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|
| | import random |
| | import sys, os, pdb |
| | import json, math |
| | import datasets |
| | from datasets import load_dataset |
| | import csv |
| |
|
| | random.seed(42) |
| |
|
| | DATASETS = { |
| | "natural_language_understanding": [ |
| | "ATIS", "ATIS-NER", "BANKING77", "BANKING77-OOS", "CLINC-Single-Domain-OOS-banking", |
| | "CLINC-Single-Domain-OOS-credit_cards", "CLINC150", "DSTC8-SGD", "HWU64", "MIT-Movie", |
| | "MIT-Restaurant", "RESTAURANTS8K", "SNIPS", "SNIPS-NER", "TOP", "TOP-NER" |
| | ], |
| | "task_oriented": [ |
| | "ABCD", "AirDialogue", "BiTOD", "CaSiNo", "CraigslistBargains", |
| | "Disambiguation", "DSTC2-Clean", "FRAMES", "GECOR", "HDSA-Dialog", |
| | "KETOD", "KVRET", "MetaLWOZ", "MS-DC", "MuDoCo", |
| | "MulDoGO", "MultiWOZ_2.1", "MULTIWOZ2_2", "SGD", "SimJointGEN", |
| | "SimJointMovie", "SimJointRestaurant", "STAR", "Taskmaster1", "Taskmaster2", |
| | "Taskmaster3", "WOZ2_0" |
| | ], |
| | "dialogue_summarization": [ |
| | "AMI", "CRD3", "DialogSum", "ECTSum", "ICSI", |
| | "MediaSum", "QMSum", "SAMSum", "TweetSumm", "ConvoSumm", |
| | "SummScreen_ForeverDreaming", "SummScreen_TVMegaSite" |
| | ], |
| | "conversational_recommendation": [ |
| | "Redial", "DuRecDial-2.0", "OpenDialKG", "SalesBot", |
| | ], |
| | "open_domain": [ |
| | "chitchat-dataset", "ConvAI2", "AntiScam", "Empathetic", "HH-RLHF", |
| | "PLACES3.5", "Prosocial", "SODA" |
| | ], |
| | "knowledge_grounded": [ |
| | "CompWebQ", "CoQA", "CoSQL", "DART", "FeTaQA", |
| | "GrailQA", "HybridQA", "MTOP", "MultiModalQA", "SParC", |
| | "Spider", "SQA", "ToTTo", "WebQSP", "WikiSQL", |
| | "WikiTQ", "wizard_of_internet", "wizard_of_wikipedia" |
| | ], |
| | } |
| |
|
| | class Test(object): |
| | def __init__(self): |
| | pass |
| |
|
| | def test_single_dataset(self, data_name): |
| | |
| | dataset = load_dataset("Salesforce/dialogstudio", data_name, revision="download") |
| | dataset_size = { |
| | "train":0, |
| | "validation": 0, |
| | "test": 0, |
| | } |
| | for split in dataset: |
| | dataset_size[split] = len(dataset[split]) |
| | print(dataset_size) |
| | |
| | return dataset_size |
| |
|
| | def test_all(self): |
| | with open("dataset_stats.csv", "w", newline="") as tf: |
| | writer = csv.writer(tf) |
| | writer.writerow(["Category", "Data_name", "train", "val", "test"]) |
| | for cat, dataset_list in DATASETS.items(): |
| | for data_name in dataset_list: |
| | dataset_size = self.test_single_dataset(data_name=data_name) |
| | writer.writerow([cat, data_name] + list(dataset_size.values())) |
| |
|
| |
|
| | def main(): |
| | test = Test() |
| | |
| | |
| | |
| | |
| | test.test_single_dataset("Taskmaster2") |
| |
|
| | if __name__ == "__main__": |
| | main() |
| |
|
