| `lang` | Pipeline language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `null`. ~~str~~ |
| `pipeline` | Names of pipeline components in order. Should correspond to sections in the `[components]` block, e.g. `[components.ner]`. See docs on [defining components](/usage/training#config-components). Defaults to `[]`. ~~List[str]~~ |
| `disabled` | Names of pipeline components that are loaded but disabled by default and not run as part of the pipeline. Should correspond to components listed in `pipeline`. After a pipeline is loaded, disabled components can be enabled using [`Language.enable_pipe`](/api/language#enable_pipe). ~~List[str]~~ |
| `before_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `Language` subclass before it's initialized. Defaults to `null`. ~~Optional[Callable[[Type[Language]], Type[Language]]]~~ |
| `after_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object right after it's initialized. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ |
| `after_pipeline_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object after the pipeline components have been added. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ |
| `tokenizer` | The tokenizer to use. Defaults to [`Tokenizer`](/api/tokenizer). ~~Callable[[str], Doc]~~ |
| `train` | Training data corpus, typically used in `[training]` block. ~~Callable[[Language], Iterator[Example]]~~ |
| `dev` | Development data corpus, typically used in `[training]` block. ~~Callable[[Language], Iterator[Example]]~~ |
| `pretrain` | Raw text for [pretraining](/usage/embeddings-transformers#pretraining), typically used in `[pretraining]` block (if available). ~~Callable[[Language], Iterator[Example]]~~ |
| ... | Any custom or alternative corpora. ~~Callable[[Language], Iterator[Example]]~~ |
Alternatively, the `[corpora]` block can refer to **one function** that returns
a dictionary keyed by the corpus names. This can be useful if you want to load a
single corpus once and then divide it up into `train` and `dev` partitions.
| `corpora` | A dictionary keyed by string names, mapped to corpus functions that receive the current `nlp` object and return an iterator of [`Example`](/api/example) objects. ~~Dict[str, Callable[[Language], Iterator[Example]]]~~ |
| `accumulate_gradient` | Whether to divide the batch up into substeps. Defaults to `1`. ~~int~~ |
| `batcher` | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. Defaults to [`batch_by_words`](/api/top-level#batch_by_words). ~~Callable[[Iterator[Doc], Iterator[List[Doc]]]]~~ |
| `before_to_disk` | Optional callback to modify `nlp` object right before it is saved to disk during and after training. Can be used to remove or reset config values or disable components. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ |
| `before_update`<Tagvariant="new">3.5</Tag> | Optional callback that is invoked at the start of each training step with the `nlp` object and a `Dict` containing the following entries: `step`, `epoch`. Can be used to make deferred changes to components. Defaults to `null`. ~~Optional[Callable[[Language, Dict[str, Any]], None]]~~ |
| `dev_corpus` | Dot notation of the config location defining the dev corpus. Defaults to `corpora.dev`. ~~str~~ |
| `dropout` | The dropout rate. Defaults to `0.1`. ~~float~~ |
| `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ |
| `frozen_components` | Pipeline component names that are "frozen" and shouldn't be initialized or updated during training. See [here](/usage/training#config-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `annotating_components`<Tagvariant="new">3.1</Tag> | Pipeline component names that should set annotations on the predicted docs during training. See [here](/usage/training#annotating-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `gpu_allocator` | Library for cupy to route GPU memory allocation to. Can be `"pytorch"` or `"tensorflow"`. Defaults to variable `${system.gpu_allocator}`. ~~str~~ |
