Tasks and inputs
vectara/hallucination_evaluation_model is listed for Text classification. It accepts text and produces label. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
Hugging Face repository for Text classification.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
vectara/hallucination_evaluation_model is a Hugging Face repository returned by the public Text classification task feed. Its recorded download count is 170,911 and it has 360 likes when the repository snapshot was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Text classification. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists transformers as its library metadata. Its recorded tags include transformers, safetensors, HHEMv2Config, text-classification, custom_code, en, arxiv:2205.12854, arxiv:2401.00396. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Use a held-out set from your own domain. Check per-class precision and recall, ambiguous examples, class imbalance and the meaning of every returned label; an overall accuracy score can hide the errors that matter most.
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Compare the model on the work you need to complete.
vectara/hallucination_evaluation_model is listed for Text classification. It accepts text and produces label. Use the publisher’s input format and preprocessing for your chosen deployment.
Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
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