metadata
language:
- en
license: cc-by-sa-4.0
task_categories:
- token-classification
- text-classification
task_ids:
- named-entity-recognition
- part-of-speech
- parsing
tags:
- ner
- pos
- dependency-parsing
- dialog-act
- multi-task
- conllu
- spacy
- gpt-validated
- llm-gold-filtered
- knowledge-distillation
size_categories:
- 100K<n<1M
kniv-corpus-en
Multi-domain NLP corpus for English with annotations for NER (18 entity types), POS tagging (17 UPOS tags), dependency parsing (dep2label), and sentence classification (9 dialog act types). Built for training kniv multi-task NLP models for the uniko cognitive memory system.
All data is commercially licensed (CC BY-SA 4.0 compatible). No CoNLL-2003, GMB, or other research-only datasets.
Repository Structure
dragonscale-ai/kniv-corpus-en/
├── data/ # Full corpus Parquet (for load_dataset)
│ ├── train-00000-of-00001.parquet 525,850 sentences (127.7 MB)
│ ├── dev-00000-of-00001.parquet 65,731 sentences (13.8 MB)
│ └── test-00000-of-00001.parquet 65,732 sentences (14.0 MB)
├── corpus/
│ └── gold/ # LLM-validated gold corpus (recommended)
│ ├── train.parquet 237,646 sentences (56.8 MB)
│ ├── dev.parquet 65,731 sentences (13.8 MB)
│ ├── test.parquet 65,732 sentences (14.0 MB)
│ └── progress.json Filter progress metadata
├── train.conllu # Full corpus in CoNLL-U format (547.7 MB)
├── dev.conllu # (67.9 MB)
├── test.conllu # (68.8 MB)
├── metadata.json # Split sizes, domain list
└── README.md
Full corpus vs Gold corpus
Full (data/) |
Gold (corpus/gold/) |
|
|---|---|---|
| Train sentences | 525,850 | 237,646 |
| Validation | spaCy + GPT corrections | + Qwen3-8B per-example validation |
| Checks applied | None | NER entities, CLS labels, POS tags |
| Recommended for | Exploration, analysis | Model training |
The gold corpus was produced by validating every sentence with Qwen3-8B (64 concurrent requests via vLLM on A100). Each sentence passed three checks:
- NER: every entity span validated ("Is '{entity}' correctly tagged as {type}?")
- CLS: dialog act label validated with conversational context
- POS: up to 8 POS tags per sentence validated against Universal POS tagset
Sentences failing any check were removed. 45.2% of training data survived (237K from 525K).
Dataset Formats
Parquet (recommended)
Each row contains:
| Column | Type | Description |
|---|---|---|
sent_id |
string | Unique sentence ID ({domain}-{index}) |
text |
string | The sentence text |
prev_text |
string (nullable) | Previous utterance in conversation (for CLS context) |
cls |
string | Dialog act label (one of 9 types) |
tokens |
list[string] | Tokenized words |
pos_tags |
list[string] | Universal POS tags (17 UPOS labels) |
heads |
list[int] | Dependency head indices (0 = root) |
deprels |
list[string] | Dependency relation labels |
ner_tags |
list[string] | BIO-tagged NER labels (18 entity types) |
CoNLL-U
Standard CoNLL-U format with extensions:
# sent_id = conversation-004217
# text = Oh, could you please change it for outside?
# prev_text = It looks like the table will be for inside.
# cls = correction
1 Oh oh INTJ UH _ 5 discourse _ _
2 , , PUNCT , _ 5 punct _ _
3 could could AUX MD _ 5 aux _ _
4 you you PRON PRP _ 5 nsubj _ _
5 please please VERB VB _ 0 root _ _
6 change change VERB VB _ 5 xcomp _ _
7 it it PRON PRP _ 6 obj _ _
8 for for ADP IN _ 9 case _ _
9 outside outside NOUN NN _ 6 obl _ _
10 ? ? PUNCT . _ 5 punct _ _
NER tags in MISC column: NER=B-ORG, NER=I-ORG, etc.
