faquad-nli / sentence_spans.py
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feat: FaQUAD-NLI
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import spacy
import json
import requests
import pandas as pd
DATASET_URL = "https://raw.githubusercontent.com/liafacom/faquad/6ad978f20672bb41625b3b71fbe4a88b893d0a86/data/dataset.json"
class SentenceModel(object):
def __init__(self, model_name):
self.nlp = spacy.load(model_name)
def parse(self, text):
with self.nlp.select_pipes(enable=['tok2vec', "parser", "senter"]):
doc = self.nlp(text)
sentences = [ (sentence.start_char, sentence.end_char) for sentence in doc.sents ]
return sentences
def context_generator(url):
response = requests.get(url)
data = json.loads(response.text)["data"]
for idx, row in enumerate(data):
for paragraph_idx, paragraph_row in enumerate(row["paragraphs"]):
context = paragraph_row["context"]
yield idx, paragraph_idx, context
def define_split(document_index):
if document_index % 5 == 0:
return "test"
elif document_index % 5 == 1:
return "validation"
else:
return "train"
def main():
df = []
model = SentenceModel("pt_core_news_sm")
for idx, paragraph_idx, context in context_generator(DATASET_URL):
for start_char, end_char in model.parse(context):
row = {
"document_index": idx,
"paragraph_index": paragraph_idx,
"sentence_start_char": start_char,
"sentence_end_char": end_char,
"split": define_split(idx)
}
df.append(row)
df = pd.DataFrame(df)
df.to_csv("spans.csv", index=False)
if __name__ == "__main__":
main()