Text Labeling Tool

Annotate text for Named Entity Recognition (NER) or Span Categorization. Easy to set up and integrate into your workflow.

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Interfaces for NER and span categorization

Flexible labels for your use case

Non-overlapping labels for Named Entity Recognition (NER)
Potentially overlapping labels for span categorization
Character, word, and sentence-level labels
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Bulk upload samples

Use our web platform to bulk upload samples.
Support for txt, json and csv files.

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Bulk upload text samples
Customizable hotkeys

Customizable hotkeys

Label data with your keyboard by using customizable hotkeys.

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Why ML teams choose Segments.ai


    from segments import SegmentsClient

    client = SegmentsClient("api_key")

    dataset = client.add_dataset(
      "dataset_name", 
      task_type, 
      task_attributes
    )

    sample = client.add_sample(
      "user/dataset_name", 
      "sample_name", 
      attributes
    )
                  
TensorFlow, AWS, Hugging Face, and PyTorch logo

Easy to set up and integrate

Integrate data labeling into your existing ML pipelines and workflows using our simple yet powerful Python SDK.

Connect your cloud provider (AWS, Google Cloud, Azure)

Export to popular ML frameworks (PyTorch, TensorFlow, 🤗 Hugging Face)

Manage your workforce

Collaborate with your team or onboard an external workforce. Our management tools make it easy to build and review large datasets together.

Give collaborators access to specific datasets and choose their permissions

Onboard an external workforce from our labeling service partners

Set up a reviewing step and communicate with labelers via issues

Track labeling performance through metrics and custom dashboards.

Model predictions vs. corrected labels
Model predictions vs. corrected labels

Set up advanced workflows

Implement active learning by using your model predictions to find failure cases and to speed up labeling.

STEP 1

Label images and point clouds on Segments.ai

STEP 2

Train a model and keep it on your side. Only upload model predictions

STEP 3

Compare predictions with ground-truth labels and use our search syntax to find failure cases

STEP 4

Use model predictions as prelabels to speed up labeling

Start building better datasets today

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