spaCy/website/docs/api/data-formats.md

title	teaser	menu
Data formats	Details on spaCy's input and output data formats	Training Config config Training Data training Pretraining Data pretraining Vocabulary vocab-jsonl Model Meta meta

This section documents input and output formats of data used by spaCy, including the training config, training data and lexical vocabulary data. For an overview of label schemes used by the models, see the models directory. Each model documents the label schemes used in its components, depending on the data it was trained on.

Training config

Config files define the training process and model pipeline and can be passed to spacy train. They use Thinc's configuration system under the hood. For details on how to use training configs, see the usage documentation. To get started with the recommended settings for your use case, check out the quickstart widget or run the init config command.

What does the @ mean?

The @ syntax lets you refer to function names registered in the function registry. For example, @architectures = "spacy.HashEmbedCNN.v1" refers to a registered function of the name spacy.HashEmbedCNN.v1 and all other values defined in its block will be passed into that function as arguments. Those arguments depend on the registered function. See the usage guide on registered functions for details.

https://github.com/explosion/spaCy/blob/develop/spacy/default_config.cfg

Under the hood, spaCy's configs are powered by our machine learning library Thinc's config system, which uses pydantic for data validation based on type hints. See spacy/schemas.py for the schemas used to validate the default config. Arguments of registered functions are validated against their type annotations, if available. To debug your config and check that it's valid, you can run the spacy debug config command.

nlp

Example

[nlp]
lang = "en"
pipeline = ["tagger", "parser", "ner"]
load_vocab_data = true
before_creation = null
after_creation = null
after_pipeline_creation = null

[nlp.tokenizer]
@tokenizers = "spacy.Tokenizer.v1"

Defines the nlp object, its tokenizer and processing pipeline component names.

Name	Description
lang	Model language ISO code. 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. Defaults to []. List[str]
load_vocab_data	Whether to load additional lexeme and vocab data from spacy-lookups-data if available. Defaults to true. bool
before_creation	Optional callback to modify Language subclass before it's initialized. Defaults to null. Optional[CallableType[Language, Type[Language]]]
after_creation	Optional callback to modify nlp object right after it's initialized. Defaults to null. Optional[CallableLanguage], Language
after_pipeline_creation	Optional callback to modify nlp object after the pipeline components have been added. Defaults to null. Optional[CallableLanguage], Language
tokenizer	The tokenizer to use. Defaults to Tokenizer. Callable[[str], Doc]

components

Example

[components.textcat]
factory = "textcat"
labels = ["POSITIVE", "NEGATIVE"]

[components.textcat.model]
@architectures = "spacy.TextCatBOW.v1"
exclusive_classes = false
ngram_size = 1
no_output_layer = false

This section includes definitions of the pipeline components and their models, if available. Components in this section can be referenced in the pipeline of the [nlp] block. Component blocks need to specify either a factory (named function to use to create component) or a source (name of path of pretrained model to copy components from). See the docs on defining pipeline components for details.

paths, system

These sections define variables that can be referenced across the other sections as variables. For example ${paths.train} uses the value of train defined in the block [paths]. If your config includes custom registered functions that need paths, you can define them here. All config values can also be overwritten on the CLI when you run spacy train, which is especially relevant for data paths that you don't want to hard-code in your config file.

$ python -m spacy train config.cfg --paths.train ./corpus/train.spacy

training

This section defines settings and controls for the training and evaluation process that are used when you run spacy train.

Name	Description
seed	The random seed. Defaults to variable ${system.seed}. int
dropout	The dropout rate. Defaults to 0.1. float
accumulate_gradient	Whether to divide the batch up into substeps. Defaults to 1. int
init_tok2vec	Optional path to pretrained tok2vec weights created with spacy pretrain. Defaults to variable ${paths.init_tok2vec}. Optional[str]
raw_text	Optional path to a jsonl file with unlabelled text documents for a rehearsal step. Defaults to variable ${paths.raw}. Optional[str]
vectors	Model name or path to model containing pretrained word vectors to use, e.g. created with init model. Defaults to null. Optional[str]
patience	How many steps to continue without improvement in evaluation score. Defaults to 1600. int
max_epochs	Maximum number of epochs to train for. Defaults to 0. int
max_steps	Maximum number of update steps to train for. Defaults to 20000. int
eval_frequency	How often to evaluate during training (steps). Defaults to 200. int
score_weights	Score names shown in metrics mapped to their weight towards the final weighted score. See here for details. Defaults to {}. Dict[str, float]
frozen_components	Pipeline component names that are "frozen" and shouldn't be updated during training. See here for details. Defaults to []. List[str]
train_corpus	Callable that takes the current nlp object and yields Example objects. Defaults to Corpus. CallableLanguage], Iterator[Example
dev_corpus	Callable that takes the current nlp object and yields Example objects. Defaults to Corpus. CallableLanguage], Iterator[Example
batcher	Callable that takes an iterator of Doc objects and yields batches of Docs. Defaults to batch_by_words. CallableIterator[Doc], Iterator[List[Doc]]
optimizer	The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to Adam. Optimizer

