# coding=utf-8 # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. # # 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. # Lint as: python3 """Linguistic Probing Benchmark from SentEval""" from __future__ import absolute_import, division, print_function import csv import os import textwrap import six import datasets _Linguisticprobing_CITATION = r"""@inproceedings{conneau-etal-2018-cram, title = "What you can cram into a single {\$}{\&}!{\#}* vector: Probing sentence embeddings for linguistic properties", author = {Conneau, Alexis and Kruszewski, German and Lample, Guillaume and Barrault, Lo{\"\i}c and Baroni, Marco}, booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P18-1198", doi = "10.18653/v1/P18-1198", pages = "2126--2136", abstract = "Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing. {``}Downstream{''} tasks, often based on sentence classification, are commonly used to evaluate the quality of sentence representations. The complexity of the tasks makes it however difficult to infer what kind of information is present in the representations. We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.", } """ _Linguisticprobing_DESCRIPTION = """\ 10 probing tasks designed to capture simple linguistic features of sentences, """ DATA_URL = "https://www.dropbox.com/s/djsk4kbu8in66gp/linguisticprobing.zip?dl=1" TASK_TO_LABELS = { "subj_number": ["NN", "NNS"], "word_content": [ "abandoned", "abruptly", "accent", "access", "according", "account", "ache", "ached", "acted", "acting", "actions", "actual", "address", "advantage", "advice", "afford", "agent", "agreement", "aiden", "airport", "alarm", "albert", "alert", "alexander", "alice", "alley", "allowing", "aloud", "amanda", "amber", "american", "amused", "amusement", "ancient", "andy", "annie", "announced", "annoyed", "answering", "anticipation", "anxious", "anybody", "apologize", "appearance", "appears", "approach", "approaching", "arched", "argue", "argument", "aria", "arrive", "ashe", "assume", "assured", "attached", "attacked", "attempted", "audience", "available", "awful", "awkward", "background", "backpack", "backward", "backwards", "bags", "balance", "bank", "barn", "bars", "bastard", "bath", "beating", "begged", "begins", "begun", "behavior", "bell", "belly", "belong", "belonged", "bench", "biggest", "bike", "billy", "bird", "birds", "birth", "birthday", "bitch", "bitter", "blank", "blast", "bleeding", "blind", "blocked", "blond", "blonde", "bond", "bone", "bored", "bothered", "bound", "bowed", "bowl", "boxes", "branches", "brave", "bread", "breast", "breaths", "brian", "brick", "brilliant", "broad", "brows", "brush", "brushing", "buddy", "build", "buildings", "bullet", "bunch", "butt", "cage", "cain", "caleb", "callum", "calmly", "cameron", "capable", "cards", "career", "carol", "caroline", "carriage", "cash", "cassidy", "casual", "catching", "causing", "centuries", "chain", "chairs", "challenge", "chamber", "chances", "changes", "changing", "charged", "checking", "chicken", "chief", "chill", "chloe", "chocolate", "choked", "chosen", "christian", "chuckle", "cigarette", "circles", "circumstances", "claim", "claimed", "clary", "classes", "claws", "clay", "cleaned", "cleaning", "clearing", "clicked", "cliff", "climb", "climbing", "closely", "closest", "cloth", "clothing", "cloud", "clue", "clung", "clutched", "coach", "cocked", "code", "colin", "collapsed", "collar", "colors", "commander", "comment", "complicated", "concrete", "condition", "confidence", "confident", "confirmed", "connected", "connor", "convince", "convinced", "cook", "cops", "copy", "corners", "correct", "cost", "courage", "cousin", "covering", "covers", "crack", "cracked", "crap", "crash", "crashed", "crawled", "cream", "create", "credit", "crept", "crime", "crossing", "crowded", "cruel", "crystal", "cupped", "curiosity", "current", "curse", "cursed", "cute", "cutting", "daemon", "dagger", "damned", "damp", "danced", "danny", "dante", "darkened", "darling", "darted", "dating", "dave", "dawn", "dealing", "delicate", "delicious", "della", "demons", "deny", "department", "desert", "deserved", "destroy", "destroyed", "detail", "