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zX?dnidz1S*xh?pj5$gxl&tCZC_5=rL_L4w(DJmtc69KvE;#%qGdOzZ*!|3@)k?X)& zzM6^F#}o5e(F3N#h|UEme+9d$Tf5uASy5gR$p^_2X`X zIdM^(muEUF=a^AVqC$xm9YOm~c&46N2U|xstEw6L;Q?j{6XP+K4AOzvwM#HPa!jw> ZI-I7@p>Fa3XA - - -The Prodigy integration will require a nightly version of Prodigy that supports -spaCy v3+. You can already use annotations created with Prodigy in spaCy v3 by -exporting your data with -[`data-to-spacy`](https://prodi.gy/docs/recipes#data-to-spacy) and running -[`spacy convert`](/api/cli#convert) to convert it to the binary format. - - - [Prodigy](https://prodi.gy) is a modern annotation tool for creating training data for machine learning models, developed by us. It integrates with spaCy out-of-the-box and provides many different @@ -776,17 +766,23 @@ with and without a model in the loop. If Prodigy is installed in your project, you can start the annotation server from your `project.yml` for a tight feedback loop between data development and training. -The following example command starts the Prodigy app using the -[`ner.correct`](https://prodi.gy/docs/recipes#ner-correct) recipe and streams in -suggestions for the given entity labels produced by a pretrained model. You can -then correct the suggestions manually in the UI. After you save and exit the -server, the full dataset is exported in spaCy's format and split into a training -and evaluation set. + + +This integration requires [Prodigy v1.11](https://prodi.gy/docs/changelog#v1.11) +or higher. If you're using an older version of Prodigy, you can still use your +annotations in spaCy v3 by exporting your data with +[`data-to-spacy`](https://prodi.gy/docs/recipes#data-to-spacy) and running +[`spacy convert`](/api/cli#convert) to convert it to the binary format. + + + +The following example shows a workflow for merging and exporting NER annotations +collected with Prodigy and training a spaCy pipeline: > #### Example usage > > ```cli -> $ python -m spacy project run annotate +> $ python -m spacy project run all > ``` @@ -794,36 +790,71 @@ and evaluation set. ### project.yml vars: prodigy: - dataset: 'ner_articles' - labels: 'PERSON,ORG,PRODUCT' - model: 'en_core_web_md' + train_dataset: "fashion_brands_training" + eval_dataset: "fashion_brands_eval" + +workflows: + all: + - data-to-spacy + - train_spacy commands: - - name: annotate - - script: - - 'python -m prodigy ner.correct ${vars.prodigy.dataset} ${vars.prodigy.model} ./assets/raw_data.jsonl --labels ${vars.prodigy.labels}' - - 'python -m prodigy data-to-spacy ./corpus/train.json ./corpus/eval.json --ner ${vars.prodigy.dataset}' - - 'python -m spacy convert ./corpus/train.json ./corpus/train.spacy' - - 'python -m spacy convert ./corpus/eval.json ./corpus/eval.spacy' - - deps: - - 'assets/raw_data.jsonl' - - outputs: - - 'corpus/train.spacy' - - 'corpus/eval.spacy' + - name: "data-to-spacy" + help: "Merge your annotations and create data in spaCy's binary format" + script: + - "python -m prodigy data-to-spacy corpus/ --ner ${vars.prodigy.train_dataset},eval:${vars.prodigy.eval_dataset}" + outputs: + - "corpus/train.spacy" + - "corpus/dev.spacy" + - name: "train_spacy" + help: "Train a named entity recognition model with spaCy" + script: + - "python -m spacy train configs/config.cfg --output training/ --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy" + deps: + - "corpus/train.spacy" + - "corpus/dev.spacy" + outputs: + - "training/model-best" ``` -You can use the same approach for other types of projects and annotation +> #### Example train curve output +> +> [![Screenshot of train curve terminal output](../images/prodigy_train_curve.jpg)](https://prodi.gy/docs/recipes#train-curve) + +The [`train-curve`](https://prodi.gy/docs/recipes#train-curve) recipe is another +cool workflow you can include in your project. It will run the training with +different portions of the data, e.g. 25%, 50%, 75% and 100%. As a rule of thumb, +if accuracy increases in the last segment, this could indicate that collecting +more annotations of the same type might improve the model further. + + +```yaml +### project.yml (excerpt) +- name: "train_curve" + help: "Train the model with Prodigy by using different portions of training examples to evaluate if more annotations can potentially improve the performance" + script: + - "python -m prodigy train-curve --ner ${vars.prodigy.train_dataset},eval:${vars.prodigy.eval_dataset} --config configs/${vars.config} --show-plot" +``` + +You can use the same approach for various types of projects and annotation workflows, including -[text classification](https://prodi.gy/docs/recipes#textcat), -[dependency parsing](https://prodi.gy/docs/recipes#dep), +[named entity recognition](https://prodi.gy/docs/named-entity-recognition), +[span categorization](https://prodi.gy/docs/span-categorization), +[text classification](https://prodi.gy/docs/text-classification), +[dependency parsing](https://prodi.gy/docs/dependencies-relations), [part-of-speech tagging](https://prodi.gy/docs/recipes#pos) or fully -[custom recipes](https://prodi.gy/docs/custom-recipes) – for instance, an A/B -evaluation workflow that lets you compare two different models and their -results. +[custom recipes](https://prodi.gy/docs/custom-recipes). You can also use spaCy +project templates to quickly start the annotation server to collect more +annotations and add them to your Prodigy dataset. - +Get started with spaCy and Prodigy using our project template. It includes +commands to create a merged training corpus from your Prodigy annotations, +training and packaging a spaCy pipeline and analyzing if more annotations may +improve performance. + + ---