Datasets:
Languages:
English
Multilinguality:
monolingual
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
| # 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. | |
| # Lint as: python3 | |
| """The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark.""" | |
| import textwrap | |
| import pandas as pd | |
| import datasets | |
| _SILICONE_CITATION = """\ | |
| @inproceedings{chapuis-etal-2020-hierarchical, | |
| title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog", | |
| author = "Chapuis, Emile and | |
| Colombo, Pierre and | |
| Manica, Matteo and | |
| Labeau, Matthieu and | |
| Clavel, Chlo{\'e}", | |
| booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", | |
| month = nov, | |
| year = "2020", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/2020.findings-emnlp.239", | |
| doi = "10.18653/v1/2020.findings-emnlp.239", | |
| pages = "2636--2648", | |
| abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a | |
| key component of spoken dialog systems. In this work, we propose a new approach to learn | |
| generic representations adapted to spoken dialog, which we evaluate on a new benchmark we | |
| call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). | |
| SILICONE is model-agnostic and contains 10 different datasets of various sizes. | |
| We obtain our representations with a hierarchical encoder based on transformer architectures, | |
| for which we extend two well-known pre-training objectives. Pre-training is performed on | |
| OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We | |
| demonstrate how hierarchical encoders achieve competitive results with consistently fewer | |
| parameters compared to state-of-the-art models and we show their importance for both | |
| pre-training and fine-tuning.", | |
| } | |
| """ | |
| _SILICONE_DESCRIPTION = """\ | |
| The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection | |
| of resources for training, evaluating, and analyzing natural language understanding systems | |
| specifically designed for spoken language. All datasets are in the English language and cover a | |
| variety of domains including daily life, scripted scenarios, joint task completion, phone call | |
| conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant | |
| labels. | |
| """ | |
| _URL = "https://raw.githubusercontent.com/eusip/SILICONE-benchmark/main" | |
| SWDA_DA_DESCRIPTION = { | |
| "sd": "Statement-non-opinion", | |
| "b": "Acknowledge (Backchannel)", | |
| "sv": "Statement-opinion", | |
| "%": "Uninterpretable", | |
| "aa": "Agree/Accept", | |
| "ba": "Appreciation", | |
| "fc": "Conventional-closing", | |
| "qw": "Wh-Question", | |
| "nn": "No Answers", | |
| "bk": "Response Acknowledgement", | |
| "h": "Hedge", | |
| "qy^d": "Declarative Yes-No-Question", | |
| "bh": "Backchannel in Question Form", | |
| "^q": "Quotation", | |
| "bf": "Summarize/Reformulate", | |
| 'fo_o_fw_"_by_bc': "Other", | |
| 'fo_o_fw_by_bc_"': "Other", | |
| "na": "Affirmative Non-yes Answers", | |
| "ad": "Action-directive", | |
| "^2": "Collaborative Completion", | |
| "b^m": "Repeat-phrase", | |
| "qo": "Open-Question", | |
| "qh": "Rhetorical-Question", | |
| "^h": "Hold Before Answer/Agreement", | |
| "ar": "Reject", | |
| "ng": "Negative Non-no Answers", | |
| "br": "Signal-non-understanding", | |
| "no": "Other Answers", | |
| "fp": "Conventional-opening", | |
| "qrr": "Or-Clause", | |
| "arp_nd": "Dispreferred Answers", | |
| "t3": "3rd-party-talk", | |
| "oo_co_cc": "Offers, Options Commits", | |
| "aap_am": "Maybe/Accept-part", | |
| "t1": "Downplayer", | |
| "bd": "Self-talk", | |
| "^g": "Tag-Question", | |
| "qw^d": "Declarative Wh-Question", | |
| "fa": "Apology", | |
| "ft": "Thanking", | |
| "+": "Unknown", | |
| "x": "Unknown", | |
| "ny": "Unknown", | |
| "sv_fx": "Unknown", | |
| "qy_qr": "Unknown", | |
| "ba_fe": "Unknown", | |
| } | |
| MRDA_DA_DESCRIPTION = { | |
| "s": "Statement/Subjective Statement", | |
| "d": "Declarative Question", | |
| "b": "Backchannel", | |
| "f": '"Follow-me"', | |
| "q": "Question", | |
| } | |
| IEMOCAP_E_DESCRIPTION = { | |
| "ang": "Anger", | |
| "dis": "Disgust", | |
| "exc": "Excitement", | |
