
    implicit_opinion_map = {}
    sentiment_map = {}
    lines = load_txt(implicit_opinion_path)
    for line in lines:
        items = line.split(",")
        aspect = items[0].strip()
        for item in items[1:]:
            item = item.strip()
            start = item.find("[")
            end = item.find("]")
            sentiment = item[0:start]
            opinions = item[start + 1 : end].strip().split()
            for opinion in opinions:
                implicit_opinion_map[opinion] = aspect
                sentiment_map[opinion] = sentiment
    return implicit_opinion_map, sentiment_map


def parse_ext_task_type(raw_examples):

    task_type_dict = {"ext_a": False, "ext_o": False, "ext_ao": False, "ext_as": False, "ext_aso": False}

    def _parse_raw_example(raw_example):
        entity_map = {}
        relations = []
        result_list = raw_example["annotations"][0]["result"]
        for result in result_list:
            if result["type"] == "labels":
                entity_id = result["id"]
                entity_map[entity_id] = result["value"]["labels"][0]
            elif result["type"] == "relation":
                relation_pair = (result["from_id"], result["to_id"])
                relations.append(relation_pair)
            else:
                raise ValueError(
                    "Unknown entity type [{}], it indicates that your dataset maybe not a aspect-based extraction dataset, please check it.".format(
                        result["type"]
                    )
                )

        for entity_label in entity_map.values():
            if (
                entity_label.startswith(PROMPT_ITEMS["aspect_prompt_prefix"])
                and PROMPT_ITEMS["separator"] in entity_label
            ):
                task_type_dict["ext_as"] = True
            elif entity_label == PROMPT_ITEMS["aspect_prompt_prefix"]:
                task_type_dict["ext_a"] = True
            elif entity_label == PROMPT_ITEMS["opinion_prompt"]:
                task_type_dict["ext_o"] = True
            else:
                raise ValueError("Unknown prompt: {}".format(entity_label))

        # relations store the relation between aspect and opinion by default
        if relations:
            task_type_dict["ext_ao"] = True

        if task_type_dict["ext_ao"] and task_type_dict["ext_as"]:
            task_type_dict["ext_aso"] = True

    for raw_example in raw_examples:
        # analyze task type
        _parse_raw_example(raw_example)
        if task_type_dict["ext_aso"]:
            return "ext_aso"
        elif (not task_type_dict["ext_as"]) and task_type_dict["ext_ao"]:
            return "ext_ao"

    if task_type_dict["ext_as"]:
        return "ext_as"
    elif task_type_dict["ext_o"]:
        return "ext_o"
    else:
        return "ext_a"


def do_convert():
    set_seed(args.seed)

    tic_time = time.time()
    if not os.path.exists(args.label_studio_file):
        raise ValueError("Please input the correct path of label studio file.")

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    if len(args.splits) != 0 and len(args.splits) != 3:
        raise ValueError("Only []/ len(splits)==3 accepted for splits.")

    def _check_sum(splits):
        return Decimal(str(splits[0])) + Decimal(str(splits[1])) + Decimal(str(splits[2])) == Decimal("1")

    if len(args.splits) == 3 and not _check_sum(args.splits):
        raise ValueError("Please set correct splits, sum of elements in splits should be equal to 1.")

    with open(args.label_studio_file, "r", encoding="utf-8") as f:
        raw_examples = json.loads(f.read())

    if args.is_shuffle:
        indexes = np.random.permutation(len(raw_examples))
        raw_examples = [raw_examples[i] for i in indexes]

    # construct options according
    if args.options:
        PROMPT_ITEMS["options"] = args.options
    else:
        if args.task_type == "ext":
            PROMPT_ITEMS["options"] = [
                PROMPT_ITEMS["positive_option"],
                PROMPT_ITEMS["negative_option"],
                PROMPT_ITEMS["not_mentioned_option"],
            ]
        else:
            PROMPT_ITEMS["options"] = [PROMPT_ITEMS["positive_option"], PROMPT_ITEMS["negative_option"]]

    # analyze detailed ext task type: ext_a, ext_o, ext_as, ext_ao, ext_aso
    if args.task_type == "ext":
        args.task_type = parse_ext_task_type(raw_examples)

    logger.info("You are trying perform dataset construction operation for task {}.\n".format(args.task_type))

    # load synonyms
    synonyms = None
    if args.synonym_file:
        if args.task_type in ["cls", "ext_a", "ext_o"]:
            logger.warning(
                "The param synonym_file will not work for task, because the task {} that you wanna try does not support synonym_function.".format(
                    args.task_type
                )
            )
        else:
            if not os.path.isfile(args.synonym_file):
                raise ValueError(
                    "The path you input is not a file, please input the correct path of synonym file: {}".format(
                        args.synonym_file
                    )
                )
            synonyms = load_synonym(args.synonym_file)

