Datasets:
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
Tags:
data-use
dataset-mention
named-entity-recognition
relation-extraction
text-classification
forced-displacement
License:
FCV data-use paper corpus (6 configs)
Browse files- README.md +7 -16
- extraction/train.jsonl +2 -2
README.md
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path: "impact/validation.jsonl"
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- split: holdout
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path: "impact/holdout.jsonl"
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- config_name: synthetic
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data_files:
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- split: train
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path: "synthetic/train.jsonl"
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- config_name: synthetic-relation
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data_files:
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- split: train
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| Config | Splits (records) |
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|---|---|
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| `extraction` | train:
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| `relation` | train: 640, validation: 91, holdout: 21 |
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| `impact` | train: 604, validation: 151, holdout: 84 |
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| `synthetic` | train: 17611 |
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| `synthetic-relation` | train: 976 |
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| `sources` | train: 5445 |
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## Schemas
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- `
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{"named_data", "descriptive_data", "vague_data"}, "entity_descriptions"}}`.
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- `
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`
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- `synthetic-relation`: relation-format synthetic training data (`{"input",
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"output": {"entities": {"named_data", "organization"}, "relations":
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[{"<type>": {"head", "tail"}}]}, "_meta"}`) from
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`relation_training_synthetic` and `relation_llm_generated`, with a `source`
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column.
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- `extraction` rows carry a `meta` field tagging provenance: `FCV`
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(forced-displacement / FCV corpus) or `PRWP` (Policy Research Working Papers
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-- manually annotated + reviewed/validated). `train` = FCV (2,473) + PRWP
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(2,710); `validation` and `test` are FCV-only.
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- `relation`: `{"input", "output": {"entities": {"named_data",
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"organization"}, "relations": [{"<type>": {"head", "tail"}}]}, "_meta"}`
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with types `has_organization`, `used_by`, `has_acronym`, `has_timeframe`,
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path: "impact/validation.jsonl"
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- split: holdout
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path: "impact/holdout.jsonl"
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- config_name: synthetic-relation
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data_files:
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- split: train
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| Config | Splits (records) |
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|---|---|
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+
| `extraction` | train: 22794, validation: 415, test: 1706 |
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| `relation` | train: 640, validation: 91, holdout: 21 |
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| `impact` | train: 604, validation: 151, holdout: 84 |
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| `synthetic-relation` | train: 976 |
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| `sources` | train: 5445 |
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## Schemas
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- `extraction` / `sources`: `{"input", "output": {"entities":
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{"named_data", "descriptive_data", "vague_data"}, "entity_descriptions"}}`.
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- `extraction` rows carry a `meta` field tagging provenance: `FCV`
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(forced-displacement / FCV corpus), `PRWP` (Policy Research Working Papers --
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manually annotated + reviewed/validated), or `synthetic` (seed-based synthetic
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training data, flat-NER). `train` = FCV + PRWP + synthetic; `validation` and
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`test` are FCV-only.
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- `synthetic-relation`: relation-format synthetic training data (`{"input",
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"output": {"entities": {"named_data", "organization"}, "relations":
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[{"<type>": {"head", "tail"}}]}, "_meta"}`) from
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`relation_training_synthetic` and `relation_llm_generated`, with a `source`
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column.
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- `relation`: `{"input", "output": {"entities": {"named_data",
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"organization"}, "relations": [{"<type>": {"head", "tail"}}]}, "_meta"}`
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with types `has_organization`, `used_by`, `has_acronym`, `has_timeframe`,
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extraction/train.jsonl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:c4e4a1dcf4107a7558e571fb9bf50b62a3d9aa48822fe41a052b0e456ed6e298
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size 58100138
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