Pooling Function

The Pooling Function type creates a pooling function to embed documents into a word space by using aggregators to combine the word embeddings. Conceptually, provided we have a representation of words, the function provides a way to represent a document by the combination of all the words it contains.


A new function of type pooling named <id> can be created as follows:

mldb.put("/v1/functions/"+<id>, {
    "type": "pooling",
    "params": {
        "aggregators": <ARRAY [ string ]>,
        "embeddingDataset": <SqlFromExpression>

with the following key-value definitions for params:

Field, Type, DefaultDescription

ARRAY [ string ]

Aggregator functions. Valid values are: avg, min, max, sum


Dataset containing the word embedding

The aggregators specifies the type of pooling that will be performed. To do average pooling, use the avg aggregator, etc.

Input and Output Values

Functions of this type have a single input value named words which is a row, and a single output value named embedding.


Suppose we have a word embedding represented by the following dataset called word_embedding:

rowName x y z
hello 0.2 0.95 0.4
friend 0.8 0.01 0.5
best 0.4 0.5 0.6

Also suppose we have the following bag of words in the bag_of_words dataset. This can be obtained by applying the tokenize function to any text field:

rowName hello friend best my
doc1 1 1
doc2 1 1 1 1

Let's configure a pooling function:

mldb.put("/v1/functions/pooler", {
    "type": "pooling",
    "params": {
        "aggregators": ["avg"],
        "embeddingDataset": "word_embedding"

We can now do the following call:

SELECT pooler({words: {*}})[embedding] as embed from bag_of_words
rowName embed.000000 embed.000001 embed.000002
doc1 0.5 0.48 0.45
doc2 0.466 0.516 0.5

See also

MLDB Embedding importers