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add loggers registry & logger docs sections
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@ -272,7 +272,7 @@ def train_while_improving(
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step (int): How many steps have been completed.
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score (float): The main score form the last evaluation.
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other_scores: : The other scores from the last evaluation.
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loss: The accumulated losses throughout training.
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losses: The accumulated losses throughout training.
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checkpoints: A list of previous results, where each result is a
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(score, step, epoch) tuple.
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"""
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@ -296,24 +296,25 @@ factories.
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> i += 1
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> ```
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| Registry name | Description |
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| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `architectures` | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`. |
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| `assets` | Registry for data assets, knowledge bases etc. |
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| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
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| `callbacks` | Registry for custom callbacks to [modify the `nlp` object](/usage/training#custom-code-nlp-callbacks) before training. |
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| `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points). |
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| `factories` | Registry for functions that create [pipeline components](/usage/processing-pipelines#custom-components). Added automatically when you use the `@spacy.component` decorator and also reads from [entry points](/usage/saving-loading#entry-points). |
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| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
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| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
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| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
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| `loggers` | Registry for functions that log [training results](/usage/training). |
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| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
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| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
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| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
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| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
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| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
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| `tokenizers` | Registry for tokenizer factories. Registered functions should return a callback that receives the `nlp` object and returns a [`Tokenizer`](/api/tokenizer) or a custom callable. |
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| Registry name | Description |
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| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `annotation_setters` | Registry for functions that store Tok2Vec annotations on `Doc` objects. |
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| `architectures` | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`. |
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| `assets` | Registry for data assets, knowledge bases etc. |
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| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
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| `callbacks` | Registry for custom callbacks to [modify the `nlp` object](/usage/training#custom-code-nlp-callbacks) before training. |
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| `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points). |
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| `factories` | Registry for functions that create [pipeline components](/usage/processing-pipelines#custom-components). Added automatically when you use the `@spacy.component` decorator and also reads from [entry points](/usage/saving-loading#entry-points). |
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| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
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| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
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| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
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| `loggers` | Registry for functions that log [training results](/usage/training). |
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| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
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| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
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| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
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| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
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| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
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| `tokenizers` | Registry for tokenizer factories. Registered functions should return a callback that receives the `nlp` object and returns a [`Tokenizer`](/api/tokenizer) or a custom callable. |
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### spacy-transformers registry {#registry-transformers}
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@ -327,18 +328,69 @@ See the [`Transformer`](/api/transformer) API reference and
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> ```python
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> import spacy_transformers
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>
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> @spacy_transformers.registry.annotation_setters("my_annotation_setter.v1")
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> def configure_custom_annotation_setter():
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> def annotation_setter(docs, trf_data) -> None:
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> # Set annotations on the docs
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> @spacy_transformers.registry.span_getters("my_span_getter.v1")
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> def configure_custom_span_getter() -> Callable:
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> def span_getter(docs: List[Doc]) -> List[List[Span]]:
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> # Transform each Doc into a List of Span objects
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>
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> return annotation_sette
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> return span_getter
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> ```
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| Registry name | Description |
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| ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`span_getters`](/api/transformer#span_getters) | Registry for functions that take a batch of `Doc` objects and return a list of `Span` objects to process by the transformer, e.g. sentences. |
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| [`annotation_setters`](/api/transformer#annotation_setters) | Registry for functions that create annotation setters. Annotation setters are functions that take a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. |
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| Registry name | Description |
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| ----------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`span_getters`](/api/transformer#span_getters) | Registry for functions that take a batch of `Doc` objects and return a list of `Span` objects to process by the transformer, e.g. sentences. |
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## Loggers {#loggers source="spacy/gold/loggers.py" new="3"}
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A logger records the training results for each step. When a logger is created,
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it returns a `log_step` function and a `finalize` function. The `log_step`
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function is called by the [training script](/api/cli#train) and receives a
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dictionary of information, including
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# TODO
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> #### Example config
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>
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> ```ini
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> [training.logger]
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> @loggers = "spacy.ConsoleLogger.v1"
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> ```
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Instead of using one of the built-in batchers listed here, you can also
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[implement your own](/usage/training#custom-code-readers-batchers), which may or
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may not use a custom schedule.
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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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#### spacy.WandbLogger.v1 {#WandbLogger tag="registered function"}
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> #### Installation
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>
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> ```bash
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> $ pip install wandb
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> $ wandb login
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> ```
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Built-in logger that sends the results of each training step to the dashboard of
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the [Weights & Biases`](https://www.wandb.com/) dashboard. To use this logger,
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Weights & Biases should be installed, and you should be logged in. The logger
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will send the full config file to W&B, as well as various system information
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such as GPU
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| `project_name` | The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet. ~~str~~ |
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> #### Example config
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>
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> ```ini
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> [training.logger]
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> @loggers = "spacy.WandbLogger.v1"
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> project_name = "monitor_spacy_training"
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> ```
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## Batchers {#batchers source="spacy/gold/batchers.py" new="3"}
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@ -605,6 +605,24 @@ to your Python file. Before loading the config, spaCy will import the
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$ python -m spacy train config.cfg --output ./output --code ./functions.py
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```
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#### Example: Custom logging function {#custom-logging}
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During training, the results of each step are passed to a logger function in a
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dictionary providing the following information:
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| Key | Value |
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| -------------- | ---------------------------------------------------------------------------------------------- |
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| `epoch` | How many passes over the data have been completed. ~~int~~ |
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| `step` | How many steps have been completed. ~~int~~ |
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| `score` | The main score form the last evaluation, measured on the dev set. ~~float~~ |
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| `other_scores` | The other scores from the last evaluation, measured on the dev set. ~~Dict[str, Any]~~ |
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| `losses` | The accumulated training losses. ~~Dict[str, float]~~ |
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| `checkpoints` | A list of previous results, where each result is a (score, step, epoch) tuple. ~~List[Tuple]~~ |
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By default, these results are written to the console with the [`ConsoleLogger`](/api/top-level#ConsoleLogger)
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# TODO
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#### Example: Custom batch size schedule {#custom-code-schedule}
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For example, let's say you've implemented your own batch size schedule to use
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