classify_text
Classify texts using any model from the Hugging Face hub.
Note that we do not validate the model name before executing it, so make sure it
corresponds to an existing model in the hub, otherwise the step will fail.
Usage
The following example shows how the step can be used in a recipe.Examples
Examples
To infer the ternary sentiment of tweets using a CardiffNLP model
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Inputs
Inputs
A column of texts to classify.
Outputs
Outputs
The inferred class of each text. The labels of individual categories depend on the seleted model,
and/or can be specified manually using the
labels parameter (see below).Configuration
The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e.step(..., {"param": "value", ...}) -> (output).
Parameters
Parameters
The name of a model.
This should be the full name (including the organization if applicable) of a model in the
Hugging Face model hub. You can copy it by clicking on the
icon next to the model’s name on its dedicated web page.Note that if the name doesn’t correspond to a model existing in the hub the step will fail.
Since there are hundreds if not thousands of potential models, we cannot validate if the
name is correct before executing it.
The specific model version.
Can be a branch name, a tag name, or a commit id. To identify a particular revision, on
a model’s webpage (such as https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual),
browse to the Files and versions tab,
and use the branch or history dropdown menus to see the available branch names or commit IDs.
If not provided, will use the latest (newest) available version (usually from the “main” branch).
Map original model output to human-readable labels.
Unfortunately, many models in Hugging Face are badly configured and output labels like
LABEL_0,
LABEL_1, etc. which isn’t very helpful. You can use the “Hosted inference API”
widget on the model’s web page to test its output labels. If necessary, use this parameter
to map the default output labels to ones you prefer.Item properties
Item properties
One or more additional parameters.
Examples
Examples
- E.g. to map ternary sentiment labels
Minimum probability (score) to accept prediction label.
Class labels with a corresponding probability smaller than this value will be removed
(replaced with NaN, i.e. the missing value).Values must be in the following range:
How many texts to process simultaneously.
May get ignored when running on CPU.Values must be in the following range:
Number of threads used to feed GPU with texts.Values must be in the following range:
Which CPU/GPU to run model on.
Pass -1 to use CPU, and 0 to use first available GPU. By default, of
when passed
null, the step will use GPU automatically if one is found
otherwise CPU.ID of a Hugging Face integration configured in Graphext.
To use a private model from the Hugging Face hub, you need to configure a
Hugging Face “API Key” integration (in the relevant Graphext team > Add Integration
API KEYS > Add API Key > Hugging Face > paste an access token previously configured in your huggingface account). Graphext will automatically assign an ID to your integration which gets autocompleted where required (e.g. in the recipe editor).