Datasets:
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values | provenance stringclasses 2
values | failure_reason stringclasses 2
values | difficulty_weight float64 0 7 | quality_pass_fraction float64 0.13 1 | register dict | register_proximity float64 0 0.97 | token_edit float64 0.2 0.9 | gptzero_class stringclasses 1
value | quality_passed bool 1
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BY AND BETWEEN
AETHERION BIOTECH PRIVATE LIMITED, a company incorporated under the Companies Act, 2013, having its registered office at Bengaluru, Karnataka, India, beari... | THIS COMMERCIAL COLLABORATION AND TECHNOLOGY SUPPLY AGREEMENT (“Agreement”) is made and entered into on this [●] day of [●], 2026 (“Effective Date”),
BY AND BETWEEN
AETHERION BIOTECH PRIVATE LIMITED, a company incorporated under the Companies Act, 2013, having its registered office at Bengaluru, Karnataka, India, CIN [... | 006c11ff87ad99f92e24c29770b329a886fa922ea96cfa3ed2850e031cf47219 | api | natural | none | 7 | 0.125 | {
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[
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When analysing a business case study, assessing the macro and competitive environments is essential for understanding the full picture of the challenges and opportunities a company faces. Without this context, any recommendations ma... | Why is it important to assess the macro and competitive environments when analysing a case study?
In order to make a sound analysis of a case study, it is necessary to understand the macro and competitive environments in which the business operates. Otherwise, the analysis will be incomplete and the recommendations mad... | 05aea9100e5af957d1336e0ca9c1aad4c3d437aaa3b402ba7c9bd5a6166686e1 | api | natural | none | 0.142857 | 0.875 | {
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The internet has changed the way people communicate, learn, and live. While some people argue that it has created problems such as cyberbullying, misinformation, and addiction, its positive impact on society is much greater than its negative effects. The internet is a blessing to soci... | The Internet is a Blessing to Society
The internet has revolutionized the way we communicate, learn, and interact with the world. While it is true that the internet has some negative effects, such as cyberbullying, misinformation, and internet addiction, its positive impact on society outweighs its negative effects. T... | 079d03c213c5e74713c5ce13a69586291d55c57c74a0cf2688b2e072ee061c7c | api | natural | none | 1 | 0.5 | {
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[
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rest-v3
rest-v3 is an English text-rewriting dataset for supervised fine-tuning of a humanizing editor. Each record asks a model to rewrite a source text while preserving its meaning and contains a detector-verified natural-language rewrite.
Dataset composition
The training split contains 1,116 JSONL records:
- 1,033 newly mined, on-policy rewrites from the
from-final-bestgenerator checkpoint. - 83 compatible existing verified examples.
- 541 examples sourced from the API corpus and 575 from the web corpus.
Every record includes chat-formatted messages, the source and winning rewrite, a normalized source hash, source type, quality metadata, GPTZero verdict, register features, token-edit distance, and a normalized training weight.
Construction
The new examples were built in two stages:
- Phase 0 — generation and quality gates. A balanced corpus of 1,362 English texts (681 API and 681 web) produced eight mode-diverse rewrites per source. All candidates passed semantic, length, and faithfulness gates before selection. Candidates with a token-edit ratio below 0.20 were excluded from detector verification.
- Phase 1 — detector verification. Register-eligible candidates were ranked by proximity to the target register and checked with GPTZero. Verification stopped for a source after its first
humanverdict. Across 1,822 checks, 1,033 rewrites received ahumanverdict.
The verification run used 1,031,053 detector words. No model training was performed as part of dataset construction.
Weights and legacy data
For each record with complete metadata, the raw training weight is difficulty_weight × register_proximity. difficulty_weight is inverse quality-pass frequency across K=8 candidates, and register_proximity measures closeness to the target function-word, adjective, and lexical-diversity ranges. Source-type scaling then equalizes the mean weight between API and web records, preserving the dataset's observed source-type count ratio instead of allowing a harder source type to dominate total loss mass.
The 17 Turso-only historical examples without weight metadata are excluded. The recovered gate-fix example is merged when its source hash is not already represented; duplicate source hashes resolve to the newly verified Phase 1 example.
Intended use
Use this dataset for research and supervised fine-tuning of English text-to-text rewriting systems. The difficulty_weight, quality_pass_fraction, and register metadata support weighted or stratified training analyses.
Limitations
GPTZero human verdicts are detector outcomes, not proof of human authorship or universal quality. The data is synthetic and inherits limitations from its source corpora, generation checkpoint, quality gates, and detector. Review suitability, licensing, privacy, and safety for your deployment before use.
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