victormiller
commited on
Commit
•
5e5aef1
1
Parent(s):
e8dab56
Update curated.py
Browse files- curated.py +363 -13
curated.py
CHANGED
@@ -74,7 +74,7 @@ wikipedia_filter = pd.DataFrame(
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"Percent Removed After Unigram Probability Filter": [
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"0.00%",
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],
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-
"
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"",
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],
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"Total Percentage Remaining": [
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@@ -86,6 +86,356 @@ wikipedia_filter = pd.DataFrame(
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table_html_wikipedia = wikipedia_filter.to_html(index=False, border=0)
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table_div_wikipedia = Div(NotStr(table_html_wikipedia), style="margin: 40px;")
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filtering_process = Div(
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Section(
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@@ -139,7 +489,7 @@ filtering_process = Div(
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Ol(
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Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."),
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),
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-
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),
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Section(
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H3("S2ORC"),
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@@ -174,7 +524,7 @@ filtering_process = Div(
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Ol(
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Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup"),
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),
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-
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),
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Section(
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H3("PubMed"),
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@@ -203,7 +553,7 @@ filtering_process = Div(
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Ol(
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Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."),
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),
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-
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),
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Section(
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H3("Phil Papers"),
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@@ -226,7 +576,7 @@ filtering_process = Div(
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Ol(
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Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."),
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),
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-
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),
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Section(
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H3("Europarl"),
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@@ -248,7 +598,7 @@ filtering_process = Div(
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Ol(
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Li("After local dedup, remaining europarl was deduped again with all the datasets combined"),
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),
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-
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),
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Section(
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H3("HackerNews"),
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@@ -273,7 +623,7 @@ filtering_process = Div(
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Ol(
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Li("After local dedup, remaining data was deduped again with all the datasets combined"),
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),
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-
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),
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Section(
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H3("USPTO"),
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@@ -297,7 +647,7 @@ filtering_process = Div(
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Ol(
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Li("After local dedup, remaining data was deduped again with all the datasets combined"),
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),
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-
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),
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Section(
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H3("FreeLaw"),
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@@ -325,7 +675,7 @@ filtering_process = Div(
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Ol(
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Li("After local dedup, remaining data was deduped again with all the datasets combined"),
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),
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-
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),
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Section(
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H3("StackExchange"),
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@@ -358,7 +708,7 @@ filtering_process = Div(
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Ol(
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Li("After local dedup, remaining data was deduped again with all the datasets combined"),
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),
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-
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),
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Section(
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H3("Ubuntu IRC"),
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@@ -382,7 +732,7 @@ filtering_process = Div(
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Ol(
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Li("After local dedup, remaining data was deduped again with all the datasets combined"),
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),
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-
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),
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Section(