pretraining

This section is optional and defines settings and controls for language model pretraining. It's used when you run spacy pretrain.

Name	Description
max_epochs	Maximum number of epochs. Defaults to 1000. int
min_length	Minimum length of examples. Defaults to 5. int
max_length	Maximum length of examples. Defaults to 500. int
dropout	The dropout rate. Defaults to 0.2. float
n_save_every	Saving frequency. Defaults to null. Optional[int]
batch_size	The batch size or batch size schedule. Defaults to 3000. Union[int, Sequence[int]]
seed	The random seed. Defaults to variable ${system.seed}. int
use_pytorch_for_gpu_memory	Allocate memory via PyTorch. Defaults to variable ${system.use_pytorch_for_gpu_memory}. bool
tok2vec_model	The model section of the embedding component in the config. Defaults to "components.tok2vec.model". str
objective	The pretraining objective. Defaults to {"type": "characters", "n_characters": 4}. Dict[str, Any]
optimizer	The optimizer. Defaults to Adam. Optimizer

Training data

Binary training format

Example

from spacy.tokens import DocBin
from spacy.gold import Corpus

doc_bin = DocBin(docs=docs)
doc_bin.to_disk("./data.spacy")
reader = Corpus("./data.spacy")

The main data format used in spaCy v3.0 is a binary format created by serializing a DocBin, which represents a collection of Doc objects. This means that you can train spaCy models using the same format it outputs: annotated Doc objects. The binary format is extremely efficient in storage, especially when packing multiple documents together.

Typically, the extension for these binary files is .spacy, and they are used as input format for specifying a training corpus and for spaCy's CLI train command. The built-in convert command helps you convert spaCy's previous JSON format to the new binary format format. It also supports conversion of the .conllu format used by the Universal Dependencies corpora.

JSON training format

As of v3.0, the JSON input format is deprecated and is replaced by the binary format. Instead of converting Doc objects to JSON, you can now serialize them directly using the DocBin container and then use them as input data.

spacy convert lets you convert your JSON data to the new .spacy format:

$ python -m spacy convert ./data.json ./output.spacy

Annotating entities

Named entities are provided in the BILUO notation. Tokens outside an entity are set to "O" and tokens that are part of an entity are set to the entity label, prefixed by the BILUO marker. For example "B-ORG" describes the first token of a multi-token ORG entity and "U-PERSON" a single token representing a PERSON entity. The biluo_tags_from_offsets function can help you convert entity offsets to the right format.

### Example structure
[{
    "id": int,                      # ID of the document within the corpus
    "paragraphs": [{                # list of paragraphs in the corpus
        "raw": string,              # raw text of the paragraph
        "sentences": [{             # list of sentences in the paragraph
            "tokens": [{            # list of tokens in the sentence
                "id": int,          # index of the token in the document
                "dep": string,      # dependency label
                "head": int,        # offset of token head relative to token index
                "tag": string,      # part-of-speech tag
                "orth": string,     # verbatim text of the token
                "ner": string       # BILUO label, e.g. "O" or "B-ORG"
            }],
            "brackets": [{          # phrase structure (NOT USED by current models)
                "first": int,       # index of first token
                "last": int,        # index of last token
                "label": string     # phrase label
            }]
        }],
        "cats": [{                  # new in v2.2: categories for text classifier
            "label": string,        # text category label
            "value": float / bool   # label applies (1.0/true) or not (0.0/false)
        }]
    }]
}]

Here's an example of dependencies, part-of-speech tags and names entities, taken from the English Wall Street Journal portion of the Penn Treebank:

https://github.com/explosion/spaCy/blob/v2.3.x/examples/training/training-data.json

Annotation format for creating training examples

An Example object holds the information for one training instance. It stores two Doc objects: one for holding the gold-standard reference data, and one for holding the predictions of the pipeline. Examples can be created using the Example.from_dict method with a reference Doc and a dictionary of gold-standard annotations.

