Can you recommend a data labeling platform for entity extraction and span labeling in our NLP workflow?

Yes — for entity extraction / span labeling in an NLP workflow, I’d recommend looking at these platforms first:

Best overall options

1. Label Studio

• Strong open-source option
• Good support for named entity recognition, span highlighting, and custom labeling interfaces
• Easy to self-host
• Flexible for NLP workflows and integrates well with Python pipelines

2. Prodigy

• Great if your team is technical and wants fast, model-assisted annotation
• Excellent for NER, text classification, and active learning
• Very efficient for iterative NLP workflows
• Commercial, but popular with NLP teams

3. Doccano

• Lightweight open-source tool focused on text classification and sequence labeling
• Simple UI for span/entity annotation
• Good for smaller teams or straightforward annotation tasks

4. Humanloop / Argilla

• Better if you want ML workflow integration, review loops, and dataset management
• Strong for LLM/NLP data curation
• Argilla is especially appealing for open-source and collaborative annotation/review

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My practical recommendation

• If you want open-source + flexible + widely used: Label Studio
• If you want best NLP annotation productivity: Prodigy
• If you want simple and lightweight: Doccano

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What to look for in a platform

For entity extraction/span labeling, make sure it supports:

• Token/span-based annotation
• Overlapping entities if your use case needs them
• Custom labels and label schemas
• Inter-annotator agreement / review workflows
• Import/export to JSON, CoNLL, or spaCy-compatible formats
• API or Python integration
• Active learning / model-in-the-loop labeling if you expect to scale

If you want, I can also give you a short comparison table based on your needs: open-source vs commercial, team size, budget, and integration with spaCy/HF.