| "fea": "Fear", | |
| "fru": "Frustration", | |
| "hap": "Happiness", | |
| "neu": "Neutral", | |
| "oth": "Other", | |
| "sad": "Sadness", | |
| "sur": "Surprise", | |
| "xxx": "Unknown", | |
| } | |
| class SiliconeConfig(datasets.BuilderConfig): | |
| """BuilderConfig for SILICONE.""" | |
| def __init__( | |
| self, | |
| text_features, | |
| label_column, | |
| data_url, | |
| citation, | |
| url, | |
| label_classes=None, | |
| **kwargs, | |
| ): | |
| """BuilderConfig for SILICONE. | |
| 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 csv/txt file corresponding | |
| to the label | |
| data_url: `string`, url to download the csv/text file from | |
| 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')`. | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(SiliconeConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) | |
| self.text_features = text_features | |
| self.label_column = label_column | |
| self.label_classes = label_classes | |
| self.data_url = data_url | |
| self.citation = citation | |
| self.url = url | |
| class Silicone(datasets.GeneratorBasedBuilder): | |
| """The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark.""" | |
| BUILDER_CONFIGS = [ | |
| SiliconeConfig( | |
| name="dyda_da", | |
| description=textwrap.dedent( | |
| """\ | |
| The DailyDialog Act Corpus contains multi-turn dialogues and is supposed to reflect daily | |
| communication by covering topics about daily life. The dataset is manually labelled with | |
| dialog act and emotions. It is the third biggest corpus of SILICONE with 102k utterances.""" | |
| ), | |
| text_features={ | |
| "Utterance": "Utterance", | |
| "Dialogue_Act": "Dialogue_Act", | |
| "Dialogue_ID": "Dialogue_ID", | |
| }, | |
| label_classes=["commissive", "directive", "inform", "question"], | |
| label_column="Dialogue_Act", | |
| data_url={ | |
| "train": _URL + "/dyda/train.csv", | |
| "dev": _URL + "/dyda/dev.csv", | |
| "test": _URL + "/dyda/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @InProceedings{li2017dailydialog, | |
| author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi}, | |
| title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset}, | |
| booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)}, | |
| year = {2017} | |
| }""" | |
| ), | |
| url="http://yanran.li/dailydialog.html", | |
| ), | |
| SiliconeConfig( | |
| name="dyda_e", | |
| description=textwrap.dedent( | |
| """\ | |
| The DailyDialog Act Corpus contains multi-turn dialogues and is supposed to reflect daily | |
| communication by covering topics about daily life. The dataset is manually labelled with | |
| dialog act and emotions. It is the third biggest corpus of SILICONE with 102k utterances.""" | |
| ), | |
| text_features={ | |
| "Utterance": "Utterance", | |
| "Emotion": "Emotion", | |
| "Dialogue_ID": "Dialogue_ID", | |
| }, | |
| label_classes=["anger", "disgust", "fear", "happiness", "no emotion", "sadness", "surprise"], | |
| label_column="Emotion", | |
| data_url={ | |
| "train": _URL + "/dyda/train.csv", | |
| "dev": _URL + "/dyda/dev.csv", | |
| "test": _URL + "/dyda/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @InProceedings{li2017dailydialog, | |
| author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi}, | |
| title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset}, | |
| booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)}, | |
| year = {2017} | |
| }""" | |
| ), | |
| url="http://yanran.li/dailydialog.html", | |
| ), | |
| SiliconeConfig( | |
| name="iemocap", | |
| description=textwrap.dedent( | |
| """\ | |
| The IEMOCAP database is a multi-modal database of ten speakers. It consists of dyadic | |
| sessions where actors perform improvisations or scripted scenarios. Emotion categories | |
| are: anger, happiness, sadness, neutral, excitement, frustration, fear, surprise, and other. | |
| There is no official split of this dataset.""" | |
| ), | |
| text_features={ | |
| "Dialogue_ID": "Dialogue_ID", | |
| "Utterance_ID": "Utterance_ID", | |
| "Utterance": "Utterance", | |
| "Emotion": "Emotion", | |
| }, | |
| label_classes=list(IEMOCAP_E_DESCRIPTION.keys()), | |
