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Update docs [ci skip]
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@ -357,8 +357,8 @@ are returned: one for logging the information for each training step, and a
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second function that is called to finalize the logging when the training is
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finished. To log each training step, a
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[dictionary](/usage/training#custom-logging) is passed on from the
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[training script](/api/cli#train), including information such as the training
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loss and the accuracy scores on the development set.
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[`spacy train`](/api/cli#train), including information such as the training loss
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and the accuracy scores on the development set.
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There are two built-in logging functions: a logger printing results to the
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console in tabular format (which is the default), and one that also sends the
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@ -366,6 +366,8 @@ results to a [Weights & Biases](https://www.wandb.com/) dashboard. Instead of
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using one of the built-in loggers listed here, you can also
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[implement your own](/usage/training#custom-logging).
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#### spacy.ConsoleLogger.v1 {#ConsoleLogger tag="registered function"}
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> #### Example config
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>
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> ```ini
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@ -373,19 +375,21 @@ using one of the built-in loggers listed here, you can also
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> @loggers = "spacy.ConsoleLogger.v1"
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> ```
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#### spacy.ConsoleLogger.v1 {#ConsoleLogger tag="registered function"}
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Writes the results of a training step to the console in a tabular format.
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<Accordion title="Example console output">
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<Accordion title="Example console output" spaced>
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```cli
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$ python -m spacy train config.cfg
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```
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```
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$ python -m spacy train config.cfg
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ℹ Using CPU
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ℹ Loading config and nlp from: config.cfg
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ℹ Pipeline: ['tok2vec', 'tagger']
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ℹ Start training
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ℹ Training. Initial learn rate: 0.0
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E # LOSS TOK2VEC LOSS TAGGER TAG_ACC SCORE
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--- ------ ------------ ----------- ------- ------
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1 0 0.00 86.20 0.22 0.00
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