Datasets:
doc_id stringlengths 6 8 | doc stringlengths 5.92k 14.2M |
|---|---|
34175989 | {"metadata": {"id": "34175989", "last_modified_date": "2023-11-15T00:00:00Z", "authors": {"0": {"author": "Eslava,Marcela"}, "1": {"author": "Melendez Arjona,Marcela"}, "2": {"author": "Tenjo Galvis,Laura Daniela"}, "3": {"author": "Urdaneta,Nicolás"}}, "count": "Latin America", "count_key": "517204", "docna": {"0": {"... |
34176024 | "{\"metadata\": {\"id\": \"34176024\", \"last_modified_date\": \"2023-10-06T00:00:00Z\", \"admreg\":(...TRUNCATED) |
34178367 | "{\"metadata\": {\"id\": \"34178367\", \"last_modified_date\": \"2024-12-05T00:00:00Z\", \"admreg\":(...TRUNCATED) |
34179149 | "{\"metadata\": {\"id\": \"34179149\", \"last_modified_date\": \"2023-11-16T00:00:00Z\", \"authors\"(...TRUNCATED) |
34186262 | "{\"metadata\": {\"id\": \"34186262\", \"last_modified_date\": \"2023-10-26T00:00:00Z\", \"authors\"(...TRUNCATED) |
34188936 | "{\"metadata\": {\"id\": \"34188936\", \"last_modified_date\": \"2023-10-31T00:00:00Z\", \"admreg\":(...TRUNCATED) |
34192225 | "{\"metadata\": {\"id\": \"34192225\", \"last_modified_date\": \"2023-11-07T00:00:00Z\", \"authors\"(...TRUNCATED) |
34192232 | "{\"metadata\": {\"id\": \"34192232\", \"last_modified_date\": \"2024-01-09T00:00:00Z\", \"admreg\":(...TRUNCATED) |
34192813 | "{\"metadata\": {\"id\": \"34192813\", \"last_modified_date\": \"2023-11-07T00:00:00Z\", \"admreg\":(...TRUNCATED) |
34192820 | "{\"metadata\": {\"id\": \"34192820\", \"last_modified_date\": \"2023-11-15T00:00:00Z\", \"authors\"(...TRUNCATED) |
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datause_raw_extractions
Raw World Bank document extractions (one document per line).
Each row has two columns:
doc_id— the document'smetadata.id.doc— a JSON string holding the full record (metadata+model_extractions, where eachmodel_extractionsentry is one page withinput_text,datasets,classifier_skipped,skip_reason).
from datasets import load_dataset
import json
ds = load_dataset('rafmacalaba/datause_raw_extractions')['train']
record = json.loads(ds[0]['doc'])
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