| `logger` | Callable that takes the `nlp` and stdout and stderr `IO` objects, sets up the logger, and returns two new callables to log a training step and to finalize the logger. Defaults to [`ConsoleLogger`](/api/top-level#ConsoleLogger). ~~Callable[[Language, IO, IO], [Tuple[Callable[[Dict[str, Any]], None], Callable[[], None]]]]~~ |
| `max_epochs` | Maximum number of epochs to train for. `0` means an unlimited number of epochs. `-1` means that the train corpus should be streamed rather than loaded into memory with no shuffling within the training loop. Defaults to `0`. ~~int~~ |
| `max_steps` | Maximum number of update steps to train for. `0` means an unlimited number of steps. Defaults to `20000`. ~~int~~ |
| `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
| `patience` | How many steps to continue without improvement in evaluation score. `0` disables early stopping. Defaults to `1600`. ~~int~~ |
| `score_weights` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~ |
| `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ |
| `train_corpus` | Dot notation of the config location defining the train corpus. Defaults to `corpora.train`. ~~str~~ |
| `max_epochs` | Maximum number of epochs. Defaults to `1000`. ~~int~~ |
| `dropout` | The dropout rate. Defaults to `0.2`. ~~float~~ |
| `n_save_every` | Saving frequency. Defaults to `null`. ~~Optional[int]~~ |
| `objective` | The pretraining objective. Defaults to `{"type": "characters", "n_characters": 4}`. ~~Dict[str, Any]~~ |
| `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
| `corpus` | Dot notation of the config location defining the corpus with raw text. Defaults to `corpora.pretrain`. ~~str~~ |
| `batcher` | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. Defaults to [`batch_by_words`](/api/top-level#batch_by_words). ~~Callable[[Iterator[Doc], Iterator[List[Doc]]]]~~ |
| `component` | Component name to identify the layer with the model to pretrain. Defaults to `"tok2vec"`. ~~str~~ |
| `layer` | The specific layer of the model to pretrain. If empty, the whole model will be used. ~~str~~ |
| `components` | Additional arguments passed to the `initialize` method of a pipeline component, keyed by component name. If type annotations are available on the method, the config will be validated against them. The `initialize` methods will always receive the `get_examples` callback and the current `nlp` object. ~~Dict[str, Dict[str, Any]]~~ |
| `init_tok2vec` | Optional path to pretrained tok2vec weights created with [`spacy pretrain`](/api/cli#pretrain). Defaults to variable `${paths.init_tok2vec}`. Ignored when actually running pretraining, as you're creating the file to be used later. ~~Optional[str]~~ |
| `lookups` | Additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). Defaults to `null`. ~~Optional[Lookups]~~ |
| `tokenizer` | Additional arguments passed to the `initialize` method of the specified tokenizer. Can be used for languages like Chinese that depend on dictionaries or trained models for tokenization. If type annotations are available on the method, the config will be validated against them. The `initialize` method will always receive the `get_examples` callback and the current `nlp` object. ~~Dict[str, Any]~~ |
| `vectors` | Name or path of pipeline containing pretrained word vectors to use, e.g. created with [`init vectors`](/api/cli#init-vectors). Defaults to `null`. ~~Optional[str]~~ |
| `words` | List of gold-standard tokens. ~~List[str]~~ |
| `lemmas` | List of lemmas. ~~List[str]~~ |
| `spaces` | List of boolean values indicating whether the corresponding tokens is followed by a space or not. ~~List[bool]~~ |
| `tags` | List of fine-grained [POS tags](/usage/linguistic-features#pos-tagging). ~~List[str]~~ |
| `pos` | List of coarse-grained [POS tags](/usage/linguistic-features#pos-tagging). ~~List[str]~~ |
| `morphs` | List of [morphological features](/usage/linguistic-features#rule-based-morphology). ~~List[str]~~ |
| `sent_starts` | List of boolean values indicating whether each token is the first of a sentence or not. ~~List[bool]~~ |
| `deps` | List of string values indicating the [dependency relation](/usage/linguistic-features#dependency-parse) of a token to its head. ~~List[str]~~ |
