DeepSData
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Labelled data for fine tuning a geological Named Entity Recognition and Entity Relation Extraction model

This dataset consists of annotated sentences extracted from BGS memoirs, DECC/OGA onshore hydrocarbons well reports and Mineral Reconnaissance Programme (MRP) reports, for fine-tuning geological Named Entity Recognition and Entity Relation Extraction models.

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Machine learning & corporaFree to access

Key facts

InstitutionBritish Geological Survey (BGS)
CoverageUK geology (rock formations, geological ages, rock types, physical properties, locations)
Time spanPublished 2024
Scale3 files
LicenseLicense statement is still being checked
AccessGitHub (BritishGeologicalSurvey/princeton-nlp-relation-extraction)

Contents & fields

The dataset is provided in JSONL format, containing sentences from BGS memoirs, DECC/OGA onshore well reports, and MRP reports, annotated with entities and relations. Entities include rock formations, geological ages, rock types, physical properties, and locations; relations include overlies and observedIn. Data exported from doccano annotation tool.

  • rock formations——rock formation entity
  • geological ages——geological age entity
  • rock types——rock type entity
  • physical properties——physical property entity
  • locations——location entity
  • overlies——relation: overlies
  • observedIn——relation: observed in

Research uses

Can be used to fine-tune pre-trained language models for automatic extraction of structured information from unstructured geological text, supporting geological modelling and subsurface characterisation.

Information comes from the source page. Please check that page for current details and terms.

Keywords

geologynamed entity recognitionrelation extractionlabelled dataNLPBritish Geological Survey

Access & license

License: License statement is still being checked | Free to access

Why this is hard to get on your own

High-quality labelled geological data for NLP fine-tuning is scarce; this dataset provides a proof-of-concept annotated sample.

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