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victormiller commited on
Commit
48b277d
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1 Parent(s): 5e5aef1

Update curated.py

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Files changed (1) hide show
  1. curated.py +52 -52
curated.py CHANGED
@@ -89,19 +89,19 @@ table_div_wikipedia = Div(NotStr(table_html_wikipedia), style="margin: 40px;")
89
  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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  "",
@@ -118,16 +118,16 @@ table_div_freelaw = Div(NotStr(table_html_freelaw), style="margin: 40px;")
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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": [
127
  "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": [
133
  "0.00%",
@@ -148,19 +148,19 @@ table_div_dmm = Div(NotStr(table_html_dmm), style="margin: 40px;")
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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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- "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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  "",
@@ -177,19 +177,19 @@ table_div_uspto = Div(NotStr(table_html_uspto), style="margin: 40px;")
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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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  "",
@@ -207,19 +207,19 @@ table_div_pg19 = Div(NotStr(table_html_pg19), style="margin: 40px;")
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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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  "",
@@ -237,19 +237,19 @@ table_div_hn = Div(NotStr(table_html_hn), style="margin: 40px;")
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  uirc_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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  "",
@@ -266,16 +266,16 @@ table_div_uirc = Div(NotStr(table_html_uirc), style="margin: 40px;")
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  up_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%",
@@ -295,16 +295,16 @@ table_div_up = Div(NotStr(table_html_up), style="margin: 40px;")
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  se_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%",
@@ -324,19 +324,19 @@ table_div_se = Div(NotStr(table_html_se), style="margin: 40px;")
324
  arx_filter = pd.DataFrame(
325
  {
326
  "Dataset": [
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- "Wikipedia",
328
  ],
329
  "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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  "",
@@ -353,16 +353,16 @@ table_div_arx = Div(NotStr(table_html_arx), style="margin: 40px;")
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  s2o_filter = pd.DataFrame(
354
  {
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  "Dataset": [
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- "Wikipedia",
357
  ],
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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%",
@@ -382,19 +382,19 @@ table_div_s2o = Div(NotStr(table_html_s2o), style="margin: 40px;")
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  med_filter = pd.DataFrame(
383
  {
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  "Dataset": [
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- "Wikipedia",
386
  ],
387
  "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": [
400
  "",
@@ -411,19 +411,19 @@ table_div_med = Div(NotStr(table_html_med), style="margin: 40px;")
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  phil_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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  "",
 
89
  freelaw_filter = pd.DataFrame(
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  {
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  "Dataset": [
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+ "FreeLaw",
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  ],
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  "Lines Downloaded": [
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+ "75971288",
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  ],
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  "Percent Removed After Language Filter": [
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+ "3.00%",
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  ],
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  "Percent Removed After Min Word Count Filter": [
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+ "7.49%",
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  ],
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  "Percent Removed After Unigram Probability Filter": [
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+ "0.07%",
105
  ],
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  "Percent Removed After Local Dedup": [
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  "",
 
118
  dmm_filter = pd.DataFrame(
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  {
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  "Dataset": [
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+ "DM Math",
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  ],
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  "Lines Downloaded": [
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+ "112559888",
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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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+ "0.00%",
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  ],
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  "Percent Removed After Unigram Probability Filter": [
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  "0.00%",
 
148
  uspto_filter = pd.DataFrame(
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  {
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  "Dataset": [
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+ "USPTO",
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  ],
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  "Lines Downloaded": [
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+ "6880276",
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  ],
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  "Percent Removed After Language Filter": [
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+ "0.02%",
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  ],
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  "Percent Removed After Min Word Count Filter": [
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+ "1.88%",
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  ],
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  "Percent Removed After Unigram Probability Filter": [
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+ "0.01%",
164
  ],
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  "Percent Removed After Local Dedup": [
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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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+ "PG-19",
181
  ],
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  "Lines Downloaded": [
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+ "28752",
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  ],
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  "Percent Removed After Language Filter": [
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+ "0.24%",
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  ],
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  "Percent Removed After Min Word Count Filter": [
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+ "0.00%",
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  ],
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  "Percent Removed After Unigram Probability Filter": [
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+ "0.17%",
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  ],
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  "Percent Removed After Local Dedup": [
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  "",
 