| label_column="Emotion", | |
| data_url={ | |
| "train": _URL + "/iemocap/train.csv", | |
| "dev": _URL + "/iemocap/dev.csv", | |
| "test": _URL + "/iemocap/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @article{busso2008iemocap, | |
| title={IEMOCAP: Interactive emotional dyadic motion capture database}, | |
| author={Busso, Carlos and Bulut, Murtaza and Lee, Chi-Chun and Kazemzadeh, Abe and Mower, | |
| Emily and Kim, Samuel and Chang, Jeannette N and Lee, Sungbok and Narayanan, Shrikanth S}, | |
| journal={Language resources and evaluation}, | |
| volume={42}, | |
| number={4}, | |
| pages={335}, | |
| year={2008}, | |
| publisher={Springer} | |
| }""" | |
| ), | |
| url="https://sail.usc.edu/iemocap/", | |
| ), | |
| SiliconeConfig( | |
| name="maptask", | |
| description=textwrap.dedent( | |
| """\ | |
| The HCRC MapTask Corpus was constructed through the verbal collaboration of participants | |
| in order to construct a map route. This corpus is small (27k utterances). As there is | |
| no standard train/dev/test split performance depends on the split.""" | |
| ), | |
| text_features={ | |
| "Speaker": "Speaker", | |
| "Utterance": "Utterance", | |
| "Dialogue_Act": "Dialogue_Act", | |
| }, | |
| label_classes=[ | |
| "acknowledge", | |
| "align", | |
| "check", | |
| "clarify", | |
| "explain", | |
| "instruct", | |
| "query_w", | |
| "query_yn", | |
| "ready", | |
| "reply_n", | |
| "reply_w", | |
| "reply_y", | |
| ], | |
| label_column="Dialogue_Act", | |
| data_url={ | |
| "train": _URL + "/maptask/train.txt", | |
| "dev": _URL + "/maptask/dev.txt", | |
| "test": _URL + "/maptask/test.txt", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @inproceedings{thompson1993hcrc, | |
| title={The HCRC map task corpus: natural dialogue for speech recognition}, | |
| author={Thompson, Henry S and Anderson, Anne H and Bard, Ellen Gurman and Doherty-Sneddon, | |
| Gwyneth and Newlands, Alison and Sotillo, Cathy}, | |
| booktitle={HUMAN LANGUAGE TECHNOLOGY: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993}, | |
| year={1993} | |
| }""" | |
| ), | |
| url="http://groups.inf.ed.ac.uk/maptask/", | |
| ), | |
| SiliconeConfig( | |
| name="meld_e", | |
| description=textwrap.dedent( | |
| """\ | |
| The Multimodal EmotionLines Dataset enhances and extends the EmotionLines dataset where | |
| multiple speakers participate in the dialogue.""" | |
| ), | |
| text_features={ | |
| "Utterance": "Utterance", | |
| "Speaker": "Speaker", | |
| "Emotion": "Emotion", | |
| "Dialogue_ID": "Dialogue_ID", | |
| "Utterance_ID": "Utterance_ID", | |
| }, | |
| label_classes=["anger", "disgust", "fear", "joy", "neutral", "sadness", "surprise"], | |
| label_column="Emotion", | |
| data_url={ | |
| "train": _URL + "/meld/train.csv", | |
| "dev": _URL + "/meld/dev.csv", | |
| "test": _URL + "/meld/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @article{chen2018emotionlines, | |
| title={Emotionlines: An emotion corpus of multi-party conversations}, | |
| author={Chen, Sheng-Yeh and Hsu, Chao-Chun and Kuo, Chuan-Chun and Ku, Lun-Wei and others}, | |
| journal={arXiv preprint arXiv:1802.08379}, | |
| year={2018} | |
| }""" | |
| ), | |
| url="https://affective-meld.github.io/", | |
| ), | |
| SiliconeConfig( | |
| name="meld_s", | |
| description=textwrap.dedent( | |
| """\ | |
| The Multimodal EmotionLines Dataset enhances and extends the EmotionLines dataset where | |
| multiple speakers participate in the dialogue.""" | |
| ), | |
| text_features={ | |
| "Utterance": "Utterance", | |
| "Speaker": "Speaker", | |
| "Sentiment": "Sentiment", | |
| "Dialogue_ID": "Dialogue_ID", | |
| "Utterance_ID": "Utterance_ID", | |
| }, | |
| label_classes=["negative", "neutral", "positive"], | |
| label_column="Sentiment", | |
| data_url={ | |
| "train": _URL + "/meld/train.csv", | |
| "dev": _URL + "/meld/dev.csv", | |
| "test": _URL + "/meld/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @article{chen2018emotionlines, | |
| title={Emotionlines: An emotion corpus of multi-party conversations}, | |
| author={Chen, Sheng-Yeh and Hsu, Chao-Chun and Kuo, Chuan-Chun and Ku, Lun-Wei and others}, | |
