Custom Text Datasets for AI Training and NLP
Wavebreak Media creates and annotates text for classification, information extraction, semantic search and LLM fine-tuning. Commission new content or have authorized source text prepared to your schema.
Text Dataset Types
Domain-Specific Corpora
Text authored or curated around your subject, terminology and writing style.
Text Classification
Records labeled by topic, intent, sentiment, safety category or your own taxonomy.
Named Entity Recognition
Text annotated with entity spans and types, such as organizations, products, dates or specialist terms.
Search and Retrieval
Query-passage pairs with relevance labels and positive or negative matches for semantic search and ranking.
Questions and Instructions
Question-answer or prompt-response pairs, with supporting context where required, for language-model training and testing.
Summarization
Source text paired with reference summaries that follow your coverage, length and style requirements.
Define Your Text Dataset
- Content and coverage: language, locale, domain, record length, volume and dataset splits.
- Structure and annotation: fields, labels, entities, annotation rules and edge cases.
- Sources and rights: creation or sourcing method, authorized materials and permitted uses and rights documentation.
- Delivery: JSONL, JSON, CSV, TXT or an agreed format, with schema documentation and transfer requirements.
From Brief to Validated Text Records
- Agree the specification: confirm the task, sample record structure and acceptance criteria.
- Create and annotate: author new text or prepare authorized source material using the approved instructions.
- Validate: check completeness, label consistency, duplicates and schema compliance against the agreed quality criteria.
- Deliver: package records, dataset splits and documentation in the agreed format.
Frequently Asked Questions (FAQ)
This service focuses on language records and their labels. For page layouts, tables and OCR, see document datasets. For text paired with images or video, see vision-language datasets.
Feasibility is reviewed against the languages, subject expertise, volume and validation requirements in your brief. Language and domain coverage are confirmed before production.
Yes. A project can include held-out text records, test prompts, reference answers or scoring criteria. For a dedicated testing brief, see model evaluation datasets.
Start with the model task, language, domain, approximate record count and deadline. An example record with expected labels or responses helps define the scope; include a schema if you already have one.
Request a Custom Text Dataset
Send your brief and an example record to scope production and annotation.
