import json import os import zipfile from typing import List import datasets import pandas as pd from datasets import ClassLabel, Value, load_dataset _URLS = { "go_emotions": { "urls": [ "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv", "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv", "https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv", ], "license": "apache license 2.0" }, "daily_dialog": { "urls": ["http://yanran.li/files/ijcnlp_dailydialog.zip"], "license": "CC BY-NC-SA 4.0" }, "emotion": { "data": ["data/data.jsonl.gz"], "license": "educational/research" } } _SUB_CLASSES = [ "anger", "fear", "joy", "love", "sadness", "surprise", "neutral", ] _CLASS_NAMES = [ "no emotion", "happiness", "admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion", "curiosity", "desire", "disappointment", "disapproval", "disgust", "embarrassment", "excitement", "fear", "gratitude", "grief", "joy", "love", "nervousness", "optimism", "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral", ] class EmotionsDatasetConfig(datasets.BuilderConfig): def __init__(self, features, label_classes, **kwargs): super().__init__(**kwargs) self.features = features self.label_classes = label_classes class EmotionsDataset(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ EmotionsDatasetConfig( name="all", label_classes=_CLASS_NAMES, features=["text", "label", "dataset", "license"] ), EmotionsDatasetConfig( name="multilingual", label_classes=_SUB_CLASSES, features=["text", "label", "dataset", "license"] ) ] DEFAULT_CONFIG_NAME = "all" def _info(self): if self.config.name == "all": return datasets.DatasetInfo( features=datasets.Features( { "id": datasets.Value("string"), 'text': Value(dtype='string', id=None), 'label': ClassLabel(names=_CLASS_NAMES, id=None), 'dataset': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None) } ) ) else: return datasets.DatasetInfo( features=datasets.Features( { "id": datasets.Value("string"), 'text': Value(dtype='string', id=None), 'label': ClassLabel(names=_SUB_CLASSES, id=None), 'dataset': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None) } ) ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: splits = [] if self.config.name == "all": for k, v in _URLS.items(): downloaded_files = dl_manager.download_and_extract(v.get("urls", v.get("data"))) splits.append(datasets.SplitGenerator(name=k, gen_kwargs={"filepaths": downloaded_files, "dataset": k, "license": v.get("license")})) else: downloaded_files = dl_manager.download_and_extract(["data/many_emotions.json.gz"]) for lang in ["en", "fr", "it", "es", "de"]: splits.append(datasets.SplitGenerator(name=lang, gen_kwargs={"filepaths": downloaded_files, "language": lang, "dataset": "many_emotions"})) return splits def process_daily_dialog(self, filepaths, dataset): # TODO move outside emo_mapping = {0: "no emotion", 1: "anger", 2: "disgust", 3: "fear", 4: "happiness", 5: "sadness", 6: "surprise"} for i, filepath in enumerate(filepaths): if os.path.isdir(filepath): emotions = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_emotion.txt"), "r").read() text = open(os.path.join(filepath, "ijcnlp_dailydialog/dialogues_text.txt"), "r").read() else: # TODO check if this can be removed archive = zipfile.ZipFile(filepath, 'r') emotions = archive.open("ijcnlp_dailydialog/dialogues_emotion.txt", "r").read().decode() text = archive.open("ijcnlp_dailydialog/dialogues_text.txt", "r").read().decode() emotions = emotions.split("\n") text = text.split("\n") for idx_out, (e, t) in enumerate(zip(emotions, text)): if len(t.strip()) > 0: cast_emotions = [int(j) for j in e.strip().split(" ")] cast_dialog = [d.strip() for d in t.split("__eou__") if len(d)] for idx_in, (ce, ct) in enumerate(zip(cast_emotions, cast_dialog)): uid = f"daily_dialog_{i}_{idx_out}_{idx_in}" yield uid, {"text": ct, "id": uid, "dataset": dataset, "license": license, "label": emo_mapping[ce]} def _generate_examples(self, filepaths, dataset, license=None, language=None): if dataset == "go_emotions": for i, filepath in enumerate(filepaths): df = pd.read_csv(filepath) current_classes = list(set(df.columns).intersection(set(_CLASS_NAMES))) df = df[["text"] + current_classes] df = df[df[current_classes].sum(axis=1) == 1].reset_index(drop=True) for row_idx, row in df.iterrows(): uid = f"go_emotions_{i}_{row_idx}" yield uid, {"text": row["text"], "id": uid, "dataset": dataset, "license": license, "label": row[current_classes][row == 1].index.item()} elif dataset == "daily_dialog": for d in self.process_daily_dialog(filepaths, dataset): yield d elif dataset == "emotion": emo_mapping = {0: "sadness", 1: "joy", 2: "love", 3: "anger", 4: "fear", 5: "surprise"} for i, filepath in enumerate(filepaths): with open(filepath, encoding="utf-8") as f: for idx, line in enumerate(f): uid = f"{dataset}_{idx}" example = json.loads(line) example.update({ "id": uid, "dataset": dataset, "license": license, "label": emo_mapping[example["label"]] }) yield uid, example elif dataset == "many_emotions": for i, filepath in enumerate(filepaths): with open(filepath, encoding="utf-8") as f: for idx, line in enumerate(f): example = json.loads(line) if language != "all": example = { "id": example["id"], 'text': example["text" if language == "en" else language], 'label': example["label"], 'dataset': example["dataset"], 'license': example["license"] } label = _CLASS_NAMES[example["label"]] if label == "no emotion": label = "neutral" elif label == "happiness": label = "joy" example.update({ "label": label }) yield example["id"], example if __name__ == "__main__": dataset = load_dataset("ma2za/many_emotions", name="all", split="emotion") print()