Example

example = Example.from_dict(doc, gold_dict)

Example objects are used as part of the internal training API and they're expected when you call nlp.update. However, for most use cases, you shouldn't have to write your own training scripts. It's recommended to train your models via the spacy train command with a config file to keep track of your settings and hyperparameters and your own registered functions to customize the setup.

Example

{
   "text": str,
   "words": List[str],
   "lemmas": List[str],
   "spaces": List[bool],
   "tags": List[str],
   "pos": List[str],
   "morphs": List[str],
   "sent_starts": List[bool],
   "deps": List[string],
   "heads": List[int],
   "entities": List[str],
   "entities": List[(int, int, str)],
   "cats": Dict[str, float],
   "links": Dict[(int, int), dict],
}

Name	Description
text	Raw text. 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. List[str]
pos	List of coarse-grained POS tags. List[str]
morphs	List of morphological features. 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 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 per token of the format "{action}-{label}", or None for unannotated tokens. List[str]
entities	Option 2: List of "(start, end, label)" tuples defining all entities in the text. List[Tuple[int, int, str]]
cats	Dictionary of label/value pairs indicating how relevant a certain text category is for the text. Dict[str, float]
links	Dictionary of offset/dict pairs defining named entity links. The character offsets are linked to a dictionary of relevant knowledge base IDs. Dict[Tuple[int, int], Dict]

• Multiple formats are possible for the "entities" entry, but you have to pick one.
• Any values for sentence starts will be ignored if there are annotations for dependency relations.
• If the dictionary contains values for "text" and "words", but not "spaces", the latter are inferred automatically. If "words" is not provided either, the values are inferred from the Doc argument.

### Examples
# Training data for a part-of-speech tagger
doc = Doc(vocab, words=["I", "like", "stuff"])
gold_dict = {"tags": ["NOUN", "VERB", "NOUN"]}
example = Example.from_dict(doc, gold_dict)

# Training data for an entity recognizer (option 1)
doc = nlp("Laura flew to Silicon Valley.")
gold_dict = {"entities": ["U-PERS", "O", "O", "B-LOC", "L-LOC"]}
example = Example.from_dict(doc, gold_dict)

# Training data for an entity recognizer (option 2)
doc = nlp("Laura flew to Silicon Valley.")
gold_dict = {"entities": [(0, 5, "PERSON"), (14, 28, "LOC")]}
example = Example.from_dict(doc, gold_dict)

# Training data for text categorization
doc = nlp("I'm pretty happy about that!")
gold_dict = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
example = Example.from_dict(doc, gold_dict)

# Training data for an Entity Linking component
doc = nlp("Russ Cochran his reprints include EC Comics.")
gold_dict = {"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}}
example = Example.from_dict(doc, gold_dict)

Pretraining data

The spacy pretrain command lets you pretrain the "token-to-vector" embedding layer of pipeline components from raw text. Raw text can be provided as a .jsonl (newline-delimited JSON) file containing one input text per line (roughly paragraph length is good). Optionally, custom tokenization can be provided.

Tip: Writing JSONL

Our utility library srsly provides a handy write_jsonl helper that takes a file path and list of dictionaries and writes out JSONL-formatted data.

import srsly
data = [{"text": "Some text"}, {"text": "More..."}]
srsly.write_jsonl("/path/to/text.jsonl", data)

Key	Description
text	The raw input text. Is not required if tokens is available. str
tokens	Optional tokenization, one string per token. List[str]

### Example
{"text": "Can I ask where you work now and what you do, and if you enjoy it?"}
{"text": "They may just pull out of the Seattle market completely, at least until they have autonomous vehicles."}
{"text": "My cynical view on this is that it will never be free to the public. Reason: what would be the draw of joining the military? Right now their selling point is free Healthcare and Education. Ironically both are run horribly and most, that I've talked to, come out wishing they never went in."}
{"tokens": ["If", "tokens", "are", "provided", "then", "we", "can", "skip", "the", "raw", "input", "text"]}

Lexical data for vocabulary

To populate a model's vocabulary, you can use the spacy init model command and load in a newline-delimited JSON (JSONL) file containing one lexical entry per line via the --jsonl-loc option. The first line defines the language and vocabulary settings. All other lines are expected to be JSON objects describing an individual lexeme. The lexical attributes will be then set as attributes on spaCy's Lexeme object. The vocab command outputs a ready-to-use spaCy model with a Vocab containing the lexical data.