    # load implicit opinions
    implicit_opinion_map = None
    sentiment_map = None
    if args.implicit_file:
        if args.task_type in ["cls", "ext_a", "ext_o", "ext_as"]:
            logger.warning(
                "The param implicit_file will not work for task, because the task {} that you wanna try does not support implicit opinion function.".format(
                    args.task_type
                )
            )
        else:
            if not os.path.isfile(args.implicit_file):
                raise ValueError(
                    "The path you input is not a file, please input the correct path of implicit opinion file: {}".format(
                        args.implicit_file
                    )
                )
            implicit_opinion_map, sentiment_map = load_implicit_opinion(args.implicit_file)

    # split examples into train/dev/test examples
    i1, i2, _ = args.splits
    p1 = int(len(raw_examples) * i1)
    p2 = int(len(raw_examples) * (i1 + i2))

    # define Convertor and convert raw examples to model examples
    convertor = Convertor(negative_ratio=args.negative_ratio)

    if args.task_type.startswith("ext"):
        train_examples = convertor.convert_ext_examples(
            raw_examples[:p1],
            synonyms=synonyms,
            implicit_opinion_map=implicit_opinion_map,
            sentiment_map=sentiment_map,
            task_type=args.task_type,
            data_flag="Train",
        )
        dev_examples = convertor.convert_ext_examples(
            raw_examples[p1:p2],
            synonyms=synonyms,
            implicit_opinion_map=implicit_opinion_map,
            sentiment_map=sentiment_map,
            task_type=args.task_type,
            data_flag="Dev",
        )
        test_examples = convertor.convert_ext_examples(
            raw_examples[p2:],
            synonyms=synonyms,
            implicit_opinion_map=implicit_opinion_map,
            sentiment_map=sentiment_map,
            task_type=args.task_type,
            data_flag="Test",
        )
    else:
        train_examples = convertor.convert_cls_examples(raw_examples[:p1], data_flag="Train")
        dev_examples = convertor.convert_cls_examples(raw_examples[p1:p2], data_flag="Dev")
        test_examples = convertor.convert_cls_examples(raw_examples[p2:], data_flag="Test")

    # save examples
    def _save_examples(save_dir, file_name, examples):
        count = 0
        save_path = os.path.join(save_dir, file_name)
        with open(save_path, "w", encoding="utf-8") as f:
            for example in examples:
                f.write(json.dumps(example, ensure_ascii=False) + "\n")
                count += 1
        logger.info("Save %d examples to %s." % (count, save_path))

    _save_examples(args.save_dir, "train.json", train_examples)
    _save_examples(args.save_dir, "dev.json", dev_examples)
    _save_examples(args.save_dir, "test.json", test_examples)

    logger.info("Finished! It takes {:.2f} seconds".format(time.time() - tic_time))


if __name__ == "__main__":
    # yapf: disable
    parser = argparse.ArgumentParser()

    parser.add_argument("--label_studio_file", default="./data/label_studio.json", type=str, help="The annotation file exported from label studio platform.")
    parser.add_argument("--synonym_file", type=str, help="The synonmy file of aspect to support aspect aggregation.")
    parser.add_argument("--implicit_file", type=str, help="The implicit opinion file whose aspect not be mentioned in text, to support extraction of implicit opinion.")
    parser.add_argument("--save_dir", default="./data", type=str, help="The path of data that you wanna save.")
    parser.add_argument("--negative_ratio", default=5, type=int, help="Worked only for the extraction task, it means that for each task (aspect-based opinion extraction, aspect-based sentiment classicition) of an example, at least negative_ratio negative examples will be generated without considering synonym_file and implicit_file.")
    parser.add_argument("--splits", default=[0.8, 0.1, 0.1], type=float, nargs="*", help="The ratio of samples in datasets. [0.6, 0.2, 0.2] means 60% samples used for training, 20% for evaluation and 20% for test.")
    parser.add_argument("--task_type", choices=['ext', 'cls'], default="ext", type=str, help="Two task types [ext, cls] are supported, ext represents the aspect-based extraction task and cls represents the sentence-level classification task, defaults to ext.")
    parser.add_argument("--options", type=str, nargs="+", help="Used only for the classification task, the options for classification")
    parser.add_argument("--is_shuffle", type=strtobool, default="True", help="Whether to shuffle the labeled dataset, defaults to True.")
    parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization")

    args = parser.parse_args()
    # yapf: enablecl
    logger.info("Parameter Description:\n{}\n".format(args.__dict__))

    do_convert()