Datasets:
tillwenke
commited on
Commit
·
1686162
1
Parent(s):
463a104
add dataset
Browse files- .ipynb +0 -277
- data/passages.parquet/part.0.parquet +2 -2
- generate.py +1 -0
.ipynb
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"source": [
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"import pandas as pd\n",
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"a = pd.read_parquet(\"data/test.parquet\")\n",
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"b = pd.read_parquet(\"data/passages.parquet\")"
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" <th></th>\n",
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" <th>question</th>\n",
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" <th>answer</th>\n",
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" <th>relevant_passage_ids</th>\n",
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" <tr>\n",
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" <th>id</th>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Is Hirschsprung disease a mendelian or a multi...</td>\n",
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" <td>Coding sequence mutations in RET, GDNF, EDNRB,...</td>\n",
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" <td>[20598273, 6650562, 15829955, 15617541, 230011...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>List signaling molecules (ligands) that intera...</td>\n",
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" <td>The 7 known EGFR ligands are: epidermal growt...</td>\n",
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" <td>[23821377, 24323361, 23382875, 22247333, 23787...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Is the protein Papilin secreted?</td>\n",
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" <td>Yes, papilin is a secreted protein</td>\n",
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" <td>[21784067, 19297413, 15094122, 7515725, 332004...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Are long non coding RNAs spliced?</td>\n",
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" <td>Long non coding RNAs appear to be spliced thro...</td>\n",
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" <td>[22955974, 21622663, 22707570, 22955988, 24285...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Is RANKL secreted from the cells?</td>\n",
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" <td>Receptor activator of nuclear factor κB ligand...</td>\n",
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" <td>[22867712, 23827649, 21618594, 23835909, 24265...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4714</th>\n",
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" <td>Is PPROM a condition that occurs in males or f...</td>\n",
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" <td>Preterm premature rupture of fetal membranes (...</td>\n",
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" <td>[23599878, 23573382, 24304137, 18301713, 23179...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4715</th>\n",
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" <td>What is EpiMethylTag?</td>\n",
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" <td>EpiMethylTag is a fast, low-input, low sequenc...</td>\n",
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" <td>[31752933]</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4716</th>\n",
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" <td>What is the target of Sutimlimab?</td>\n",
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" <td>Sutimlimab is a novel humanized monoclonal ant...</td>\n",
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" <td>[30635392, 31229501, 33826820, 32176765, 31114...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4717</th>\n",
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" <td>Can parasite infections by Schistosoma japonic...</td>\n",
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" <td>A peptide named as SJMHE1 from Schistosoma jap...</td>\n",
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" <td>[26840774, 34703270, 28614408, 31496071, 18654...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4718</th>\n",
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" <td>Describe Multilocus Inherited Neoplasia Allele...</td>\n",
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" <td>Genetic testing of hereditary cancer using com...</td>\n",
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" <td>[30580288]</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>4719 rows × 3 columns</p>\n",
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],
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"text/plain": [
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" question \\\n",
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"id \n",
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"0 Is Hirschsprung disease a mendelian or a multi... \n",
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"1 List signaling molecules (ligands) that intera... \n",
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"2 Is the protein Papilin secreted? \n",
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"3 Are long non coding RNAs spliced? \n",
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"4 Is RANKL secreted from the cells? \n",
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"... ... \n",
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"4714 Is PPROM a condition that occurs in males or f... \n",
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"4715 What is EpiMethylTag? \n",
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"4716 What is the target of Sutimlimab? \n",
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"4717 Can parasite infections by Schistosoma japonic... \n",
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"4718 Describe Multilocus Inherited Neoplasia Allele... \n",
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"\n",
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" answer \\\n",
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"id \n",
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"0 Coding sequence mutations in RET, GDNF, EDNRB,... \n",
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"1 The 7 known EGFR ligands are: epidermal growt... \n",
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"2 Yes, papilin is a secreted protein \n",
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"3 Long non coding RNAs appear to be spliced thro... \n",
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"4 Receptor activator of nuclear factor κB ligand... \n",
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"... ... \n",
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"4714 Preterm premature rupture of fetal membranes (... \n",
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"4715 EpiMethylTag is a fast, low-input, low sequenc... \n",
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"4716 Sutimlimab is a novel humanized monoclonal ant... \n",
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"4717 A peptide named as SJMHE1 from Schistosoma jap... \n",
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"4718 Genetic testing of hereditary cancer using com... \n",
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"\n",
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" relevant_passage_ids \n",
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"id \n",
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"0 [20598273, 6650562, 15829955, 15617541, 230011... \n",
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"1 [23821377, 24323361, 23382875, 22247333, 23787... \n",
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"2 [21784067, 19297413, 15094122, 7515725, 332004... \n",
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"3 [22955974, 21622663, 22707570, 22955988, 24285... \n",
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"4 [22867712, 23827649, 21618594, 23835909, 24265... \n",
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"... ... \n",
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"4714 [23599878, 23573382, 24304137, 18301713, 23179... \n",
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"4715 [31752933] \n",
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"4716 [30635392, 31229501, 33826820, 32176765, 31114... \n",
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"4717 [26840774, 34703270, 28614408, 31496071, 18654... \n",
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"4718 [30580288] \n",
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"\n",
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"[4719 rows x 3 columns]"
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>passage</th>\n",
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" <tr>\n",
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" <th>id</th>\n",
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" <th>21495810</th>\n",
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" <td>OBJECT: Factors determining choice of an acade...</td>\n",
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" <tr>\n",
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" <th>26869762</th>\n",
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" <td>Castleman disease (CD) is a rare, heterogeneou...</td>\n",
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" <tr>\n",
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" <th>28049410</th>\n",
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" <td>BACKGROUND: Data extraction and integration me...</td>\n",
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" <tr>\n",
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" <th>24510469</th>\n",
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" <td>Flecainide is recommended as a first-line anti...</td>\n",
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" <tr>\n",
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" <th>8650761</th>\n",
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" <td>Primary intestinal lymphangiectasia (PIL), fir...</td>\n",
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" </tr>\n",
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"28049410 BACKGROUND: Data extraction and integration me...\n",
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"24510469 Flecainide is recommended as a first-line anti...\n",
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"8650761 Primary intestinal lymphangiectasia (PIL), fir..."
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data/passages.parquet/part.0.parquet
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:93afd23506e6ab65451a43a411fd6481b6378a00fef7983b44cc8c3ddbb37c84
|
| 3 |
+
size 24470604
|
generate.py
CHANGED
|
@@ -73,6 +73,7 @@ if __name__ == "__main__":
|
|
| 73 |
|
| 74 |
# intermediate save
|
| 75 |
if i % 1000 == 0:
|
|
|
|
| 76 |
dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET)
|
| 77 |
|
| 78 |
|
|
|
|
| 73 |
|
| 74 |
# intermediate save
|
| 75 |
if i % 1000 == 0:
|
| 76 |
+
passages.index.name = "id"
|
| 77 |
dd.from_pandas(passages, npartitions=1).to_parquet(PATH_TO_PASSAGE_DATASET)
|
| 78 |
|
| 79 |
|