# coding=utf-8 # Copyright 2020 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. """TODO: Add a description here.""" import csv import json import os import re from readline import parse_and_bind import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @inproceedings{thomson-etal-2020-sportsett, title = "{S}port{S}ett:Basketball - A robust and maintainable data-set for Natural Language Generation", author = "Thomson, Craig and Reiter, Ehud and Sripada, Somayajulu", booktitle = "Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation", month = sep, year = "2020", address = "Santiago de Compostela, Spain", publisher = "Association for Computational Lingustics", url = "https://aclanthology.org/2020.intellang-1.4", pages = "32--40", } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ SportSett:Basketball dataset for Data-to-Text Generation contains NBA games stats aligned with their human written summaries. """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/nlgcat/sport_sett_basketball" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" # TODO: Add link to the official dataset URLs here # The HuggingFace dataset library don't host the datasets but only point to the original files # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLs = { "train": "train.jsonl", "validation": "validation.jsonl", "test": "test.jsonl" } def detokenize(text): """ Untokenizing a text undoes the tokenizing operation, restoring punctuation and spaces to the places that people expect them to be. Ideally, `untokenize(tokenize(text))` should be identical to `text`, except for line breaks. """ step1 = text.replace("`` ", '"').replace(" ''", '"').replace('. . .', '...') step2 = step1.replace(" ( ", " (").replace(" ) ", ") ") step3 = re.sub(r' ([.,:;?!%]+)([ \'"`])', r"\1\2", step2) step4 = re.sub(r' ([.,:;?!%]+)$', r"\1", step3) step5 = step4.replace(" '", "'").replace(" n't", "n't").replace( "can not", "cannot").replace(" 've", "'ve") step6 = step5.replace(" ` ", " '") return step6.strip() # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class SportsettBasketball(datasets.GeneratorBasedBuilder): """SportSett:Basketball datatset for Data-to-Text Generation.""" VERSION = datasets.Version("1.1.0") def _info(self): features = datasets.Features( { "sportsett_id": datasets.Value("string"), "gem_id": datasets.Value("string"), "game": { "day": datasets.Value("string"), "month": datasets.Value("string"), "year": datasets.Value("string"), "dayname": datasets.Value("string"), "season": datasets.Value("string"), "stadium": datasets.Value("string"), "city": datasets.Value("string"), "state": datasets.Value("string"), "attendance": datasets.Value("string"), "capacity": datasets.Value("string"), "game_id": datasets.Value("string") }, "teams": { "home": { "name": datasets.Value("string"), "place": datasets.Value("string"), "conference": datasets.Value("string"), "division": datasets.Value("string"), "wins": datasets.Value("string"), "losses": datasets.Value("string"), "conference_standing": datasets.Value("int32"), "game_number": datasets.Value("string"), "previous_game_id": datasets.Value("string"), "next_game_id": datasets.Value("string"), "line_score": { "game": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PF": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "H1": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "H2": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q1": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q2": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q3": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q4": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "OT": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") } }, "box_score": [ { "first_name": datasets.Value("string"), "last_name": datasets.Value("string"), "name": datasets.Value("string"), "starter": datasets.Value("string"), "MIN": datasets.Value("string"), "FGM": datasets.Value("string"), "FGA": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3A": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FTM": datasets.Value("string"), "FTA": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "OREB": datasets.Value("string"), "DREB": datasets.Value("string"), "TREB": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "BLK": datasets.Value("string"), "TOV": datasets.Value("string"), "PF": datasets.Value("string"), "PTS": datasets.Value("string"), "+/-": datasets.Value("string"), "DOUBLE": datasets.Value("string") } ], "next_game": { "day": datasets.Value("string"), "month": datasets.Value("string"), "year": datasets.Value("string