| `heads` | List of integer values indicating the dependency head of each token, referring to the absolute index of each token in the text. ~~List[int]~~ |
| `entities` | **Option 1:** List of [BILUO tags](/usage/linguistic-features#accessing-ner) per token of the format `"{action}-{label}"`, or `None` for unannotated tokens. ~~List[str]~~ |
| `cats` | Dictionary of `label`/`value` pairs indicating how relevant a certain [text category](/api/textcategorizer) is for the text. ~~Dict[str, float]~~ |
| `links` | Dictionary of `offset`/`dict` pairs defining [named entity links](/usage/linguistic-features#entity-linking). The character offsets are linked to a dictionary of relevant knowledge base IDs. ~~Dict[Tuple[int, int], Dict]~~ |
| `lang` | Pipeline language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `"en"`. ~~str~~ |
| `name` | Pipeline name, e.g. `"core_web_sm"`. The final package name will be `{lang}_{name}`. Defaults to `"pipeline"`. ~~str~~ |
| `version` | Pipeline version. Will be used to version a Python package created with [`spacy package`](/api/cli#package). Defaults to `"0.0.0"`. ~~str~~ |
| `spacy_version` | spaCy version range the package is compatible with. Defaults to the spaCy version used to create the pipeline, up to next minor version, which is the default compatibility for the available [trained pipelines](/models). For instance, a pipeline trained with v3.0.0 will have the version range `">=3.0.0,<3.1.0"`. ~~str~~ |
| `parent_package` | Name of the spaCy package. Typically `"spacy"` or `"spacy_nightly"`. Defaults to `"spacy"`. ~~str~~ |
| `requirements` | Python package requirements that the pipeline depends on. Will be used for the Python package setup in [`spacy package`](/api/cli#package). Should be a list of package names with optional version specifiers, just like you'd define them in a `setup.cfg` or `requirements.txt`. Defaults to `[]`. ~~List[str]~~ |
| `description` | Pipeline description. Also used for Python package. Defaults to `""`. ~~str~~ |
| `author` | Pipeline author name. Also used for Python package. Defaults to `""`. ~~str~~ |
| `email` | Pipeline author email. Also used for Python package. Defaults to `""`. ~~str~~ |
| `url` | Pipeline author URL. Also used for Python package. Defaults to `""`. ~~str~~ |
| `license` | Pipeline license. Also used for Python package. Defaults to `""`. ~~str~~ |
| `sources` | Data sources used to train the pipeline. Typically a list of dicts with the keys `"name"`, `"url"`, `"author"` and `"license"`. [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `None`. ~~Optional[List[Dict[str, str]]]~~ |
| `vectors` | Information about the word vectors included with the pipeline. Typically a dict with the keys `"width"`, `"vectors"` (number of vectors), `"keys"` and `"name"`. ~~Dict[str, Any]~~ |
| `pipeline` | Names of pipeline component names, in order. Corresponds to [`nlp.pipe_names`](/api/language#pipe_names). Only exists for reference and is not used to create the components. This information is defined in the [`config.cfg`](/api/data-formats#config). Defaults to `[]`. ~~List[str]~~ |
| `labels` | Label schemes of the trained pipeline components, keyed by component name. Corresponds to [`nlp.pipe_labels`](/api/language#pipe_labels). [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `{}`. ~~Dict[str, Dict[str, List[str]]]~~ |
| `performance` | Training accuracy, added automatically by [`spacy train`](/api/cli#train). Dictionary of [score names](/usage/training#metrics) mapped to scores. Defaults to `{}`. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
| `speed` | Inference speed, added automatically by [`spacy train`](/api/cli#train). Typically a dictionary with the keys `"cpu"`, `"gpu"` and `"nwords"` (words per second). Defaults to `{}`. ~~Dict[str, Optional[Union[float, str]]]~~ |
| `spacy_git_version`<Tagvariant="new">3</Tag> | Git commit of [`spacy`](https://github.com/explosion/spaCy) used to create pipeline. ~~str~~ |
| other | Any other custom meta information you want to add. The data is preserved in [`nlp.meta`](/api/language#meta). ~~Any~~ |