| journal={arXiv preprint arXiv:1802.08379}, | |
| year={2018} | |
| }""" | |
| ), | |
| url="https://affective-meld.github.io/", | |
| ), | |
| SiliconeConfig( | |
| name="mrda", | |
| description=textwrap.dedent( | |
| """\ | |
| ICSI MRDA Corpus consist of transcripts of multi-party meetings hand-annotated with dialog | |
| acts. It is the second biggest dataset with around 110k utterances.""" | |
| ), | |
| text_features={ | |
| "Utterance_ID": "Utterance_ID", | |
| "Dialogue_Act": "Dialogue_Act", | |
| "Channel_ID": "Channel_ID", | |
| "Speaker": "Speaker", | |
| "Dialogue_ID": "Dialogue_ID", | |
| "Utterance": "Utterance", | |
| }, | |
| label_classes=list(MRDA_DA_DESCRIPTION.keys()), | |
| label_column="Dialogue_Act", | |
| data_url={ | |
| "train": _URL + "/mrda/train.csv", | |
| "dev": _URL + "/mrda/dev.csv", | |
| "test": _URL + "/mrda/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @techreport{shriberg2004icsi, | |
| title={The ICSI meeting recorder dialog act (MRDA) corpus}, | |
| author={Shriberg, Elizabeth and Dhillon, Raj and Bhagat, Sonali and Ang, Jeremy and Carvey, Hannah}, | |
| year={2004}, | |
| institution={INTERNATIONAL COMPUTER SCIENCE INST BERKELEY CA} | |
| }""" | |
| ), | |
| url="https://www.aclweb.org/anthology/W04-2319", | |
| ), | |
| SiliconeConfig( | |
| name="oasis", | |
| description=textwrap.dedent( | |
| """\ | |
| The Bt Oasis Corpus (Oasis) contains the transcripts of live calls made to the BT and | |
| operator services. This corpus is rather small (15k utterances). There is no standard | |
| train/dev/test split.""" | |
| ), | |
| text_features={ | |
| "Speaker": "Speaker", | |
| "Utterance": "Utterance", | |
| "Dialogue_Act": "Dialogue_Act", | |
| }, | |
| label_classes=[ | |
| "accept", | |
| "ackn", | |
| "answ", | |
| "answElab", | |
| "appreciate", | |
| "backch", | |
| "bye", | |
| "complete", | |
| "confirm", | |
| "correct", | |
| "direct", | |
| "directElab", | |
| "echo", | |
| "exclaim", | |
| "expressOpinion", | |
| "expressPossibility", | |
| "expressRegret", | |
| "expressWish", | |
| "greet", | |
| "hold", | |
| "identifySelf", | |
| "inform", | |
| "informCont", | |
| "informDisc", | |
| "informIntent", | |
| "init", | |
| "negate", | |
| "offer", | |
| "pardon", | |
| "raiseIssue", | |
| "refer", | |
| "refuse", | |
| "reqDirect", | |
| "reqInfo", | |
| "reqModal", | |
| "selfTalk", | |
| "suggest", | |
| "thank", | |
| "informIntent-hold", | |
| "correctSelf", | |
| "expressRegret-inform", | |
| "thank-identifySelf", | |
| ], | |
| label_column="Dialogue_Act", | |
| data_url={ | |
| "train": _URL + "/oasis/train.txt", | |
| "dev": _URL + "/oasis/dev.txt", | |
| "test": _URL + "/oasis/test.txt", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @inproceedings{leech2003generic, | |
| title={Generic speech act annotation for task-oriented dialogues}, | |
| author={Leech, Geoffrey and Weisser, Martin}, | |
| booktitle={Proceedings of the corpus linguistics 2003 conference}, | |
| volume={16}, | |
| pages={441--446}, | |
| year={2003}, | |
| organization={Lancaster: Lancaster University} | |
| }""" | |
| ), | |
| url="http://groups.inf.ed.ac.uk/oasis/", | |
| ), | |
| SiliconeConfig( | |
| name="sem", | |
| description=textwrap.dedent( | |
| """\ | |
| The SEMAINE database comes from the Sustained Emotionally coloured Human-Machine Interaction | |
| using Nonverbal Expression project. This dataset has been annotated on three sentiments | |
| labels: positive, negative and neutral. It is built on Multimodal Wizard of Oz experiment | |
| where participants held conversations with an operator who adopted various roles designed | |
| to evoke emotional reactions. There is no official split on this dataset.""" | |
| ), | |
| text_features={ | |
| "Utterance": "Utterance", | |
| "NbPairInSession": "NbPairInSession", | |
| "Dialogue_ID": "Dialogue_ID", | |
| "SpeechTurn": "SpeechTurn", | |
| "Speaker": "Speaker", | |
| "Sentiment": "Sentiment", | |
| }, | |
| label_classes=["Negative", "Neutral", "Positive"], | |
| label_column="Sentiment", | |
| data_url={ | |
| "train": _URL + "/sem/train.csv", | |
| "dev": _URL + "/sem/dev.csv", | |
