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

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

This section documents input and output formats of data used by spaCy, including 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 data

Binary training format

Example

from pathlib import Path
from spacy.tokens import DocBin
from spacy.gold import Corpus
output_file = Path(dir) / "output.spacy"
data = DocBin(docs=docs).to_bytes()
with output_file.open("wb") as file_:
   file_.write(data)
reader = Corpus(output_file)

The main data format used in spaCy v3 is a binary format created by serializing a DocBin object, which represents a collection of Doc objects. 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.

This binary format is extremely efficient in storage, especially when packing multiple documents together.

The built-in convert command helps you convert spaCy's previous JSON format to this new DocBin 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. There are currently two formats supported for this dictionary of annotations: one with a simple, flat structure of keywords, and one with a more hierarchical structure.

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.

Flat structure

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"])
example = Example.from_dict(doc, {"tags": ["NOUN", "VERB", "NOUN"]})

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

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

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

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

Hierachical structure

Internally, a more hierarchical dictionary structure is used to store gold-standard annotations. Its format is similar to the structure described in the previous section, but there are two main sections token_annotation and doc_annotation, and the keys for token annotations should be uppercase Token attributes such as "ORTH" and "TAG".

### Hierarchical dictionary
{
    "text": string,                            # Raw text.
    "token_annotation": {
        "ORTH": List[string],                  # List of gold tokens.
        "LEMMA": List[string],                 # List of lemmas.
        "SPACY": List[bool],                   # List of boolean values indicating whether the corresponding tokens is followed by a space or not.
        "TAG": List[string],                   # List of fine-grained [POS tags](/usage/linguistic-features#pos-tagging).
        "POS": List[string],                   # List of coarse-grained [POS tags](/usage/linguistic-features#pos-tagging).
        "MORPH": List[string],                 # List of [morphological features](/usage/linguistic-features#rule-based-morphology).
        "SENT_START": List[bool],              # List of boolean values indicating whether each token is the first of a sentence or not.
        "DEP": List[string],                   # List of string values indicating the [dependency relation](/usage/linguistic-features#dependency-parse) of a token to its head.
        "HEAD": List[int],                     # List of integer values indicating the dependency head of each token, referring to the absolute index of each token in the text.
    },
    "doc_annotation": {
        "entities": List[(int, int, string)],  # List of [BILUO tags](#biluo) per token of the format `"{action}-{label}"`, or `None` for unannotated tokens.
        "cats": Dict[str, float],              # Dictionary of `label:value` pairs indicating how relevant a certain [category](/api/textcategorizer) is for the text.
        "links": Dict[(int, int), Dict],       # Dictionary of `offset:dict` pairs defining [named entity links](/usage/linguistic-features#entity-linking). The charachter offsets are linked to a dictionary of relevant knowledge base IDs.
    }
}

There are a few caveats to take into account:

• Any values for sentence starts will be ignored if there are annotations for dependency relations.
• If the dictionary contains values for "text" and "ORTH", but not "SPACY", the latter are inferred automatically. If "ORTH" is not provided either, the values are inferred from the doc argument.

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"]}

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.

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 model architectures docs for API details.

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