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H3("DM Maths"),
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@@ -403,7 +753,7 @@ filtering_process = Div(
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Ol(
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Li("None"),
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),
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-
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),
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Section(
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H3("PG19"),
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@@ -425,7 +775,7 @@ filtering_process = Div(
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Ol(
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Li("After local dedup, remaining data was deduped again with all the datasets combined"),
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),
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-
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),
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)
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"Percent Removed After Unigram Probability Filter": [
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"0.00%",
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],
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+
"Percent Removed After Local Dedup": [
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"",
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],
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"Total Percentage Remaining": [
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table_html_wikipedia = wikipedia_filter.to_html(index=False, border=0)
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table_div_wikipedia = Div(NotStr(table_html_wikipedia), style="margin: 40px;")
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+
freelaw_filter = pd.DataFrame(
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{
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"Dataset": [
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"Wikipedia",
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],
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+
"Lines Downloaded": [
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"61614907",
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+
],
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"Percent Removed After Language Filter": [
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"0.00%",
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],
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+
"Percent Removed After Min Word Count Filter": [
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"1.86%",
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+
],
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+
"Percent Removed After Unigram Probability Filter": [
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+
"0.00%",
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+
],
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+
"Percent Removed After Local Dedup": [
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+
"",
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+
],
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+
"Total Percentage Remaining": [
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"98.14%",
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],
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}
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+
)
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+
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+
table_html_freelaw = freelaw_filter.to_html(index=False, border=0)
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+
table_div_freelaw = Div(NotStr(table_html_freelaw), style="margin: 40px;")
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+
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dmm_filter = pd.DataFrame(
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{
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"Dataset": [
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"Wikipedia",
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],
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+
"Lines Downloaded": [
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"61614907",
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],
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+
"Percent Removed After Language Filter": [
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"0.00%",
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+
],
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+
"Percent Removed After Min Word Count Filter": [
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+
"1.86%",
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+
],
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+
"Percent Removed After Unigram Probability Filter": [
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+
"0.00%",
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+
],
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135 |
+
"Percent Removed After Local Dedup": [
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+
"",
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137 |
+
],
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138 |
+
"Total Percentage Remaining": [
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139 |
+
"98.14%",
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+
],
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+
}
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+
)
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+
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+
table_html_dmm = dmm_filter.to_html(index=False, border=0)
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+
table_div_dmm = Div(NotStr(table_html_dmm), style="margin: 40px;")
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+
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+
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+
uspto_filter = pd.DataFrame(
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+
{
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+
"Dataset": [
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+
"Wikipedia",
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+
],
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+
"Lines Downloaded": [
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+
"61614907",
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+
],
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+
"Percent Removed After Language Filter": [
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157 |
+
"0.00%",
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+
],
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159 |
+
"Percent Removed After Min Word Count Filter": [
