Convert dataset to Parquet (#4)
Browse files- Convert dataset to Parquet (d77a99e6499f42dd4e031b0ea0845ccaa69aaa8d)
- Delete data file (7589957123fc6aa686c3ebaee671da717bdf5efc)
- Delete loading script (08a19786ffd04846adf149f6105ec78778156155)
- Delete data file (18754307222ceffa4b92630398e6400c030e5d36)
- EEG-semantic-text-relevance.py +0 -176
- README.md +42 -0
- data/metadataForCleanedEEG.pkl → cleaned/train-00000-of-00024.parquet +2 -2
- data/cleanedEEG.npy → cleaned/train-00001-of-00024.parquet +2 -2
- cleaned/train-00002-of-00024.parquet +3 -0
- cleaned/train-00003-of-00024.parquet +3 -0
- cleaned/train-00004-of-00024.parquet +3 -0
- cleaned/train-00005-of-00024.parquet +3 -0
- cleaned/train-00006-of-00024.parquet +3 -0
- cleaned/train-00007-of-00024.parquet +3 -0
- cleaned/train-00008-of-00024.parquet +3 -0
- cleaned/train-00009-of-00024.parquet +3 -0
- cleaned/train-00010-of-00024.parquet +3 -0
- cleaned/train-00011-of-00024.parquet +3 -0
- cleaned/train-00012-of-00024.parquet +3 -0
- cleaned/train-00013-of-00024.parquet +3 -0
- cleaned/train-00014-of-00024.parquet +3 -0
- cleaned/train-00015-of-00024.parquet +3 -0
- cleaned/train-00016-of-00024.parquet +3 -0
- cleaned/train-00017-of-00024.parquet +3 -0
- cleaned/train-00018-of-00024.parquet +3 -0
- cleaned/train-00019-of-00024.parquet +3 -0
- cleaned/train-00020-of-00024.parquet +3 -0
- cleaned/train-00021-of-00024.parquet +3 -0
- cleaned/train-00022-of-00024.parquet +3 -0
- cleaned/train-00023-of-00024.parquet +3 -0
EEG-semantic-text-relevance.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""TODO: Add a description here."""
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import csv
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import datasets
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import numpy as np
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import pandas as pd
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings{Submitted to NeurIPS 2024,
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title = {An EEG dataset of word-level brain responses for
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semantic text relevance},
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author={Vadym Gryshchuk, Michiel Spapé, Maria Maistro, Christina Lioma,
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Tuukka Ruotsalo},
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year={2024}
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}
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"""
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# You can copy an official description
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_DESCRIPTION = """\
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A dataset containing 23,270 time-locked (0.7s) word-level EEG
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recordings acquired from 15 participants who read both text that was
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semantically relevant and irrelevant to self-selected topics.
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"""
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_HOMEPAGE = "https://vadymv.github.io/EEG-dataset-for-semantic-text-relevance/"
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_LICENSE = "Apache Licence 2.0"
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_URLS = {
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"cleaned": {
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"eeg": "./data/cleanedEEG.npy",
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"metadata": "./data/metadataForCleanedEEG.pkl"
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}
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}
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class EEGSemanticTextRelevance(datasets.GeneratorBasedBuilder):
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"""
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A dataset containing 23,270 time-locked (0.7s) word-level EEG
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recordings acquired from 15 participants who read both text that was
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semantically relevant and irrelevant to self-selected topics."""
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VERSION = datasets.Version("1.1.0")
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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# If you need to make complex sub-parts in the datasets with configurable options
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# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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# BUILDER_CONFIG_CLASS = MyBuilderConfig
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# You will be able to load one or the other configurations in the following list with
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="cleaned", version=VERSION,
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description="Load the preprocessed (cleaned) EEG data"),
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]
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DEFAULT_CONFIG_NAME = "cleaned" # It's not mandatory to have a default configuration. Just use one if it make sense.
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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if self.config.name == "cleaned": # This is the name of the configuration selected in BUILDER_CONFIGS above
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features = datasets.Features(
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{
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"event": datasets.Value("int64"),
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"word": datasets.Value("string"),
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"topic": datasets.Value("string"),
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"selected_topic": datasets.Value("string"),
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"semantic_relevance": datasets.Value("int64"),
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"interestingness": datasets.Value("int64"),
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"pre-knowledge": datasets.Value("int64"),
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"sentence_number": datasets.Value("int64"),
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"participant": datasets.Value("string"),
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"eeg": datasets.Array2D(shape=(32, 2001), dtype="float64"),
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# These are the features of your dataset like images, labels ...
