Trainable classifier

This component predicts a label for each box over the whole document using machine learning.

Note

You must train the model your model to use this classifier. See Model training for more information

Examples

The classifier is composed of the following blocks:

• a configurable embedding layer
• a linear classification layer

In this example, we use a simple CNN-based embedding layer (sub-box-cnn-pooler), which applies a stack of CNN layers to the embeddings computed by a text embedding layer (simple-text-embedding).

API-basedConfiguration-based

pipeline.add_pipe(
    "trainable-classifier",
    name="classifier",
    config={
        # simple embedding computed by pooling embeddings of words in each box
        "embedding": {
            "@factory": "sub-box-cnn-pooler",
            "out_channels": 64,
            "kernel_sizes": (3, 4, 5),
            "embedding": {
                "@factory": "simple-text-embedding",
                "size": 72,
            },
        },
        "labels": ["body", "pollution"],
    },
)

[components.classifier]
@factory = "trainable-classifier"
labels = ["body", "pollution"]

[components.classifier.embedding]
@factory = "sub-box-cnn-pooler"
out_channels = 64
kernel_sizes = (3, 4, 5)

[components.classifier.embedding.embedding]
@factory = "simple-text-embedding"
size = 72

Parameters

PARAMETER	DESCRIPTION
labels	Initial labels of the classifier (will be completed during initialization) TYPE: Sequence[str] DEFAULT: ('pollution')
embedding	Embedding module to encode the PDF boxes TYPE: TrainablePipe[EmbeddingOutput]

---