| "test": _URL + "/sem/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @article{mckeown2011semaine, | |
| title={The semaine database: Annotated multimodal records of emotionally colored conversations | |
| between a person and a limited agent}, | |
| author={McKeown, Gary and Valstar, Michel and Cowie, Roddy and Pantic, Maja and Schroder, Marc}, | |
| journal={IEEE transactions on affective computing}, | |
| volume={3}, | |
| number={1}, | |
| pages={5--17}, | |
| year={2011}, | |
| publisher={IEEE} | |
| }""" | |
| ), | |
| url="https://ieeexplore.ieee.org/document/5959155", | |
| ), | |
| SiliconeConfig( | |
| name="swda", | |
| description=textwrap.dedent( | |
| """\ | |
| Switchboard Dialog Act Corpus (SwDA) is a telephone speech corpus consisting of two-sided | |
| telephone conversations with provided topics. This dataset includes additional features | |
| such as speaker id and topic information.""" | |
| ), | |
| text_features={ | |
| "Utterance": "Utterance", | |
| "Dialogue_Act": "Dialogue_Act", | |
| "From_Caller": "From_Caller", | |
| "To_Caller": "To_Caller", | |
| "Topic": "Topic", | |
| "Dialogue_ID": "Dialogue_ID", | |
| "Conv_ID": "Conv_ID", | |
| }, | |
| label_classes=list(SWDA_DA_DESCRIPTION.keys()), | |
| label_column="Dialogue_Act", | |
| data_url={ | |
| "train": _URL + "/swda/train.csv", | |
| "dev": _URL + "/swda/dev.csv", | |
| "test": _URL + "/swda/test.csv", | |
| }, | |
| citation=textwrap.dedent( | |
| """\ | |
| @article{stolcke2000dialogue, | |
| title={Dialogue act modeling for automatic tagging and recognition of conversational speech}, | |
| author={Stolcke, Andreas and Ries, Klaus and Coccaro, Noah and Shriberg, Elizabeth and | |
| Bates, Rebecca and Jurafsky, Daniel and Taylor, Paul and Martin, Rachel and Ess-Dykema, | |
| Carol Van and Meteer, Marie}, | |
| journal={Computational linguistics}, | |
| volume={26}, | |
| number={3}, | |
| pages={339--373}, | |
| year={2000}, | |
| publisher={MIT Press} | |
| }""" | |
| ), | |
| url="https://web.stanford.edu/~jurafsky/ws97/", | |
| ), | |
| ] | |
| def _info(self): | |
| features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()} | |
| if self.config.label_classes: | |
| features["Label"] = datasets.features.ClassLabel(names=self.config.label_classes) | |
| features["Idx"] = datasets.Value("int32") | |
| return datasets.DatasetInfo( | |
| description=_SILICONE_DESCRIPTION, | |
| features=datasets.Features(features), | |
| homepage=self.config.url, | |
| citation=self.config.citation + "\n" + _SILICONE_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_files = dl_manager.download(self.config.data_url) | |
| splits = [] | |
| splits.append( | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "data_file": data_files["train"], | |
| "split": "train", | |
| }, | |
| ) | |
| ) | |
| splits.append( | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "data_file": data_files["dev"], | |
| "split": "dev", | |
| }, | |
| ) | |
| ) | |
| splits.append( | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "data_file": data_files["test"], | |
| "split": "test", | |
| }, | |
| ) | |
| ) | |
| return splits | |
| def _generate_examples(self, data_file, split): | |
| if self.config.name not in ("maptask", "iemocap", "oasis"): | |
| df = pd.read_csv(data_file, delimiter=",", header=0, quotechar='"', dtype=str)[ | |
| self.config.text_features.keys() | |
| ] | |
| if self.config.name == "iemocap": | |
| df = pd.read_csv( | |
| data_file, | |
| delimiter=",", | |
| header=0, | |
| quotechar='"', | |
| names=["Dialogue_ID", "Utterance_ID", "Utterance", "Emotion", "Valence", "Activation", "Dominance"], | |
| dtype=str, | |
| )[self.config.text_features.keys()] | |
| if self.config.name in ("maptask", "oasis"): | |
| df = pd.read_csv(data_file, delimiter="|", names=["Speaker", "Utterance", "Dialogue_Act"], dtype=str)[ | |
| self.config.text_features.keys() | |
| ] | |
| rows = df.to_dict(orient="records") | |
| for n, row in enumerate(rows): | |
| example = row | |
| example["Idx"] = n | |
| if self.config.label_column in example: | |
| label = example[self.config.label_column] | |
| example["Label"] = label | |
| yield example["Idx"], example | |