"), "dayname": datasets.Value("string"), "stadium": datasets.Value("string"), "city": datasets.Value("string"), "opponent_name": datasets.Value("string"), "opponent_place": datasets.Value("string"), "is_home": datasets.Value("string"), } }, "vis": { "name": datasets.Value("string"), "place": datasets.Value("string"), "conference": datasets.Value("string"), "division": datasets.Value("string"), "wins": datasets.Value("string"), "losses": datasets.Value("string"), "conference_standing": datasets.Value("int32"), "game_number": datasets.Value("string"), "previous_game_id": datasets.Value("string"), "next_game_id": datasets.Value("string"), "line_score": { "game": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PF": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "H1": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "H2": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q1": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q2": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q3": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "Q4": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") }, "OT": { "FG3A": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FGA": datasets.Value("string"), "FGM": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FTA": datasets.Value("string"), "FTM": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "DREB": datasets.Value("string"), "OREB": datasets.Value("string"), "TREB": datasets.Value("string"), "BLK": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "TOV": datasets.Value("string"), "PTS": datasets.Value("string"), "MIN": datasets.Value("string") } }, "box_score": [ { "first_name": datasets.Value("string"), "last_name": datasets.Value("string"), "name": datasets.Value("string"), "starter": datasets.Value("string"), "MIN": datasets.Value("string"), "FGM": datasets.Value("string"), "FGA": datasets.Value("string"), "FG_PCT": datasets.Value("string"), "FG3M": datasets.Value("string"), "FG3A": datasets.Value("string"), "FG3_PCT": datasets.Value("string"), "FTM": datasets.Value("string"), "FTA": datasets.Value("string"), "FT_PCT": datasets.Value("string"), "OREB": datasets.Value("string"), "DREB": datasets.Value("string"), "TREB": datasets.Value("string"), "AST": datasets.Value("string"), "STL": datasets.Value("string"), "BLK": datasets.Value("string"), "TOV": datasets.Value("string"), "PF": datasets.Value("string"), "PTS": datasets.Value("string"), "+/-": datasets.Value("string"), "DOUBLE": datasets.Value("string") } ], "next_game": { "day": datasets.Value("string"), "month": datasets.Value("string"), "year": datasets.Value("string"), "dayname": datasets.Value("string"), "stadium": datasets.Value("string"), "city": datasets.Value("string"), "opponent_name": datasets.Value("string"), "opponent_place": datasets.Value("string"), "is_home": datasets.Value("string"), } } }, "summaries": datasets.Sequence(datasets.Value("string")), "target": datasets.Value("string"), "references": [datasets.Value("string")], "linearized_input": datasets.Value("string") } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive data_dir = dl_manager.download_and_extract(_URLs) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir["train"], "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir["test"], "split": "test" }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir["validation"], "split": "validation", }, ), ] def tokenize_initials(self, value): attrib_value = re.sub(r"(\w)\.(\w)\.", r"\g<1>. \g<2>.", value) return attrib_value def sort_players_by_pts(self, entry, type='HOME'): """ Sort players by points and return the indices sorted by points bs --> [{'pts': 10}, {'pts': 30}, {'pts': 35}, {'pts': 5}] return --> [2, 1, 0, 3] """ all_pts = [int(item['PTS']) for item in entry['teams'][type.lower()]['box_score']] all_pts1 = [[item, idx] for idx, item in enumerate(all_pts)] all_pts1.sort() all_pts1.reverse() return [item[1] for item in all_pts1] def get_one_player_data(self, player_stats, team_name, rank): """ player_line = " %s %s %s %s %d %d %d %d %d %d %d %d \ %d %d %d %d %d %s %d %d %d %d" """ pos = f'STARTER YES' if player_stats['starter'] == True else f'STARTER NO' player_min = int(player_stats['MIN']) rank = rank if player_min > 0 else f"{rank.split('-')[0]}-DIDNTPLAY" player_line = f" {self.tokenize_initials(player_stats['name'])} {team_name} {pos} {rank}" player_line = f"{player_line} {player_stats['MIN']} {player_stats['PTS']} {player_stats['FGM']} {player_stats['FGA']} {player_stats['FG_PCT']}" player_line = f"{player_line} {player_stats['FG3M']} {player_stats['FG3A']} {player_stats['FG3_PCT']}" player_line = f"{player_line} {player_stats['FTM']} {player_stats['FTA']} {player_stats['FT_PCT']}" player_line = f"{player_line} {player_stats['TREB']} {player_stats['AST']} {player_stats['STL']}" player_line = f"{player_line} {player_stats['BLK']} {player_stats['DREB']} {player_stats['OREB']} {player_stats['TOV']}" player_line = f"{player_line} {player_stats['DOUBLE']}" return player_line def get_box_score(self, entry, type='HOME'): bs = entry['teams'][type.lower()]['box_score'] team_name = f"{entry['teams'][type.lower()]['place']} {entry['teams'][type.lower()]['name']}" sorted_idx = self.sort_players_by_pts(entry, type) player_lines = [self.get_one_player_data(bs[idx], team_name, f'{type}-{rank}') for rank, idx in enumerate(sorted_idx)] return ' '.join(player_lines) def get_team_line(self, entry, type='HOME', winner='HOME'): """ team_line = "%s %s %s %s %d %d %d %d %d %d %d \ %d <3PT> %d %d %d %d %d" """ line_score = entry['teams'][type.lower()]['line_score']['game'] team_line = f" {entry['teams'][type.lower()]['name']} {entry['teams'][type.lower()]['place']}" if winner == type: team_line = f"{team_line} won" else: team_line = f"{team_line} lost" team_line = f"{team_line} {line_score['PTS']} {entry['teams'][type.lower()]['wins']} {entry['teams'][type.lower()]['losses']}" team_line = f"{team_line} {entry['teams'][type.lower()]['line_score']['Q1']['PTS']} {entry['teams'][type.lower()]['line_score']['Q2']['PTS']}" team_line = f"{team_line} {entry['teams'][type.lower()]['line_score']['Q3']['PTS']} {entry['teams'][type.lower()]['line_score']['Q4']['PTS']}" team_line = f"{team_line} {line_score['AST']} <3PT> {line_score['FG3M']} {line_score['FGM']} {line_score['FTM']}" team_line = f"{team_line} {line_score['TREB']} {line_score['TOV']}" return team_line def get_box_and_line_scores(self, entry): """Get line- & box- scores data for a single game""" home_team_pts = entry['teams']['home']['line_score']['game']['PTS'] vis_team_pts = entry['teams']['home']['line_score']['game']['PTS'] winner = 'HOME' if int(home_team_pts) > int(vis_team_pts) else 'VIS' home_team_line = self.get_team_line(entry, type='HOME', winner=winner) vis_team_line = self.get_team_line(entry, type='VIS', winner=winner) home_box_score = self.get_box_score(entry, type='HOME') vis_box_score = self.get_box_score(entry, type='VIS') return home_team_line, vis_team_line, home_box_score, vis_box_score def get_game_data(self, entry): """Get game data for a single game""" game_date = f"{entry['day']} {entry['month']} {entry['year']}" game_day = entry['dayname'] game_stadium = entry['stadium'] game_city = entry['city'] return f" {game_date} {game_day} {game_stadium} {game_city}" def get_next_game_data_of_a_team(self, entry): """ next_game_line = " %s %s %s %s" """ next_game_date = f"{entry['day']} {entry['month']} {entry['year']}" next_game_is_home = 'yes' if entry['is_home'] == 'True' else 'no' next_game_line = f" {next_game_date} {entry['dayname']}" next_game_line = f"{next_game_line} {entry['stadium']} {entry['city']}" next_game_line = f"{next_game_line} {entry['opponent_place']} {entry['opponent_name']}" next_game_line = f"{next_game_line} {next_game_is_home}" return next_game_line def get_next_game_info(self, entry): """ Get next game data for both teams in a game. In case of no next game, all values will be ''. """ home_next_game = self.get_next_game_data_of_a_team(entry['teams']['home']['next_game']) vis_next_game = self.get_next_game_data_of_a_team(entry['teams']['vis']['next_game']) return home_next_game, vis_next_game def linearize_input(self, entry): """ Linearizes the input to the model. """ game_data = self.get_game_data(entry['game']) home_line, vis_line, home_box_score, vis_box_score = self.get_box_and_line_scores(entry) home_next, vis_next = self.get_next_game_info(entry) linearized_input = f" {game_data} {home_line} {home_next} {vis_line} {vis_next} {home_box_score} {vis_box_score}" return linearized_input def _generate_examples( self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """ Yields examples as (key, example) tuples. """ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. # js = json.load(open(filepath, encoding="utf-8")) with open(filepath, encoding="utf-8") as f: for id_, row in enumerate(f): data = json.loads(row) yield id_, { "sportsett_id": data["sportsett_id"], "gem_id": data["gem_id"], "game": data["game"], "teams": data["teams"], "summaries": data["summaries"], "target": detokenize(data["summaries"][0]), "references": [detokenize(s) for s in data["summaries"]], "linearized_input": self.linearize_input(data) }