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import json |
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import re |
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from typing import List |
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import html |
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import datasets |
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ENTITY = 'entity' |
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ENTITY_PATTERN = r'<entity>{}</entity>' |
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logger = datasets.logging.get_logger(__name__) |
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class RedialConfig(datasets.BuilderConfig): |
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"""BuilderConfig for ReDIAL.""" |
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def __init__(self, features, |
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initiator_prefix='User: ', |
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respondent_prefix='System: ', |
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**kwargs): |
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"""BuilderConfig for ReDIAL. |
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Args: |
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features: *list[string]*, list of the features that will appear in the |
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feature dict. Should not include "label". |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(version=datasets.Version("0.0.1"), **kwargs) |
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self.features = features |
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self.initiator_prefix = initiator_prefix |
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self.respondent_prefix = respondent_prefix |
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_URL = "./" |
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_URLS = { |
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"train": _URL + "train.jsonl", |
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"valid": _URL + "valid.jsonl", |
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"test": _URL + "test.jsonl", |
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} |
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class ReDIAL(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG_NAME = "rec" |
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BUILDER_CONFIGS = [ |
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RedialConfig( |
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name="SA", |
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description="For using the ReDIAL dataset to train sentiment analysis on movies in sentences", |
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features={ |
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"movieId": datasets.Value("int32"), |
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"movieName": datasets.Value("string"), |
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"messages": datasets.features.Sequence(datasets.Value("string")), |
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"senders": datasets.features.Sequence(datasets.Value("int32")), |
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"form": datasets.features.Sequence( |
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datasets.Value("int32"), length=6 |
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) |
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}, |
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), |
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RedialConfig( |
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name="autorec", |
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description="For training autorec model on ReDIAL data", |
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features=datasets.Features({ |
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"movieIds": datasets.Sequence(datasets.Value("int32")), |
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"ratings": datasets.Sequence(datasets.Value("float")) |
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}), |
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), |
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RedialConfig( |
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name="rec", |
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description="For using the ReDIAL dataset to train recommender", |
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features={ |
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"movieIds": datasets.Sequence(datasets.Value("int32")), |
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"messages": datasets.features.Sequence(datasets.Value("string")), |
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"senders": datasets.features.Sequence(datasets.Value("int32")), |
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}, |
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), |
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RedialConfig( |
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name="formatted", |
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description='Embed all information into a text sequence for each dialog', |
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features={ |
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"messages": datasets.features.Sequence(datasets.Value("string")), |
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} |
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) |
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] |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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self.last_sender = None |
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def _processMessage(self, msg, initialId): |
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""" |
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msg example: { |
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"timeOffset": 0, |
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"text": "Hi I am looking for a movie like @111776", |
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"senderWorkerId": 956, |
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"messageId": 204171 |
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}, |
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""" |
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res = { |
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"text": msg["text"], |
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"sender": 1 if msg["senderWorkerId"] == initialId else -1 |
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} |
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return res |
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def _flattenMessages(self, conversation, add_prefix=False): |
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messages = [] |
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senders = [] |
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for message in conversation["messages"]: |
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role = 1 if message["senderWorkerId"] == conversation["initiatorWorkerId"] else -1 |
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text = message["text"] |
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if len(senders) > 0 and senders[-1] == role: |
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messages[-1] += "\n" + text |
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else: |
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senders.append(role) |
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if add_prefix: |
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prefix = self.config.initiator_prefix if role == 1 else self.config.respondent_prefix |
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text = prefix + text |
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messages.append(text) |
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return messages, senders |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=self.config.description, |
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features=datasets.Features(self.config.features), |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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urls_to_download = _URLS |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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movie_pattern = re.compile(r'@(\d+)') |
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default_movie_entity = '<movie>' |
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def _process_utt(self, utt, movieid2name, replace_movieId=True, remove_movie=False): |
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def convert(match): |
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movieid = match.group(0)[1:] |
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if movieid in movieid2name: |
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if remove_movie: |
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return '<movie>' |
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movie_name = movieid2name[movieid] |
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movie_name = ' '.join(movie_name.split()) |
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return ENTITY_PATTERN.format(movie_name) |
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else: |
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return match.group(0) |
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if replace_movieId: |
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utt = re.sub(self.movie_pattern, convert, utt) |
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utt = ' '.join(utt.split()) |
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utt = html.unescape(utt) |
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return utt |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", filepath) |
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if self.config.name == "autorec": |
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with open(filepath, encoding="utf-8") as f: |
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idx = 0 |
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for line in f: |
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conversation = json.loads(line) |
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movieIds = [] |
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ratings = [] |
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if len(conversation["initiatorQuestions"]) == 0: |
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continue |
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for id, form in conversation["initiatorQuestions"].items(): |
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rating = int(form["liked"]) |
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if rating < 2: |
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movieIds.append(id) |
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ratings.append(rating) |
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if len(movieIds) > 0: |
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yield idx, { |
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"movieIds": movieIds, |
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"ratings": ratings |
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} |
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idx += 1 |
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elif "SA" in self.config.name: |
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Idx = 0 |
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date_pattern = re.compile(r'\(\d{4}\)') |
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with open(filepath, encoding="utf-8") as f: |
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for line in f: |
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conversation = json.loads(line) |
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init_q = conversation["initiatorQuestions"] |
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resp_q = conversation["respondentQuestions"] |
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msgs, senders = self._flattenMessages(conversation) |
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gen = [key for key in init_q if key in resp_q] |
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for id in gen: |
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movieName = date_pattern.sub('', conversation["movieMentions"][id]).strip(" ") |
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if len(movieName) == 0: |
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continue |
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yield Idx, { |
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"movieId": int(id), |
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"movieName": movieName, |
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"messages": msgs, |
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"senders": senders, |
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"form": [init_q[id]["suggested"], init_q[id]["seen"], init_q[id]["liked"], |
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resp_q[id]["suggested"], resp_q[id]["seen"], resp_q[id]["liked"], ] |
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} |
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Idx += 1 |
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if Idx > 100 and "debug" in self.config.name: |
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break |
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elif "rec" in self.config.name: |
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Idx = 0 |
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with open(filepath, encoding="utf-8") as f: |
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for line in f: |
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conversation = json.loads(line) |
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msgs, senders = self._flattenMessages(conversation) |
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yield Idx, { |
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"messages": msgs, |
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"senders": senders, |
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"movieIds": [int(movieId) for movieId in conversation["movieMentions"]] |
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} |
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Idx += 1 |
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elif "formatted" in self.config.name: |
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Idx = 0 |
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with open(filepath, encoding="utf-8") as f: |
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for line in f: |
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dialog = json.loads(line) |
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msgs, senders = self._flattenMessages(dialog, add_prefix=True) |
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movieid2name = dialog['movieMentions'] |
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formatted_msgs = [self._process_utt(utt, movieid2name) for utt in msgs] |
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yield Idx, { |
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"messages": formatted_msgs, |
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} |
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Idx += 1 |
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