How to hf text classification in Hugging Face

HF Text Classification in Hugging Face — start free

Inputs

The fields this action accepts.

Every field can be mapped from an upstream trigger, AI step, table row, or hard-coded literal.

Field	Type	Required	Description
Model model	string	Required	HF model id
Text text	string	Required	Text (required)

Sample request

{
  "model": "e.g. cardiffnlp/twitter-roberta-base-sentiment-latest",
  "text": "{{trigger.text}}"
}

Returns

[
  [
    {
      "label": "POSITIVE",
      "score": 0.99
    }
  ]
]

Use these fields in downstream nodes for routing, logging, or error handling.

FAQ

Questions about HF Text Classification.

What does the HF Text Classification action do in Hugging Face?

Runs any classifier model from HF Hub on text input — sentiment, toxicity, NER, intent. Pick the right classifier for your use case from the broad Hub catalog.

What inputs does HF Text Classification require?

Required: Model, Text. Every input accepts a static value or a variable from any upstream node in your workflow.

Can I use dynamic inputs from earlier workflow nodes?

Yes. Any field on this action can pull values from upstream nodes, whether that's a form response, a trigger payload, an AI output, or a lookup result.

What happens if Hugging Face returns an error?

The workflow pauses on the failed node, the error message is captured in the run log, and you can retry the run with one click. Auto-retry policies are configurable per workflow with exponential backoff up to 5 attempts.

Does HF Text Classification support batch operations?

Yes. Run HF Text Classification inside a Loop node to process arrays. TinyCommand handles Hugging Face's rate limits automatically so you don't have to throttle manually.

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