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rename "custom models" to "custom functions"
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svlandeg
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@ -13,7 +13,7 @@ menu:
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TODO: intro and how architectures work, link to
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[`registry`](/api/top-level#registry),
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[custom models](/usage/training#custom-models) usage etc.
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[custom functions](/usage/training#custom-functions) usage etc.
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## Tok2Vec architectures {#tok2vec-arch source="spacy/ml/models/tok2vec.py"}
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@ -276,7 +276,7 @@ python -m spacy init fill-config tmp/starter-config_invalid.cfg --base tmp/start
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| Name | Description |
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| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
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| `--code_path`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--code_path`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
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| **PRINTS** | Config validation errors, if available. |
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@ -448,7 +448,7 @@ will not be available.
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| Name | Description |
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| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--ignore-warnings`, `-IW` | Ignore warnings, only show stats and errors. ~~bool (flag)~~ |
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| `--verbose`, `-V` | Print additional information and explanations. ~~bool (flag)~~ |
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| `--no-format`, `-NF` | Don't pretty-print the results. Use this if you want to write to a file. ~~bool (flag)~~ |
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@ -612,9 +612,9 @@ Train a model. Expects data in spaCy's
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Will save out the best model from all epochs, as well as the final model. The
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`--code` argument can be used to provide a Python file that's imported before
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the training process starts. This lets you register
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[custom functions](/usage/training#custom-models) and architectures and refer to
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them in your config, all while still using spaCy's built-in `train` workflow. If
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you need to manage complex multi-step training workflows, check out the new
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[custom functions](/usage/training#custom-functions) and architectures and refer
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to them in your config, all while still using spaCy's built-in `train` workflow.
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If you need to manage complex multi-step training workflows, check out the new
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[spaCy projects](/usage/projects).
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<Infobox title="New in v3.0" variant="warning">
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@ -636,7 +636,7 @@ $ python -m spacy train [config_path] [--output] [--code] [--verbose] [overrides
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| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
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| `--output`, `-o` | Directory to store model in. Will be created if it doesn't exist. ~~Optional[Path] \(positional)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--verbose`, `-V` | Show more detailed messages during training. ~~bool (flag)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
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@ -674,7 +674,7 @@ $ python -m spacy pretrain [texts_loc] [output_dir] [config_path] [--code] [--re
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| `texts_loc` | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `"tokens"`. [See here](/api/data-formats#pretrain) for details. ~~Path (positional)~~ |
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| `output_dir` | Directory to write models to on each epoch. ~~Path (positional)~~ |
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| `config_path` | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
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| `--resume-path`, `-r` | Path to pretrained weights from which to resume pretraining. ~~Optional[Path] \(option)~~ |
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| `--epoch-resume`, `-er` | The epoch to resume counting from when using `--resume-path`. Prevents unintended overwriting of existing weight files. ~~Optional[int] \(option)~~ |
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| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
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@ -295,23 +295,23 @@ factories.
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> return Model("custom", forward, dims={"nO": nO})
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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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| `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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| `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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| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
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| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
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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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| `assets` | Registry for data assets, knowledge bases etc. |
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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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| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
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| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
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| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
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| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
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| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
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| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
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| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
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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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| `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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| `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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| `languages` | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points). |
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| `lookups` | Registry for large lookup tables available via `vocab.lookups`. |
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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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| `assets` | Registry for data assets, knowledge bases etc. |
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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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| `readers` | Registry for training and evaluation data readers like [`Corpus`](/api/corpus). |
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| `batchers` | Registry for training and evaluation [data batchers](#batchers). |
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| `optimizers` | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers). |
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| `schedules` | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules). |
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| `layers` | Registry for functions that create [layers](https://thinc.ai/docs/api-layers). |
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| `losses` | Registry for functions that create [losses](https://thinc.ai/docs/api-loss). |
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| `initializers` | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers). |
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### spacy-transformers registry {#registry-transformers}
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@ -5,7 +5,7 @@ menu:
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- ['Introduction', 'basics']
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- ['Quickstart', 'quickstart']
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- ['Config System', 'config']
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- ['Custom Models', 'custom-models']
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- ['Custom Functions', 'custom-functions']
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- ['Transfer Learning', 'transfer-learning']
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- ['Parallel Training', 'parallel-training']
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- ['Internal API', 'api']
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@ -127,7 +127,7 @@ Some of the main advantages and features of spaCy's training config are:
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[optimizers](https://thinc.ai/docs/api-optimizers) or
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[schedules](https://thinc.ai/docs/api-schedules) and define arguments that are
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passed into them. You can also register your own functions to define
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[custom architectures](#custom-models), reference them in your config and
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[custom architectures](#custom-functions), reference them in your config and
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tweak their parameters.
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- **Interpolation.** If you have hyperparameters or other settings used by
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multiple components, define them once and reference them as
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@ -299,7 +299,7 @@ case [`compounding.v1`](https://thinc.ai/docs/api-schedules#compounding) defined
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in the [function registry](/api/top-level#registry). All other values defined in
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the block are passed to the function as keyword arguments when it's initialized.
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You can also use this mechanism to register
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[custom implementations and architectures](#custom-models) and reference them
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[custom implementations and architectures](#custom-functions) and reference them
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from your configs.
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> #### How the config is resolved
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@ -481,7 +481,7 @@ still look good.
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</Accordion>
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## Custom model implementations and architectures {#custom-models}
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## Custom model implementations and architectures {#custom-functions}
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<!-- TODO: intro, should summarise what spaCy v3 can do and that you can now use fully custom implementations, models defined in PyTorch and TF, etc. etc. -->
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