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+
"1.86%",
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+
],
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+
"Percent Removed After Unigram Probability Filter": [
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+
"0.00%",
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+
],
|
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+
"Percent Removed After Local Dedup": [
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+
"",
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167 |
+
],
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168 |
+
"Total Percentage Remaining": [
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169 |
+
"98.14%",
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+
],
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}
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)
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+
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+
table_html_uspto = uspto_filter.to_html(index=False, border=0)
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+
table_div_uspto = Div(NotStr(table_html_uspto), style="margin: 40px;")
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+
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+
pg19_filter = pd.DataFrame(
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+
{
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"Dataset": [
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+
"Wikipedia",
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+
],
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+
"Lines Downloaded": [
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+
"61614907",
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+
],
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+
"Percent Removed After Language Filter": [
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+
"0.00%",
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+
],
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+
"Percent Removed After Min Word Count Filter": [
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+
"1.86%",
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+
],
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+
"Percent Removed After Unigram Probability Filter": [
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+
"0.00%",
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+
],
|
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+
"Percent Removed After Local Dedup": [
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+
"",
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+
],
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+
"Total Percentage Remaining": [
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+
"98.14%",
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],
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}
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)
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table_html_pg19 = pg19_filter.to_html(index=False, border=0)
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table_div_pg19 = Div(NotStr(table_html_pg19), style="margin: 40px;")
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+
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+
hn_filter = pd.DataFrame(
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+
{
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+
"Dataset": [
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"Wikipedia",
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],
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+
"Lines Downloaded": [
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"61614907",
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],
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+
"Percent Removed After Language Filter": [
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+
"0.00%",
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+
],
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+
"Percent Removed After Min Word Count Filter": [
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+
"1.86%",
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+
],
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+
"Percent Removed After Unigram Probability Filter": [
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+
"0.00%",
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+
],
|
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+
"Percent Removed After Local Dedup": [
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+
"",
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226 |
+
],
|
227 |
+
"Total Percentage Remaining": [
|
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+
"98.14%",
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+
],
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+
}
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+
)
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+
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table_html_hn = hn_filter.to_html(index=False, border=0)
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table_div_hn = Div(NotStr(table_html_hn), style="margin: 40px;")
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+
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+
uirc_filter = pd.DataFrame(
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+
{
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239 |
+
"Dataset": [
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+
"Wikipedia",
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+
],
|
242 |
+
"Lines Downloaded": [
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243 |
+
"61614907",
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+
],
|
245 |
+
"Percent Removed After Language Filter": [
|
246 |
+
"0.00%",
|
247 |
+
],
|
248 |
+
"Percent Removed After Min Word Count Filter": [
|
249 |
+
"1.86%",
|
250 |
+
],
|
251 |
+
"Percent Removed After Unigram Probability Filter": [
|
252 |
+
"0.00%",
|
253 |
+
],
|
254 |
+
"Percent Removed After Local Dedup": [
|
255 |
+
"",
|
256 |
+
],
|
257 |
+
"Total Percentage Remaining": [
|
258 |
+
"98.14%",
|
259 |
+
],
|
260 |
+
}
|
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+
)
|
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+
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+
table_html_uirc = uirc_filter.to_html(index=False, border=0)
|
264 |
+
table_div_uirc = Div(NotStr(table_html_uirc), style="margin: 40px;")
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265 |
+
|
266 |
+
up_filter = pd.DataFrame(
|
267 |
+
{
|
268 |
+
"Dataset": [
|
269 |
+
"Wikipedia",
|
270 |
+
],
|
271 |
+
"Lines Downloaded": [
|
272 |
+
"61614907",
|
273 |
+
],
|
274 |
+
"Percent Removed After Language Filter": [
|
275 |
+
"0.00%",
|
276 |
+
],
|
277 |
+
"Percent Removed After Min Word Count Filter": [
|
278 |
+
"1.86%",
|