Dataset Details
| Total sentences | 657,313 |
| Gold sentences | 237,646 |
| NER entity types | 18 (BIO-tagged = 37 labels) |
| POS tags | 17 (Universal Dependencies UPOS) |
| Dep labels | ~1,440 (dep2label composite tags) |
| CLS labels | 9 (dialog act types) |
| Annotation | spaCy en_core_web_trf + GPT-5.4-nano validation |
| Gold filtering | Qwen3-8B via vLLM (NER + CLS + POS) |
| License | CC BY-SA 4.0 |
Domains
| Domain | Full | Gold | Sources | License |
|---|---|---|---|---|
| Business | 197,891 | 73,525 | SEC EDGAR 10-K, Enron emails, OpenStax, Odoo docs, Wikipedia, CUAD contracts, OpenAlex | Public domain / CC BY-4.0 / CC BY-SA 3.0 / ODC-BY |
| Technical | 193,032 | 56,438 | Wikipedia (CS/engineering), Python docs | CC BY-SA 3.0 / PSF License |
| Conversation | 89,934 | 29,203 | Taskmaster 1/2/3, OASST1, MultiWOZ 2.2, Glaive, Discord Dialogues | CC BY-4.0 / Apache 2.0 / MIT |
| Encyclopedic | 66,490 | 29,088 | Wikipedia (science, history, geography, arts) | CC BY-SA 3.0 |
| News | 58,026 | 27,541 | Wikinews, Wikipedia (journalism, politics) | CC BY 2.5 / CC BY-SA 3.0 |
| Narrative | 51,940 | 21,851 | Project Gutenberg (classic fiction) | Public domain |
NER Entity Types (18)
| Type | Description | Example |
|---|---|---|
| PERSON | People | Caroline, Dr. Smith |
| ORG | Organizations | Apple, United Nations |
| GPE | Geopolitical entities | France, New York |
| LOC | Locations | Mount Everest, Pacific Ocean |
| DATE | Dates and periods | January 2024, last quarter |
| TIME | Times | 3pm, two hours |
| MONEY | Monetary values | $394B, 50 million euros |
| PERCENT | Percentages | 15.3%, a third |
| QUANTITY | Measurements | 100 kilometers, 5 pounds |
| ORDINAL | Ordinal numbers | first, 3rd |
| CARDINAL | Cardinal numbers | three, 42 |
| NORP | Nationalities/groups | British, Republican |
| FAC | Facilities | the White House, Highway 101 |
| PRODUCT | Products | iPhone, Boeing 747 |
| EVENT | Events | World War II, Olympics |
| WORK_OF_ART | Works of art | Hamlet, The Starry Night |
| LAW | Laws | the First Amendment |
| LANGUAGE | Languages | English, Mandarin |
CLS Dialog Act Labels (9)
| Label | Description | Uniko action | Gold count |
|---|---|---|---|
inform |
States a fact or observation | Extract observation | 173,401 (73.0%) |
question |
Asks for information | Record knowledge gap | 14,979 (6.3%) |
plan_commit |
Commits to action, offers, suggests | Link to Goal/Task | 12,518 (5.3%) |
request |
Asks someone to do something | Create action node | 10,679 (4.5%) |
filler |
Fragments, headers, formatting | Skip entirely | 10,538 (4.4%) |
correction |
Corrects or contradicts prior statement | Flag for update | 7,587 (3.2%) |
social |
Greeting, goodbye, thanks, apology | Skip extraction | 3,111 (1.3%) |
agreement |
Agrees with or confirms something | Reinforce observation | 2,956 (1.2%) |
feedback |
Acknowledges without adding info | Skip extraction | 1,877 (0.8%) |
dep2label Encoding
Dependency parsing reformulated as token classification using rel-pos encoding (Strzyz et al., 2019):
+1@nsubj@VERB → "1st VERB to the right, relation=nsubj"
Usage
Load with HuggingFace Datasets
from datasets import load_dataset
# Full corpus
ds = load_dataset("dragonscale-ai/kniv-corpus-en")
# Gold corpus (recommended for training)
from huggingface_hub import snapshot_download
snapshot_download(
"dragonscale-ai/kniv-corpus-en",
repo_type="dataset",
allow_patterns="corpus/gold/*",
local_dir="kniv-data",
)
import pandas as pd
gold = pd.read_parquet("kniv-data/corpus/gold/train.parquet")
print(f"Gold training: {len(gold)} sentences")
Train a multi-task model
git clone https://github.com/rustic-ai/kniv-nlp-models
cd kniv-nlp-models
# Prepare training data from gold corpus
python models/deberta-v3-large-nlp-en/prepare_data.py --gold corpus/output/final-gold
# Train teacher (DeBERTa-v3-large, 304M params)
python models/deberta-v3-large-nlp-en/train.py
# Quick test (1 epoch, 100 samples)
python models/deberta-v3-large-nlp-en/train.py --quick-test
Annotation Pipeline
collect → preprocess → annotate (spaCy) → validate (GPT) → classify (GPT) → export → gold filter (Qwen3-8B)
- Collect — raw text from open sources (APIs, HuggingFace datasets, git repos)
- Preprocess — sentence splitting, cleaning, dedup, prev_text linking for conversations
- Annotate — spaCy
en_core_web_trffor POS, NER, dependency parse - Validate — GPT-5.4-nano checks annotations in batches, returns corrections
- Classify — GPT-5.4-nano assigns dialog act labels (with prev_text context for conversations)
- Export — corrections applied, BIO tags repaired, split into train/dev/test
- Gold filter — Qwen3-8B validates every NER entity, CLS label, and POS tag independently. 64 concurrent requests via vLLM on A100. Sentences failing any check removed.
Quality Notes
- POS/Dep: UD English EWT v2.14 data used for POS+Dep training head (expert-annotated, not gold-filtered — already gold standard)
- NER: spaCy annotations validated by GPT-5.4-nano, then gold-filtered by Qwen3-8B. ~9.3% of entities rejected.
- CLS: GPT-5.4-nano classification validated by Qwen3-8B. ~4.0% rejected. Context-dependent labels (correction, agreement) remain noisier than context-free ones.
- POS on corpus: spaCy POS validated by Qwen3-8B. ~41% rejected (aggressive filtering — checks 8 tokens per sentence, so even one disagreement drops the sentence).
Source
- Training code: rustic-ai/kniv-nlp-models
- Trained models: dragonscale-ai on HuggingFace
Citation
@misc{kniv-corpus-en,
title={kniv-corpus-en: Multi-domain NLP Corpus for English},
author={Dragonscale AI},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/datasets/dragonscale-ai/kniv-corpus-en}
}
License
CC BY-SA 4.0. Individual domain source licenses are listed in the domains table above.