detective", "devil", "devon", "digging", "dining", "direct", "directions", "disappear", "disappointed", "disappointment", "disbelief", "discuss", "disgust", "display", "distant", "distracted", "dogs", "dollars", "double", "doubted", "dozen", "drag", "dragging", "drake", "drank", "drawer", "drinks", "driveway", "dropping", "drops", "drug", "drugs", "ducked", "duke", "dull", "duncan", "duty", "dylan", "eager", "eased", "east", "eaten", "echo", "echoed", "eddie", "edges", "elbow", "elena", "ellie", "embarrassed", "embrace", "emerged", "emergency", "emotional", "ends", "enemies", "enormous", "envelope", "equipment", "evan", "event", "everybody", "exact", "exchange", "exchanged", "exclaimed", "exhausted", "exist", "existed", "existence", "exit", "expensive", "experienced", "explanation", "exploded", "exposed", "extended", "extremely", "eyed", "fabric", "fade", "fairly", "faith", "fake", "falls", "families", "fangs", "farm", "farther", "favor", "feared", "feed", "fellow", "fence", "fierce", "fifty", "file", "filling", "fingertips", "finn", "fired", "fixed", "flame", "flashlight", "flicked", "floating", "flushed", "foolish", "football", "footsteps", "forcing", "formed", "frame", "freedom", "french", "friday", "friendly", "frightened", "frown", "frowning", "froze", "frozen", "frustrated", "fuck", "fucking", "furious", "furniture", "fury", "gabe", "games", "garage", "garrett", "gary", "gasp", "gates", "gather", "gathering", "gavin", "gear", "gesture", "gestured", "ghost", "girlfriend", "glances", "glare", "glowing", "goodbye", "gorgeous", "government", "gown", "grabbing", "graham", "grand", "grandfather", "grandma", "grant", "grasp", "grave", "greeted", "grief", "grinning", "groan", "growl", "guessed", "guest", "guests", "guns", "gwen", "halfway", "hank", "hanna", "happily", "happiness", "harry", "harsh", "heal", "heartbeat", "heaven", "height", "hero", "hesitation", "hissed", "hitting", "holds", "holiday", "holly", "holy", "honor", "hopefully", "horizon", "houses", "humor", "hundreds", "hunger", "hunt", "hunting", "hurting", "ideas", "idiot", "ignoring", "images", "imagination", "impressed", "impression", "inches", "including", "incredible", "incredibly", "informed", "injured", "inner", "insane", "intended", "intensity", "invisible", "invited", "iron", "isaac", "issue", "items", "jamie", "jared", "jealous", "jeremy", "jerk", "jess", "jesus", "jimmy", "joey", "johnny", "jonas", "jordan", "joseph", "joshua", "josie", "journey", "judge", "jumping", "kane", "karen", "katherine", "katie", "keeps", "kevin", "kicking", "kidding", "killer", "kisses", "knelt", "knocking", "ladies", "landing", "language", "larger", "lately", "laura", "lauren", "lawyer", "lean", "leapt", "learning", "lesson", "letters", "licked", "lies", "lifting", "lightning", "likes", "lined", "liquid", "lobby", "location", "london", "lonely", "louder", "lover", "loves", "loving", "lucien", "lucy", "major", "mama", "manner", "marks", "martin", "mask", "mason", "mass", "massive", "mate", "material", "mattered", "matters", "meaning", "meat", "medical", "melissa", "member", "members", "mental", "mere", "merely", "midnight", "military", "minds", "miranda", "mist", "mixed", "moaned", "monday", "moonlight", "morgan", "mortal", "motioned", "mountains", "movements", "moves", "movies", "mumbled", "muscle", "mystery", "nails", "named", "nathan", "nearest", "necklace", "needing", "nerves", "nervously", "nicolas", "nicole", "nightmare", "nights", "nina", "nodding", "nods", "nora", "noted", "notes", "numbers", "object", "occurred", "offering", "officers", "oliver", "opens", "opinion", "option", "orange", "ordinary", "original", "ourselves", "overhead", "owner", "packed", "pages", "painful", "paint", "painted", "painting", "palace", "palms", "paris", "parted", "particularly", "partner", "parts", "passenger", "passion", "patch", "patience", "patient", "patrick", "patted", "paying", "period", "permission", "pete", "photo", "pile", "pillow", "pissed", "pity", "placing", "plain", "planet", "plastic", "pleasant", "plus", "pockets", "points", "polite", "popped", "positive", "possibility", "potential", "pounded", "powers", "precious", "prefer", "prepare", "president", "preston", "pretend", "pretending", "previous", "price", "pride", "princess", "prison", "professor", "program", "progress", "project", "proof", "proper", "property", "protection", "protest", "provide", "provided", "pulse", "punch", "punched", "pure", "purple", "puts", "quinn", "quit", "racing", "radio", "rafe", "raise", "raising", "rapidly", "rare", "rarely", "reaches", "react", "realizing", "rear", "rebecca", "recall", "received", "recently", "record", "reflection", "refuse", "regular", "reluctantly", "remaining", "remains", "remembering", "remind", "remove", "replaced", "request", "required", "rescue", "research", "resist", "responsibility", "responsible", "result", "returning", "reveal", "revealed", "revealing", "ribs", "riding", "rifle", "roar", "robe", "robert", "rode", "rope", "rubbing", "ruin", "ruined", "rule", "runs", "rushing", "sadly", "sadness", "sake", "sally", "satisfaction", "satisfied", "saturday", "savannah", "saving", "scanned", "scar", "scare", "scattered", "scott", "scrambled", "screams", "seated", "seats", "secrets", "section", "sees", "self", "sell", "sensation", "sensed", "senses", "sentence", "separate", "serena", "series", "serve", "served", "settle", "sexy", "shade", "shakes", "shame", "sharply", "shattered", "sheet", "sheets", "sheriff", "shield", "shining", "shiver", "shivered", "shooting", "shopping", "shore", "shortly", "shorts", "shout", "shouting", "shown", "shrug", "shuddered", "sidewalk", "sideways", "signal", "signs", "silk", "silly", "similar", "sisters", "site", "sits", "skills", "skirt", "skull", "slapped", "sleeve", "slide", "slipping", "slowed", "slumped", "smaller", "smith", "snap", "social", "society", "sofa", "somewhat", "sooner", "sophia", "sophie", "souls", "sounding", "source", "spare", "speech", "spencer", "spending", "spinning", "split", "spring", "squeeze", "stands", "stated", "statement", "states", "steal", "stepping", "stiff", "stirred", "stole", "stones", "stops", "straightened", "stream", "stretch", "strike", "strode", "stroked", "struggle", "struggling", "student", "students", "studying", "stuffed", "style", "success", "suggest", "sunday", "sunlight", "supplies", "surprisingly", "surrounding", "survived", "suspect", "suspected", "suspicious", "swallow", "sweetheart", "swiftly", "swing", "switch", "t-shirt", "tables", "tail", "tapped", "tara", "target", "task", "tasted", "taught", "taylor", "teach", "teacher", "teased", "teasing", "television", "tells", "temper", "temple", "tense", "tent", "terms", "terrified", "terror", "tessa", "text", "thigh", "thighs", "thousands", "threatened", "threatening", "throughout", "thrown", "thrust", "tighter", "till", "tipped", "toby", "toes", "tony", "tore", "torn", "total", "tough", "towel", "tower", "trace", "tracks", "traffic", "trailed", "trained", "trap", "trapped", "traveled", "travis", "tray", "treat", "treated", "trembled", "trevor", "trick", "tristan", "trunk", "trusted", "twelve", "ugly", "unconscious", "unexpected", "uniform", "universe", "unknown", "unlike", "unsure", "unusual", "upper", "upright", "upward", "useful", "useless", "valley", "vanished", "various", "vehicle", "veins", "victor", "video", "vincent", "violet", "visible", "vulnerable", "walks", "wandered", "warrior", "wash", "washed", "waving", "weather", "werewolf", "whipped", "whispers", "whom", "willow", "winced", "wings", "winked", "wise", "wishing", "witch", "wolves", "worn", "wounded", "wounds", "wrap", "wrapping", "wrists", "wrote", "xavier", "yanked", "yard", "yards", "yelling", "zach", ], "obj_number": ["NN", "NNS"], "past_present": ["PAST", "PRES"], "sentence_length": [0, 1, 2, 3, 4, 5], "top_constituents": [ "ADVP_NP_VP_.", "CC_ADVP_NP_VP_.", "CC_NP_VP_.", "IN_NP_VP_.", "NP_ADVP_VP_.", "NP_NP_VP_.", "NP_PP_.", "NP_VP_.", "OTHER", "PP_NP_VP_.", "RB_NP_VP_.", "SBAR_NP_VP_.", "SBAR_VP_.", "S_CC_S_.", "S_NP_VP_.", "S_VP_.", "VBD_NP_VP_.", "VP_.", "WHADVP_SQ_.", "WHNP_SQ_.", ], "tree_depth": ["depth_5", "depth_6", "depth_7", "depth_8", "depth_9", "depth_10", "depth_11"], "coordination_inversion": ["I", "O"], "odd_man_out": ["C", "O"], "bigram_shift": ["I", "O"], } def