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Add scripts to create MS MARCO v1 Document Jsonl Collection #905

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merged 9 commits into from
Dec 14, 2021
Merged

Add scripts to create MS MARCO v1 Document Jsonl Collection #905

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198ea27
add ms marco doc collection creation script
ronakice Dec 7, 2021
c487a8a
update ms marco doc collection creation script
ronakice Dec 7, 2021
a79800c
update ms marco doc segmented collection creation script
ronakice Dec 8, 2021
8da7f2f
update ms marco doc segmented collection creation script
ronakice Dec 8, 2021
1688d68
update ms marco doc segmented collection creation script
ronakice Dec 8, 2021
3aee6fe
update ms marco doc segmented collection creation script
ronakice Dec 8, 2021
7d10f1a
update ms marco doc segmented collection creation script
ronakice Dec 8, 2021
4cd6f31
update ms marco doc segmented collection creation script
ronakice Dec 8, 2021
2372641
remove accidental config file
ronakice Dec 14, 2021
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update ms marco doc segmented collection creation script
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@ronakice
ronakice committed Dec 8, 2021
commit 8da7f2fefcc055173454f02f50de8e786272947d
1 change: 0 additions & 1 deletion scripts/msmarco-doc/convert_msmarco_doc_to_anserini_collection.py
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Expand Up @@ -26,7 +26,6 @@ def generate_output_dict(doc):
f_corpus = gzip.open(args.original_docs_path, mode='rt')
f_out = open(args.output_docs_path, 'w')

print('Appending predictions...')
for line in tqdm(f_corpus):
output_dict = generate_output_dict(line.split('\t'))
f_out.write(json.dumps(output_dict) + '\n')
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4 changes: 2 additions & 2 deletions scripts/msmarco-doc/convert_msmarco_doc_to_segmented_anserini_collection.py
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Expand Up @@ -17,7 +17,7 @@ def generate_output_dicts(doc, nlp, max_length, stride):
segment = ' '.join(sentences[i:i + max_length])
doc_text = f'{doc_url}\n{doc_title}\n{segment}'
output_dicts.append({'id': f'{doc_id}#{i}', 'contents': doc_text})
return output_dict
return output_dicts


if __name__ == '__main__':
Expand All @@ -38,7 +38,7 @@ def generate_output_dicts(doc, nlp, max_length, stride):
f_corpus = gzip.open(args.original_docs_path, mode='rt')
f_out = open(args.output_docs_path, 'w')

print('Appending predictions...')
print('Creating collection...')
for line in tqdm(f_corpus):
output_dicts = generate_output_dicts(line.split('\t'), nlp, args.max_length, args.stride)
for output_dict in output_dicts:
Expand Down