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}
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)
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else: # This is an example to show how to have different features for "first_domain" and "second_domain"
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raise ValueError("Not implemented.")
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features,
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# Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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# supervised_keys=("sentence", "label"),
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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# data_dir = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath_eeg": urls["eeg"],
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"filepath_metadata": urls["metadata"],
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},
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),
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# datasets.SplitGenerator(
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# name=datasets.Split.VALIDATION,
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# # These kwargs will be passed to _generate_examples
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# gen_kwargs={
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# "filepath": os.path.join(data_dir, "dev.jsonl"),
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# "split": "dev",
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# },
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# ),
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# datasets.SplitGenerator(
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# name=datasets.Split.TEST,
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# # These kwargs will be passed to _generate_examples
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# gen_kwargs={
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# "filepath": os.path.join(data_dir, "test.jsonl"),
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# "split": "test"
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# },
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# ),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath_eeg, filepath_metadata):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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eeg_data = np.load(filepath_eeg)
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metadata = pd.read_pickle(filepath_metadata)
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for key, row in metadata.iterrows():
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if self.config.name == "cleaned":
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# Yields examples as (key, example) tuples
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yield key, {
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"event": row["event"],
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"word": row["word"],
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"topic": row["topic"],
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"selected_topic": row["selected_topic"],
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"semantic_relevance": row["semantic_relevance"],
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"interestingness": row["interestingness"],
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"pre-knowledge": row["pre-knowledge"],
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"sentence_number": row["sentence_number"],
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"participant": row["participant"],
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"eeg": eeg_data[key],
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}
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else:
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raise ValueError("Not implemented.")
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README.md
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We release a dataset containing 23,270 time-locked (0.7s) word-level EEG recordings acquired from 15 participants who read both text that was semantically relevant and irrelevant to self-selected topics.
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---
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dataset_info:
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config_name: cleaned
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features:
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- name: event
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dtype: int64
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- name: word
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dtype: string
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- name: topic
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dtype: string
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- name: selected_topic
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dtype: string
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- name: semantic_relevance
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dtype: int64
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- name: interestingness
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dtype: int64
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- name: pre-knowledge
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dtype: int64
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- name: sentence_number
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dtype: int64
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- name: participant
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dtype: string
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- name: eeg
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dtype:
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array2_d:
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shape:
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- 32
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- 2001
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dtype: float64
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splits:
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- name: train
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num_bytes: 11925180913
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num_examples: 23270
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download_size: 11927979870
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dataset_size: 11925180913
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configs:
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- config_name: cleaned
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data_files:
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- split: train
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path: cleaned/train-*
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default: true
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---
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We release a dataset containing 23,270 time-locked (0.7s) word-level EEG recordings acquired from 15 participants who read both text that was semantically relevant and irrelevant to self-selected topics.
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data/metadataForCleanedEEG.pkl → cleaned/train-00000-of-00024.parquet
RENAMED
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:213df2d4738344c52eacc5124a6893117f6dbdc18f49b98ccfb947d39e5a9b38
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size 497284247
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data/cleanedEEG.npy → cleaned/train-00001-of-00024.parquet
RENAMED
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 497284013
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cleaned/train-00002-of-00024.parquet
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version https://git-lfs.github.com/spec/v1
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size 497060154
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cleaned/train-00003-of-00024.parquet
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+
version https://git-lfs.github.com/spec/v1
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oid sha256:7a135b6e47a58f65e0f01399a260ae94dbf1840dd9d621d29c8ff62355a81135
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size 496996991
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cleaned/train-00004-of-00024.parquet
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:2bf9eccd579aff0491ca73aa0a48f4261fd7265da68176db059be28896649318
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size 497284491
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cleaned/train-00005-of-00024.parquet
ADDED
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version https://git-lfs.github.com/spec/v1
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size 497284596
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cleaned/train-00006-of-00024.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:a2663678bb16533c78750ded4670fd95674586d17c6573151b88e51ea160feae
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size 497284105
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cleaned/train-00007-of-00024.parquet
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:ecf0f304e8d7bb7f219805b3bd0f39acf4d6c3e7b0d8aa2aca6d48db3995912a
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size 497285071
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cleaned/train-00008-of-00024.parquet
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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