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omkarenator
commited on
Commit
•
2783986
1
Parent(s):
3eba508
updates
Browse files- requirements.txt +1 -0
- web.py +40 -17
requirements.txt
CHANGED
@@ -5,3 +5,4 @@ fh-plotly
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pandas
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Jinja2
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rich
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pandas
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Jinja2
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rich
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jsonlines
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web.py
CHANGED
@@ -4,6 +4,28 @@ import json
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import random
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import string
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from rich import print
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def view_data(
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@@ -15,7 +37,10 @@ def view_data(
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if target is None:
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target = "".join(random.choices(string.ascii_lowercase, k=8))
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-
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max_doc_id = len(left) - 1
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slider = Input(
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type="range",
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@@ -208,32 +233,30 @@ def web_data():
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articles, sex education, technical blogs, etc. Specifically, we randomly took 903M URLs and matched them with
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4.6M domain names in the UT1 blocklist. 24 URL domains were detected with more than 4k matches, which are shown below.
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"""),
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src="path/to/24_URL_domains.png",
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alt="24 URL domains with more than 4k matches",
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),
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P("""
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We manually removed the following 6 domains from the UT1 blocklist so that they will not be removed from our dataset.
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"""),
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alt="6 URL domains that are removed from the blocklist",
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),
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),
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H5("1.3.2 Excluded High Quality Sources"),
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P("""
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To avoid duplication with our high-quality curated datasets, we exclude the following domains from our dataset.
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"""),
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),
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),
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H3("2. Line-Level Removal"),
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P("""
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import random
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import string
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from rich import print
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import jsonlines
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from data.url_blocklist import urls_high_matches, urls_false_positives
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from data.non_web_urls import non_web_urls
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def view_data_static(
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left,
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header,
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):
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col1 = Div(
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Pre(
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json.dumps(left, indent=4, ensure_ascii=False),
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style="white-space: pre-wrap; word-break: break-all;",
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),
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style="float: left; overflow-x: auto;",
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)
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data_display = Div(
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col1,
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style="overflow: auto; clear: both; height: 200px; border: 1px solid #ccc; padding: 20px;",
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)
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return Div(H3(header), data_display, style="margin-top: 10px;")
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def view_data(
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if target is None:
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target = "".join(random.choices(string.ascii_lowercase, k=8))
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if left_file.endswith("jsonl"):
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left = [x for x in jsonlines.open(left_file)]
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else:
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left = json.load(open(left_file, encoding="utf-8"))
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max_doc_id = len(left) - 1
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slider = Input(
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type="range",
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articles, sex education, technical blogs, etc. Specifically, we randomly took 903M URLs and matched them with
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4.6M domain names in the UT1 blocklist. 24 URL domains were detected with more than 4k matches, which are shown below.
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"""),
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view_data_static(urls_high_matches, "24 URL domains with more than 4k matches"),
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P("""
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We manually removed the following 6 domains from the UT1 blocklist so that they will not be removed from our dataset.
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"""),
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view_data_static(
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urls_false_positives, "6 url domains that are removed from the blocklist"
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),
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view_data(
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"data/bad_url_doc.jsonl",
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3,
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"Sample documents whose urls are blocked by the refined url blocklist",
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),
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H5("1.3.2 Excluded High Quality Sources"),
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P("""
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To avoid duplication with our high-quality curated datasets, we exclude the following domains from our dataset.
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"""),
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view_data_static(
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non_web_urls,
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"curated url domains that are excluded from our dataset",
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),
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view_data(
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"data/sample_url_exclusion.json",
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0,
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"Sample documents whose urls are in our curated url domain list",
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),
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H3("2. Line-Level Removal"),
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P("""
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