get_labels(task): return TASK_TO_LABELS[task] class LinguisticprobingConfig(datasets.BuilderConfig): """BuilderConfig for Linguisticprobing.""" def __init__( self, text_features, label_classes=None, process_label=lambda x: x, **kwargs, ): """BuilderConfig for Linguisticprobing. Args: text_features: `dict[string, string]`, map from the name of the feature dict for each text field to the name of the column in the tsv file label_column: `string`, name of the column in the tsv file corresponding to the label data_url: `string`, url to download the zip file from data_dir: `string`, the path to the folder containing the tsv files in the downloaded zip citation: `string`, citation for the data set url: `string`, url for information about the data set label_classes: `list[string]`, the list of classes if the label is categorical. If not provided, then the label will be of type `datasets.Value('float32')`. process_label: `Function[string, any]`, function taking in the raw value of the label and processing it to the form required by the label feature **kwargs: keyword arguments forwarded to super. """ super(LinguisticprobingConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) self.text_features = text_features self.label_column = "label" self.label_classes = get_labels(self.name) self.data_url = DATA_URL self.data_dir = os.path.join("linguisticprobing", self.name) self.citation = textwrap.dedent(_Linguisticprobing_CITATION) self.process_label = process_label self.description = "" self.url = "" class Linguisticprobing(datasets.GeneratorBasedBuilder): """The General Language Understanding Evaluation (Linguisticprobing) benchmark.""" BUILDER_CONFIG_CLASS = LinguisticprobingConfig BUILDER_CONFIGS = [ LinguisticprobingConfig( name="subj_number", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="word_content", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="obj_number", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="past_present", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="sentence_length", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="top_constituents", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="tree_depth", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="coordination_inversion", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="odd_man_out", text_features={"sentence": "sentence"}, ), LinguisticprobingConfig( name="bigram_shift", text_features={"sentence": "sentence"}, ), ] def _info(self): features = {text_feature: datasets.Value("string") for text_feature in six.iterkeys(self.config.text_features)} if self.config.label_classes: features["label"] = datasets.features.ClassLabel(names=self.config.label_classes) else: features["label"] = datasets.Value("float32") features["idx"] = datasets.Value("int32") return datasets.DatasetInfo( description=_Linguisticprobing_DESCRIPTION, features=datasets.Features(features), homepage=self.config.url, citation=self.config.citation + "\n" + _Linguisticprobing_CITATION, ) def _split_generators(self, dl_manager): dl_dir = dl_manager.download_and_extract(self.config.data_url) data_dir = os.path.join(dl_dir, self.config.data_dir) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_file": os.path.join(data_dir or "", "train.tsv"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "data_file": os.path.join(data_dir or "", "dev.tsv"), "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "data_file": os.path.join(data_dir or "", "test.tsv"), "split": "test", }, ), ] def _generate_examples(self, data_file, split): process_label = self.config.process_label label_classes = self.config.label_classes with open(data_file, encoding="utf8") as f: reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) for n, row in enumerate(reader): example = {feat: row[col] for feat, col in six.iteritems(self.config.text_features)} example["idx"] = n if self.config.label_column in row: label = row[self.config.label_column] if label_classes and label not in label_classes: label = int(label) if label else None example["label"] = process_label(label) else: example["label"] = process_label(-1) yield example["idx"], example