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Prepared Datasets

This page provides a detailed overview of the official datasets for GLiClass models.

Name▼Total examples▼Unique labels▼Cache size (GB)▼
gliclass-v2.01 196 2181 382 9521.11
gliclass-v2.0-RAC612 142857 0271.31

gliclass-v2.0-RAC​

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To further enhance classification performance, we generated a Retrieval-Augmented Classification (RAC) dataset. Each text example in the gliclass-v2.0 dataset was encoded using the paraphrase-MiniLM-L6-v2 sentence transformer and indexed in an HNSW (Hierarchical Navigable Small World) database. For 250k randomly selected samples, we retrieved up to three most similar examples (cosine similarity > 0.5) from the dataset.

During augmentation:

  • The number of retrieved examples per sample was randomly chosen between 1 and 3.
  • 30% of retrieved examples were replaced with random, unrelated examples to introduce controlled noise.
  • If true labels were present in a retrieved example, false labels were removed with a 50% probability to balance information clarity.

Each retrieved example was formatted using structured <<EXAMPLE>> ... <</EXAMPLE>> tags, where:

  • True labels were explicitly marked as <<TRUE_LABEL>> {label}.
  • False labels were marked as <<FALSE_LABEL>> {label}, unless removed.

For each randomly selected 250k examples, the “text” was modified as {original_text} <<EXAMPLE>> {retrieved_text} {true_labels_str} {false_labels_str} <</EXAMPLE>>... Where:

  • {original_text} is the original example text.
  • {retrieved_text} is a similar or randomly selected example.
  • {true_labels_str} contains true labels formatted as <<TRUE_LABEL>> {label}.
  • {false_labels_str} contains false labels formatted as <<FALSE_LABEL>> {label} (unless removed with 50% probability).

Such a strategy allows the model to learn how to utilize the provided information without overfocusing on RAC examples. With both relevant and randomly retrieved examples, the dataset maintains a balance between useful contextual information and controlled noise. This ensures that the model does not become overly reliant on retrieval-augmented inputs while still benefiting from additional context when available.