spacy-report

The goal of this project is to generate reports for spaCy models.

what it does

The goal of spacy-report is to offer static reports for spaCy models that help users make better decisions on how the models can be used. At the moment the project supports interactive views for threshold values for classification.

Here's a preview of what to expect:

There are two kinds of charts.

1. The first kind is a density chart. This chart shows the distribution of confidence scores for a given class. The blue area represents documents that had the tag assigned to the class. The orange area represents documents that didn't.
2. The second kind is a line chart that demonstrates the accuracy, precision and recall values for a given confidence threshold. It's an interactive chart and you can explore the values by hovering over the chart.

install

You can install spacy_report directly with pip.

python -m pip install spacy_report

Alternatively, you can also install the most version from git.

python -m pip install "spacy_report @ git+https://github.com/koaning/spacy_report.git"

usage

The accuracy project provides a command line interface that can generate reports. The full CLI can also be explored via the --help flag.

> python -m spacy report --help
Usage: python -m spacy report [OPTIONS] COMMAND [ARGS]...

  Generate reports for spaCy models.

Options:
  --help  Show this message and exit.

Commands:
  textcat  Generate a report for textcat models.
  version  Print the version of spacy_report.

textcat report

To generate reports for textcat models, you can use the textcat sub-command.

> python -m spacy report textcat training/model-best/ corpus/train.spacy corpus/dev.spacy

Loading model at training/model-best
Running model on training data...
Running model on development data...
Generating Charts ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Done! You can view the report via;

python -m http.server --directory reports PORT 

This will generate a folder, "reports" by default, that contains a full dashboard for the trained spaCy model found in training/model-best.

The CLI has a few configurable settings:

Arguments:
  [MODEL_PATH]  Path to spaCy model
  [TRAIN_PATH]  Path to training data
  [DEV_PATH]    Path to development data
  [FOLDER_OUT]  Output folder for reports  [default: reports]

Options:
  --classes TEXT  Comma-separated string of classes to use
  --help          Show this message and exit.