name: annotation-and-dataset-tools
description: "Convert between annotation formats, build lexicon NER, and split
or batch labeled datasets."
metadata:
disco-role: operating
disable-model-invocation: true
license: Apache 2.0
Annotation and dataset tools
Use this sub-skill when the task is about labeled-text formats, sequence-tag conversion, lexicon NER, or batching and splitting annotated corpora.
Include here
- NER / token conversion:
jio.ner.entity2tag, jio.ner.tag2entity, jio.ner.char2word, jio.ner.word2char
- CWS conversion:
jio.cws.word2tag, jio.cws.tag2word
- POS conversion:
jio.pos.pos2tag, jio.pos.tag2pos
jio.ner.LexiconNER
jio.ner.TokenSplitSentence, jio.ner.TokenBreakLongSentence, jio.ner.TokenBatchBucket
jio.ner.analyse_dataset for NER and jio.text_classification.analyse_dataset for text-classification corpora
jio.ner.collect_dataset_entities
jio.ner.check_person_name
jio.cws.f1 / jio.ner.f1 helpers when comparing predictions to gold labels
Exclude or route elsewhere
- Raw text cleanup or HTML stripping →
text-cleaning-and-extraction
- Semantic parsing of time, money, location, or IDs →
parsing-and-normalization
- Augmentation →
text-augmentation
- Dictionary loaders and higher-level analytics →
dictionaries-and-language-analysis
What to read
references/api-reference.md for data-format signatures and recoverable wrappers.
references/data-formats.md for BIOES, BI, entity, and split-dataset shapes.
references/troubleshooting.md for NumPy and tag-alignment issues.
scripts/smoke_annotation.py for a fast validation run.
Typical flow
- Decide which annotation schema is in use.
- Convert between entity and tag formats only when token boundaries are aligned.
- Use the dataset split helper after labels are normalized.
- Keep
max_sen_len consistent across the NER batching wrappers.
Quick cues
- Ask for this sub-skill when the user says "convert BIOES tags", "split the NER dataset", "build lexicon NER", "batch long sentences", or "count labeled entities".
- Stay here if the task is about annotation plumbing rather than raw text parsing.