### First line
{"lang": "en", "settings": {"oov_prob": -20.502029418945312}}

### Entry structure
{
    "orth": string,     # the word text
    "id": int,          # can correspond to row in vectors table
    "lower": string,
    "norm": string,
    "shape": string
    "prefix": string,
    "suffix": string,
    "length": int,
    "cluster": string,
    "prob": float,
    "is_alpha": bool,
    "is_ascii": bool,
    "is_digit": bool,
    "is_lower": bool,
    "is_punct": bool,
    "is_space": bool,
    "is_title": bool,
    "is_upper": bool,
    "like_url": bool,
    "like_num": bool,
    "like_email": bool,
    "is_stop": bool,
    "is_oov": bool,
    "is_quote": bool,
    "is_left_punct": bool,
    "is_right_punct": bool
}

Here's an example of the 20 most frequent lexemes in the English training data:

https://github.com/explosion/spaCy/tree/master/examples/training/vocab-data.jsonl

Model meta

The model meta is available as the file meta.json and exported automatically when you save an nlp object to disk. Its contents are available as nlp.meta.

As of spaCy v3.0, the meta.json isn't used to construct the language class and pipeline anymore and only contains meta information for reference and for creating a Python package with spacy package. How to set up the nlp object is now defined in the config.cfg, which includes detailed information about the pipeline components and their model architectures, and all other settings and hyperparameters used to train the model. It's the single source of truth used for loading a model.

Example

{
  "name": "example_model",
  "lang": "en",
  "version": "1.0.0",
  "spacy_version": ">=3.0.0,<3.1.0",
  "parent_package": "spacy",
  "description": "Example model for spaCy",
  "author": "You",
  "email": "you@example.com",
  "url": "https://example.com",
  "license": "CC BY-SA 3.0",
  "sources": [{ "name": "My Corpus", "license": "MIT" }],
  "vectors": { "width": 0, "vectors": 0, "keys": 0, "name": null },
  "pipeline": ["tok2vec", "ner", "textcat"],
  "labels": {
    "ner": ["PERSON", "ORG", "PRODUCT"],
    "textcat": ["POSITIVE", "NEGATIVE"]
  },
  "accuracy": {
    "ents_f": 82.7300930714,
    "ents_p": 82.135523614,
    "ents_r": 83.3333333333,
    "textcat_score": 88.364323811
  },
  "speed": { "cpu": 7667.8, "gpu": null, "nwords": 10329 },
  "spacy_git_version": "61dfdd9fb"
}

Name	Description
lang	Model language ISO code. Defaults to "en". str
name	Model name, e.g. "core_web_sm". The final model package name will be {lang}_{name}. Defaults to "model". str
version	Model version. Will be used to version a Python package created with spacy package. Defaults to "0.0.0". str
spacy_version	spaCy version range the model is compatible with. Defaults to the spaCy version used to create the model, up to next minor version, which is the default compatibility for the available pretrained models. For instance, a model 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
description	Model description. Also used for Python package. Defaults to "". str
author	Model author name. Also used for Python package. Defaults to "". str
email	Model author email. Also used for Python package. Defaults to "". str
url	Model author URL. Also used for Python package. Defaults to "". str
license	Model license. Also used for Python package. Defaults to "". str
sources	Data sources used to train the model. Typically a list of dicts with the keys "name", "url", "author" and "license". See here for examples. Defaults to None. Optional[List[Dict[str, str]]]
vectors	Information about the word vectors included with the model. Typically a dict with the keys "width", "vectors" (number of vectors), "keys" and "name". Dict[str, Any]
pipeline	Names of pipeline component names in the model, in order. Corresponds to nlp.pipe_names. Only exists for reference and is not used to create the components. This information is defined in the config.cfg. Defaults to []. List[str]
labels	Label schemes of the trained pipeline components, keyed by component name. Corresponds to nlp.pipe_labels. See here for examples. Defaults to {}. Dict[str, Dict[str, List[str]]]
accuracy	Training accuracy, added automatically by spacy train. Dictionary of score names mapped to scores. Defaults to {}. Dict[str, Union[float, Dict[str, float]]]
speed	Model speed, added automatically by spacy 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 3	Git commit of spacy used to create model. str
other	Any other custom meta information you want to add. The data is preserved in nlp.meta. Any