279 |
+
],
|
280 |
+
"Percent Removed After Unigram Probability Filter": [
|
281 |
+
"0.00%",
|
282 |
+
],
|
283 |
+
"Percent Removed After Local Dedup": [
|
284 |
+
"",
|
285 |
+
],
|
286 |
+
"Total Percentage Remaining": [
|
287 |
+
"98.14%",
|
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+
],
|
289 |
+
}
|
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+
)
|
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+
|
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+
table_html_up = up_filter.to_html(index=False, border=0)
|
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table_div_up = Div(NotStr(table_html_up), style="margin: 40px;")
|
294 |
+
|
295 |
+
se_filter = pd.DataFrame(
|
296 |
+
{
|
297 |
+
"Dataset": [
|
298 |
+
"Wikipedia",
|
299 |
+
],
|
300 |
+
"Lines Downloaded": [
|
301 |
+
"61614907",
|
302 |
+
],
|
303 |
+
"Percent Removed After Language Filter": [
|
304 |
+
"0.00%",
|
305 |
+
],
|
306 |
+
"Percent Removed After Min Word Count Filter": [
|
307 |
+
"1.86%",
|
308 |
+
],
|
309 |
+
"Percent Removed After Unigram Probability Filter": [
|
310 |
+
"0.00%",
|
311 |
+
],
|
312 |
+
"Percent Removed After Local Dedup": [
|
313 |
+
"",
|
314 |
+
],
|
315 |
+
"Total Percentage Remaining": [
|
316 |
+
"98.14%",
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317 |
+
],
|
318 |
+
}
|
319 |
+
)
|
320 |
+
|
321 |
+
table_html_se = se_filter.to_html(index=False, border=0)
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322 |
+
table_div_se = Div(NotStr(table_html_se), style="margin: 40px;")
|
323 |
+
|
324 |
+
arx_filter = pd.DataFrame(
|
325 |
+
{
|
326 |
+
"Dataset": [
|
327 |
+
"Wikipedia",
|
328 |
+
],
|
329 |
+
"Lines Downloaded": [
|
330 |
+
"61614907",
|
331 |
+
],
|
332 |
+
"Percent Removed After Language Filter": [
|
333 |
+
"0.00%",
|
334 |
+
],
|
335 |
+
"Percent Removed After Min Word Count Filter": [
|
336 |
+
"1.86%",
|
337 |
+
],
|
338 |
+
"Percent Removed After Unigram Probability Filter": [
|
339 |
+
"0.00%",
|
340 |
+
],
|
341 |
+
"Percent Removed After Local Dedup": [
|
342 |
+
"",
|
343 |
+
],
|
344 |
+
"Total Percentage Remaining": [
|
345 |
+
"98.14%",
|
346 |
+
],
|
347 |
+
}
|
348 |
+
)
|
349 |
+
|
350 |
+
table_html_arx = arx_filter.to_html(index=False, border=0)
|
351 |
+
table_div_arx = Div(NotStr(table_html_arx), style="margin: 40px;")
|
352 |
+
|
353 |
+
s2o_filter = pd.DataFrame(
|
354 |
+
{
|
355 |
+
"Dataset": [
|
356 |
+
"Wikipedia",
|
357 |
+
],
|
358 |
+
"Lines Downloaded": [
|
359 |
+
"61614907",
|
360 |
+
],
|
361 |
+
"Percent Removed After Language Filter": [
|
362 |
+
"0.00%",
|
363 |
+
],
|
364 |
+
"Percent Removed After Min Word Count Filter": [
|
365 |
+
"1.86%",
|
366 |
+
],
|
367 |
+
"Percent Removed After Unigram Probability Filter": [
|
368 |
+
"0.00%",
|
369 |
+
],
|
370 |
+
"Percent Removed After Local Dedup": [
|
371 |
+
"",
|
372 |
+
],
|
373 |
+
"Total Percentage Remaining": [
|
374 |
+
"98.14%",
|
375 |
+
],
|
376 |
+
}
|
377 |
+
)
|
378 |
+
|
379 |
+
table_html_s2o = s2o_filter.to_html(index=False, border=0)
|
380 |
+
table_div_s2o = Div(NotStr(table_html_s2o), style="margin: 40px;")
|
381 |
+
|
382 |
+
med_filter = pd.DataFrame(
|
383 |
+
{
|
384 |
+
"Dataset": [
|
385 |
+
"Wikipedia",
|
386 |
+
],
|
387 |
+
"Lines Downloaded": [
|
388 |
+
"61614907",
|
389 |
+
],
|
390 |
+
"Percent Removed After Language Filter": [
|
391 |
+
"0.00%",
|
392 |
+
],
|
393 |
+
"Percent Removed After Min Word Count Filter": [
|
394 |
+
"1.86%",
|
395 |
+
],
|
396 |
+
"Percent Removed After Unigram Probability Filter": [
|
397 |
+
"0.00%",
|
398 |
+
],
|
399 |
+
"Percent Removed After Local Dedup": [
|
400 |
+
"",
|
401 |
+
],
|
402 |
+
"Total Percentage Remaining": [
|
403 |
+
"98.14%",
|
404 |
+
],
|
405 |
+
}
|
406 |
+
)
|
407 |
+
|
408 |
+
table_html_med = med_filter.to_html(index=False, border=0)
|
409 |
+
table_div_med = Div(NotStr(table_html_med), style="margin: 40px;")
|
410 |
+
|
411 |
+
phil_filter = pd.DataFrame(
|
412 |
+
{
|
413 |
+
"Dataset": [
|
414 |
+
"Wikipedia",
|
415 |
+
],
|
416 |
+
"Lines Downloaded": [
|
417 |
+
"61614907",
|
418 |
+
],
|
419 |
+
"Percent Removed After Language Filter": [
|
420 |
+
"0.00%",
|
421 |
+
],
|
422 |
+
"Percent Removed After Min Word Count Filter": [
|
423 |
+
"1.86%",
|
424 |
+
],
|
425 |
+
"Percent Removed After Unigram Probability Filter": [
|
426 |
+
"0.00%",
|
427 |
+
],
|
428 |
+
"Percent Removed After Local Dedup": [
|
429 |
+
"",
|
430 |
+
],
|
431 |
+
"Total Percentage Remaining": [
|
432 |
+
"98.14%",
|
433 |
+
],
|
434 |
+
}
|
435 |
+
)
|
436 |
+
|
437 |
+
table_html_phil = phil_filter.to_html(index=False, border=0)
|
438 |
+
table_div_phil = Div(NotStr(table_html_phil), style="margin: 40px;")
|
439 |
|
440 |
filtering_process = Div(
|
441 |
Section(
|
|
|
489 |
Ol(
|
490 |
Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."),
|
491 |
),
|
492 |
+
table_div_arx,
|
493 |
),
|
494 |
Section(
|
495 |
H3("S2ORC"),
|
|
|
524 |
Ol(
|
525 |
Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup"),
|
526 |
),
|
527 |
+
table_div_s2o,
|
528 |
),
|
529 |
Section(
|
530 |
H3("PubMed"),
|
|
|
553 |
Ol(
|
554 |
Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."),
|
555 |
),
|
556 |
+
table_div_med,
|
557 |
),
|
558 |
Section(
|
559 |
H3("Phil Papers"),
|
|
|
576 |
Ol(
|
577 |
Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."),
|
578 |
),
|
579 |
+
table_div_phil,
|
580 |
),
|
581 |
Section(
|
582 |
H3("Europarl"),
|
|
|
598 |
Ol(
|
599 |
Li("After local dedup, remaining europarl was deduped again with all the datasets combined"),
|
600 |
),
|
601 |
+
table_div_up,
|
602 |
),
|
603 |
Section(
|
604 |
H3("HackerNews"),
|
|
|
623 |
Ol(
|
624 |
Li("After local dedup, remaining data was deduped again with all the datasets combined"),
|
625 |
),
|
626 |
+
table_div_hn,
|
627 |
),
|
628 |
Section(
|
629 |
H3("USPTO"),
|
|
|
647 |
Ol(
|
648 |
Li("After local dedup, remaining data was deduped again with all the datasets combined"),
|
649 |
),
|
650 |
+
table_div_uspto,
|
651 |
),
|
652 |
Section(
|
653 |
H3("FreeLaw"),
|
|
|
675 |
Ol(
|
676 |
Li("After local dedup, remaining data was deduped again with all the datasets combined"),
|
677 |
),
|
678 |
+
table_div_freelaw,
|
679 |
),
|
680 |
Section(
|
681 |
H3("StackExchange"),
|
|
|
708 |
Ol(
|
709 |
Li("After local dedup, remaining data was deduped again with all the datasets combined"),
|
710 |
),
|
711 |
+
table_div_se,
|
712 |
),
|
713 |
Section(
|
714 |
H3("Ubuntu IRC"),
|
|
|
732 |
Ol(
|
733 |
Li("After local dedup, remaining data was deduped again with all the datasets combined"),
|
734 |
),
|
735 |
+
table_div_uirc,
|
736 |
),
|
737 |
Section(
|
738 |
H3("DM Maths"),
|
|
|
753 |
Ol(
|
754 |
Li("None"),
|
755 |
),
|
756 |
+
table_div_dmm,
|
757 |
),
|
758 |
Section(
|
759 |
H3("PG19"),
|
|
|
775 |
Ol(
|
776 |
Li("After local dedup, remaining data was deduped again with all the datasets combined"),
|
777 |
),
|
778 |
+
table_div_pg19,
|
779 |
),
|
780 |
)
|
781 |
|