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

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.

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	Type	Description	Default
lang	str	The language code to use.	null
pipeline	List[str]	Names of pipeline components in order. Should correspond to sections in the [components] block, e.g. [components.ner]. See docs on defining components.	[]
load_vocab_data	bool	Whether to load additional lexeme and vocab data from spacy-lookups-data if available.	true
before_creation	callable	Optional callback to modify Language subclass before it's initialized.	null
after_creation	callable	Optional callback to modify nlp object right after it's initialized.	null
after_pipeline_creation	callable	Optional callback to modify nlp object after the pipeline components have been added.	null
tokenizer	callable	The tokenizer to use.	Tokenizer

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

pretraining

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

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

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 object, 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

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	Type	Description
text	str	Raw text.
words	List[str]	List of gold-standard tokens.
lemmas	List[str]	List of lemmas.
spaces	List[bool]	List of boolean values indicating whether the corresponding tokens is followed by a space or not.
tags	List[str]	List of fine-grained POS tags.
pos	List[str]	List of coarse-grained POS tags.
morphs	List[str]	List of morphological features.
sent_starts	List[bool]	List of boolean values indicating whether each token is the first of a sentence or not.
deps	List[str]	List of string values indicating the dependency relation of a token to its head.
heads	List[int]	List of integer values indicating the dependency head of each token, referring to the absolute index of each token in the text.
entities	List[str]	Option 1: List of BILUO tags per token of the format "{action}-{label}", or None for unannotated tokens.
entities	List[Tuple[int, int, str]]	Option 2: List of "(start, end, label)" tuples defining all entities in the text.
cats	Dict[str, float]	Dictionary of label/value pairs indicating how relevant a certain text category is for the text.
links	Dict[(int, int), Dict]	Dictionary of offset/dict pairs defining named entity links. The character offsets are linked to a dictionary of relevant knowledge base IDs.

• 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 tok2vec 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	Type	Description
text	str	The raw input text. Is not required if tokens available.
tokens	list	Optional tokenization, one string per token.

### 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