spaCy/extra/example_data/textcat_example_data/README.md

35 lines
1.0 KiB
Markdown
Raw Normal View History

Add textcat to train CLI (#4226) * Add doc.cats to spacy.gold at the paragraph level Support `doc.cats` as `"cats": [{"label": string, "value": number}]` in the spacy JSON training format at the paragraph level. * `spacy.gold.docs_to_json()` writes `docs.cats` * `GoldCorpus` reads in cats in each `GoldParse` * Update instances of gold_tuples to handle cats Update iteration over gold_tuples / gold_parses to handle addition of cats at the paragraph level. * Add textcat to train CLI * Add textcat options to train CLI * Add textcat labels in `TextCategorizer.begin_training()` * Add textcat evaluation to `Scorer`: * For binary exclusive classes with provided label: F1 for label * For 2+ exclusive classes: F1 macro average * For multilabel (not exclusive): ROC AUC macro average (currently relying on sklearn) * Provide user info on textcat evaluation settings, potential incompatibilities * Provide pipeline to Scorer in `Language.evaluate` for textcat config * Customize train CLI output to include only metrics relevant to current pipeline * Add textcat evaluation to evaluate CLI * Fix handling of unset arguments and config params Fix handling of unset arguments and model confiug parameters in Scorer initialization. * Temporarily add sklearn requirement * Remove sklearn version number * Improve Scorer handling of models without textcats * Fixing Scorer handling of models without textcats * Update Scorer output for python 2.7 * Modify inf in Scorer for python 2.7 * Auto-format Also make small adjustments to make auto-formatting with black easier and produce nicer results * Move error message to Errors * Update documentation * Add cats to annotation JSON format [ci skip] * Fix tpl flag and docs [ci skip] * Switch to internal roc_auc_score Switch to internal `roc_auc_score()` adapted from scikit-learn. * Add AUCROCScore tests and improve errors/warnings * Add tests for AUCROCScore and roc_auc_score * Add missing error for only positive/negative values * Remove unnecessary warnings and errors * Make reduced roc_auc_score functions private Because most of the checks and warnings have been stripped for the internal functions and access is only intended through `ROCAUCScore`, make the functions for roc_auc_score adapted from scikit-learn private. * Check that data corresponds with multilabel flag Check that the training instances correspond with the multilabel flag, adding the multilabel flag if required. * Add textcat score to early stopping check * Add more checks to debug-data for textcat * Add example training data for textcat * Add more checks to textcat train CLI * Check configuration when extending base model * Fix typos * Update textcat example data * Provide licensing details and licenses for data * Remove two labels with no positive instances from jigsaw-toxic-comment data. Co-authored-by: Ines Montani <ines@ines.io>
2019-09-15 20:31:31 +00:00
## Examples of textcat training data
spacy JSON training files were generated from JSONL with:
```
python textcatjsonl_to_trainjson.py -m en file.jsonl .
```
`cooking.json` is an example with mutually-exclusive classes with two labels:
* `baking`
* `not_baking`
`jigsaw-toxic-comment.json` is an example with multiple labels per instance:
* `insult`
* `obscene`
* `severe_toxic`
* `toxic`
### Data Sources
* `cooking.jsonl`: https://cooking.stackexchange.com. The meta IDs link to the
original question as `https://cooking.stackexchange.com/questions/ID`, e.g.,
`https://cooking.stackexchange.com/questions/2` for the first instance.
* `jigsaw-toxic-comment.jsonl`: [Jigsaw Toxic Comments Classification
Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)
### Data Licenses
* `cooking.jsonl`: CC BY-SA 4.0 ([`CC_BY-SA-4.0.txt`](CC_BY-SA-4.0.txt))
* `jigsaw-toxic-comment.jsonl`:
* text: CC BY-SA 3.0 ([`CC_BY-SA-3.0.txt`](CC_BY-SA-3.0.txt))
* annotation: CC0 ([`CC0.txt`](CC0.txt))