Datasets:
Commit
•
b8a6494
1
Parent(s):
d7ae73a
feat: FaQUAD-NLI
Browse files- faquad-nli.py +124 -0
- sentence_spans.py +51 -0
- spans.csv +1175 -0
faquad-nli.py
ADDED
@@ -0,0 +1,124 @@
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1 |
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"""FaQUAD-NLI dataset"""
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import datasets
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import pandas as pd
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import json
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_CITATION = """
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"""
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_DESCRIPTION = """
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"""
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_URLS = {
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"data": "https://raw.githubusercontent.com/liafacom/faquad/6ad978f20672bb41625b3b71fbe4a88b893d0a86/data/dataset.json"
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}
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def check_overlap(interval1, interval2):
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"""Check for overlap between two integer intervals"""
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return not (interval1[1] < interval2[0] or interval2[1] < interval1[0])
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class Boun(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"document_index": datasets.Value("int32"),
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"document_title": datasets.Value("string"),
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"paragraph_index": datasets.Value("int32"),
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"question": datasets.Value("string"),
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"answer": datasets.Value("string"),
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"label": datasets.Value("int32")
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}),
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supervised_keys=None,
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homepage="https://github.com/franciellevargas/HateBR",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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downloaded_files = dl_manager.download(_URLS)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"data": downloaded_files["data"],
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"split": "train"
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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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gen_kwargs={
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"data": downloaded_files["data"],
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"split": "validation"
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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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gen_kwargs={
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"data": downloaded_files["data"],
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"split": "test"
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}
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)
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]
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def _generate_examples(self, data, split):
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with open(data, 'r') as f:
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json_data = json.load(f)
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spans = pd.read_csv("spans.csv").to_dict("records")
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counter = 0
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for span_row in spans:
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if span_row["split"] != split:
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continue
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document_title = json_data["data"][
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span_row["document_index"]
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]["title"]
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sentence = json_data["data"][
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span_row["document_index"]
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]["paragraphs"][
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span_row["paragraph_index"]
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]["context"][
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span_row["sentence_start_char"]:span_row["sentence_end_char"]
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]
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sentence_interval = (span_row["sentence_start_char"], span_row["sentence_end_char"])
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for qas_row in json_data["data"][
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span_row["document_index"]
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]["paragraphs"][
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span_row["paragraph_index"]
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]["qas"]:
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question = qas_row["question"]
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question_spans = []
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for qas_answer in qas_row["answers"]:
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qas_answer_start_span = qas_answer["answer_start"]
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qas_answer_end_span = qas_answer["answer_start"] + len(qas_answer["text"])
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question_spans.append((qas_answer_start_span, qas_answer_end_span))
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for question_interval in question_spans:
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if check_overlap(sentence_interval, question_interval):
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yield counter, {
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"document_index": span_row["document_index"],
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"document_title": document_title,
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"paragraph_index": span_row["paragraph_index"],
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"question": question,
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"answer": sentence,
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"label": 1
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}
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counter += 1
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break
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else:
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yield counter, {
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"document_index": span_row["document_index"],
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"document_title": document_title,
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"paragraph_index": span_row["paragraph_index"],
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"question": question,
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"answer": sentence,
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"label": 0
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}
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counter += 1
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sentence_spans.py
ADDED
@@ -0,0 +1,51 @@
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import spacy
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import json
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import requests
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import pandas as pd
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DATASET_URL = "https://raw.githubusercontent.com/liafacom/faquad/6ad978f20672bb41625b3b71fbe4a88b893d0a86/data/dataset.json"
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class SentenceModel(object):
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def __init__(self, model_name):
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self.nlp = spacy.load(model_name)
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def parse(self, text):
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with self.nlp.select_pipes(enable=['tok2vec', "parser", "senter"]):
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doc = self.nlp(text)
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sentences = [ (sentence.start_char, sentence.end_char) for sentence in doc.sents ]
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return sentences
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def context_generator(url):
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response = requests.get(url)
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data = json.loads(response.text)["data"]
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for idx, row in enumerate(data):
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for paragraph_idx, paragraph_row in enumerate(row["paragraphs"]):
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context = paragraph_row["context"]
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yield idx, paragraph_idx, context
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def define_split(document_index):
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if document_index % 5 == 0:
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return "test"
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elif document_index % 5 == 1:
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return "validation"
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else:
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return "train"
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def main():
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df = []
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model = SentenceModel("pt_core_news_sm")
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for idx, paragraph_idx, context in context_generator(DATASET_URL):
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for start_char, end_char in model.parse(context):
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row = {
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"document_index": idx,
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"paragraph_index": paragraph_idx,
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"sentence_start_char": start_char,
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"sentence_end_char": end_char,
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"split": define_split(idx)
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}
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df.append(row)
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df = pd.DataFrame(df)
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df.to_csv("spans.csv", index=False)
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if __name__ == "__main__":
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main()
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spans.csv
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1 |
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document_index,paragraph_index,sentence_start_char,sentence_end_char,split
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2 |
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3 |
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14 |
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17 |
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18 |
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20 |
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21 |
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22 |
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23 |
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24 |
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25 |
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26 |
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27 |
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|
28 |
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|
29 |
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|
30 |
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31 |
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32 |
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33 |
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34 |
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35 |
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36 |
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37 |
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38 |
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39 |
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40 |
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41 |
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42 |
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43 |
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44 |
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45 |
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46 |
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47 |
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48 |
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49 |
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50 |
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51 |
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52 |
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53 |
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54 |
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55 |
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56 |
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57 |
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58 |
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59 |
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60 |
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62 |
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63 |
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65 |
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66 |
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67 |
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68 |
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69 |
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70 |
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72 |
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73 |
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74 |
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75 |
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76 |
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77 |
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78 |
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81 |
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84 |
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85 |
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86 |
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87 |
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88 |
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89 |
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90 |
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91 |
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92 |
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|
93 |
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|
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