embed_text
Parse and calculate a (word-averaged) embedding vector for each text.
An embedding vector is a numerical representation of a text, such that different numerical components of the vector
capture different dimensions of the text’s meaning. Embeddings can be used, for example, to calculate the semantic similarity
between pairs of texts (see link_embeddings, for example, to create a network of texts connected by similarity).
In this step, embeddings of texts are calculated as (weighted) averages of the embeddings of each text’s individual words (the individual word embeddings are GloVe vectors, as provided by spaCy’s language models).
Use either the language parameter or a second input column to specify the language of the input texts. If neither
is provided, the language will be inferred automatically from the texts themselves (which is equivalent to first creating
a language column using the infer_language step).
Usage
The following example shows how the step can be used in a recipe.
Examples
Examples
To calculate embeddings in a way that emphasizes entities (recognized products, people etc.) over regular words:
To calculate embeddings in a way that emphasizes entities (recognized products, people etc.) over regular words:
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
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 text column to calculate embeddings for.
An (optional) column identifying the languages of the corresponding texts. It is used to identify the correct model (spaCy)
to use for each text. If the dataset doesn’t contain such a column yet, it can be created using the infer_language step.
Ideally, languages should be expressed as two-letter
ISO 639-1 language codes, such as “en”, “es” or “de” for
English, Spanish or German respectively. We also detect fully spelled out names such as “english”, “German”, “allemande”
etc., but it is not guaranteed that we will recognize all possible spellings correctly always, so ISO codes should be
preferred.
Alternatively, if all texts are in the same language, it can be identified with the language parameter instead.
Outputs
Outputs
A column of embedding vectors capturing the meaning of each input text.
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
Configure how embeddings are calculated. Toggle word vector weighting and normalization.
Whether to enable support for additional languages. By default, Arabic (“ar”), Catalan (“ca”), Basque (“eu”), and Turkish (“tu”) are not enabled, since they’re supported only by a different class of language models (stanfordNLP’s Stanza) that is much slower than the rest. This parameter can be used to enable them.
Minimum number (or proportion) of texts to include a language in processing. Any texts in a language with fewer documents than these will be ignored. Can be useful to speed up processing when there is noise in the input languages, and when ignoring languages with a small number of documents only is acceptable. Values smaller than 1 will be interpreted as a proportion of all texts, and values greater than or equal to 1 as an absolute number of documents.
The language of inputs texts.
If all texts are in the same language, it can be specified here instead of passing it as an input column. The language will be used to identify the correct spaCy model to parse and analyze the texts. For allowed values, see the comment regarding the lang column above.