207
  hn_filter = pd.DataFrame(
208
  {
209
  "Dataset": [
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+ "HackerNews",
211
  ],
212
  "Lines Downloaded": [
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+ "2064931",
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  ],
215
  "Percent Removed After Language Filter": [
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+ "2.62%%",
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  ],
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  "Percent Removed After Min Word Count Filter": [
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+ "0.02%",
220
  ],
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  "Percent Removed After Unigram Probability Filter": [
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+ "0.34%",
223
  ],
224
  "Percent Removed After Local Dedup": [
225
  "",
 
237
  uirc_filter = pd.DataFrame(
238
  {
239
  "Dataset": [
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+ "Ubunutu IRC",
241
  ],
242
  "Lines Downloaded": [
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+ "37966",
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  ],
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  "Percent Removed After Language Filter": [
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+ "38.10%",
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  ],
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  "Percent Removed After Min Word Count Filter": [
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+ "0.14%",
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  ],
251
  "Percent Removed After Unigram Probability Filter": [
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+ "1.12%",
253
  ],
254
  "Percent Removed After Local Dedup": [
255
  "",
 
266
  up_filter = pd.DataFrame(
267
  {
268
  "Dataset": [
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+ "EuroParl",
270
  ],
271
  "Lines Downloaded": [
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+ "69814",
273
  ],
274
  "Percent Removed After Language Filter": [
275
  "0.00%",
276
  ],
277
  "Percent Removed After Min Word Count Filter": [
278
+ "0.00%",
279
  ],
280
  "Percent Removed After Unigram Probability Filter": [
281
  "0.00%",
 
295
  se_filter = pd.DataFrame(
296
  {
297
  "Dataset": [
298
+ "StackExchange",
299
  ],
300
  "Lines Downloaded": [
301
+ "23246548",
302
  ],
303
  "Percent Removed After Language Filter": [
304
  "0.00%",
305
  ],
306
  "Percent Removed After Min Word Count Filter": [
307
+ "0.00%",
308
  ],
309
  "Percent Removed After Unigram Probability Filter": [
310
  "0.00%",
 
324
  arx_filter = pd.DataFrame(
325
  {
326
  "Dataset": [
327
+ "ArXiv",
328
  ],
329
  "Lines Downloaded": [
330
+ "1911867",
331
  ],
332
  "Percent Removed After Language Filter": [
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+ "2.22%",
334
  ],
335
  "Percent Removed After Min Word Count Filter": [
336
+ "5.65%",
337
  ],
338
  "Percent Removed After Unigram Probability Filter": [
339
+ "0.07%",
340
  ],
341
  "Percent Removed After Local Dedup": [
342
  "",
 
353
  s2o_filter = pd.DataFrame(
354
  {
355
  "Dataset": [
356
+ "S2ORC",
357
  ],
358
  "Lines Downloaded": [
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+ "12963563",
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  ],
361
  "Percent Removed After Language Filter": [
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  "0.00%",
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  ],
364
  "Percent Removed After Min Word Count Filter": [
365
+ "0.00%",
366
  ],
367
  "Percent Removed After Unigram Probability Filter": [
368
  "0.00%",
 
382
  med_filter = pd.DataFrame(
383
  {
384
  "Dataset": [
385
+ "PubMed - Central",
386
  ],
387
  "Lines Downloaded": [
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+ "5230932",
389
  ],
390
  "Percent Removed After Language Filter": [
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+ "7.66%",
392
  ],
393
  "Percent Removed After Min Word Count Filter": [
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+ "1.29%",
395
  ],
396
  "Percent Removed After Unigram Probability Filter": [
397
+ "0.02%",
398
  ],
399
  "Percent Removed After Local Dedup": [
400
  "",
 
411
  phil_filter = pd.DataFrame(
412
  {
413
  "Dataset": [
414
+ "Phil Papers",
415
  ],
416
  "Lines Downloaded": [
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+ "49389",
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  ],
419
  "Percent Removed After Language Filter": [
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+ "20.68%",
421
  ],
422
  "Percent Removed After Min Word Count Filter": [
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+ "0.00%",
424
  ],
425
  "Percent Removed After Unigram Probability Filter": [
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+ "0.12%",
427
  ],
428
  "Percent Removed After Local Dedup": [
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  "",