---
title: GoldParse
teaser: A collection for training annotations
tag: class
source: spacy/gold.pyx
---
## GoldParse.\_\_init\_\_ {#init tag="method"}
Create a `GoldParse`. Unlike annotations in `entities`, label annotations in
`cats` can overlap, i.e. a single word can be covered by multiple labelled
spans. The [`TextCategorizer`](/api/textcategorizer) component expects true
examples of a label to have the value `1.0`, and negative examples of a label to
have the value `0.0`. Labels not in the dictionary are treated as missing – the
gradient for those labels will be zero.
| Name | Type | Description |
| ----------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The document the annotations refer to. |
| `words` | iterable | A sequence of unicode word strings. |
| `tags` | iterable | A sequence of strings, representing tag annotations. |
| `heads` | iterable | A sequence of integers, representing syntactic head offsets. |
| `deps` | iterable | A sequence of strings, representing the syntactic relation types. |
| `entities` | iterable | A sequence of named entity annotations, either as BILUO tag strings, or as `(start_char, end_char, label)` tuples, representing the entity positions. If BILUO tag strings, you can specify missing values by setting the tag to None. |
| `cats` | dict | Labels for text classification. Each key in the dictionary may be a string or an int, or a `(start_char, end_char, label)` tuple, indicating that the label is applied to only part of the document (usually a sentence). |
| `links` | dict | Labels for entity linking. A dict with `(start_char, end_char)` keys, and the values being dicts with `kb_id:value` entries, representing external KB IDs mapped to either 1.0 (positive) or 0.0 (negative). |
| **RETURNS** | `GoldParse` | The newly constructed object. |
## GoldParse.\_\_len\_\_ {#len tag="method"}
Get the number of gold-standard tokens.
| Name | Type | Description |
| ----------- | ---- | ----------------------------------- |
| **RETURNS** | int | The number of gold-standard tokens. |
## GoldParse.is_projective {#is_projective tag="property"}
Whether the provided syntactic annotations form a projective dependency tree.
| Name | Type | Description |
| ----------- | ---- | ----------------------------------------- |
| **RETURNS** | bool | Whether annotations form projective tree. |
## Attributes {#attributes}
| Name | Type | Description |
| ------------------------------------ | ---- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `words` | list | The words. |
| `tags` | list | The part-of-speech tag annotations. |
| `heads` | list | The syntactic head annotations. |
| `labels` | list | The syntactic relation-type annotations. |
| `ner` | list | The named entity annotations as BILUO tags. |
| `cand_to_gold` | list | The alignment from candidate tokenization to gold tokenization. |
| `gold_to_cand` | list | The alignment from gold tokenization to candidate tokenization. |
| `cats` 2 | list | Entries in the list should be either a label, or a `(start, end, label)` triple. The tuple form is used for categories applied to spans of the document. |
| `links` 2.2 | dict | Keys in the dictionary are `(start_char, end_char)` triples, and the values are dictionaries with `kb_id:value` entries. |
## Utilities {#util}
### gold.docs_to_json {#docs_to_json tag="function"}
Convert a list of Doc objects into the
[JSON-serializable format](/api/annotation#json-input) used by the
[`spacy train`](/api/cli#train) command.
> #### Example
>
> ```python
> from spacy.gold import docs_to_json
>
> doc = nlp(u"I like London")
> json_data = docs_to_json([doc])
> ```
| Name | Type | Description |
| ----------- | ---------------- | ------------------------------------------ |
| `docs` | iterable / `Doc` | The `Doc` object(s) to convert. |
| `id` | int | ID to assign to the JSON. Defaults to `0`. |
| **RETURNS** | list | The data in spaCy's JSON format. |
### gold.align {#align tag="function"}
Calculate alignment tables between two tokenizations, using the Levenshtein
algorithm. The alignment is case-insensitive.
The current implementation of the alignment algorithm assumes that both
tokenizations add up to the same string. For example, you'll be able to align
`["I", "'", "m"]` and `["I", "'m"]`, which both add up to `"I'm"`, but not
`["I", "'m"]` and `["I", "am"]`.
> #### Example
>
> ```python
> from spacy.gold import align
>
> bert_tokens = ["obama", "'", "s", "podcast"]
> spacy_tokens = ["obama", "'s", "podcast"]
> alignment = align(bert_tokens, spacy_tokens)
> cost, a2b, b2a, a2b_multi, b2a_multi = alignment
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------------------- |
| `tokens_a` | list | String values of candidate tokens to align. |
| `tokens_b` | list | String values of reference tokens to align. |
| **RETURNS** | tuple | A `(cost, a2b, b2a, a2b_multi, b2a_multi)` tuple describing the alignment. |
The returned tuple contains the following alignment information:
> #### Example
>
> ```python
> a2b = array([0, -1, -1, 2])
> b2a = array([0, 2, 3])
> a2b_multi = {1: 1, 2: 1}
> b2a_multi = {}
> ```
>
> If `a2b[3] == 2`, that means that `tokens_a[3]` aligns to `tokens_b[2]`. If
> there's no one-to-one alignment for a token, it has the value `-1`.
| Name | Type | Description |
| ----------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| `cost` | int | The number of misaligned tokens. |
| `a2b` | `numpy.ndarray[ndim=1, dtype='int32']` | One-to-one mappings of indices in `tokens_a` to indices in `tokens_b`. |
| `b2a` | `numpy.ndarray[ndim=1, dtype='int32']` | One-to-one mappings of indices in `tokens_b` to indices in `tokens_a`. |
| `a2b_multi` | dict | A dictionary mapping indices in `tokens_a` to indices in `tokens_b`, where multiple tokens of `tokens_a` align to the same token of `tokens_b`. |
| `b2a_multi` | dict | A dictionary mapping indices in `tokens_b` to indices in `tokens_a`, where multiple tokens of `tokens_b` align to the same token of `tokens_a`. |
### gold.biluo_tags_from_offsets {#biluo_tags_from_offsets tag="function"}
Encode labelled spans into per-token tags, using the
[BILUO scheme](/api/annotation#biluo) (Begin, In, Last, Unit, Out). Returns a
list of unicode strings, describing the tags. Each tag string will be of the
form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of
`"B"`, `"I"`, `"L"`, `"U"`. The string `"-"` is used where the entity offsets
don't align with the tokenization in the `Doc` object. The training algorithm
will view these as missing values. `O` denotes a non-entity token. `B` denotes
the beginning of a multi-token entity, `I` the inside of an entity of three or
more tokens, and `L` the end of an entity of two or more tokens. `U` denotes a
single-token entity.
> #### Example
>
> ```python
> from spacy.gold import biluo_tags_from_offsets
>
> doc = nlp(u"I like London.")
> entities = [(7, 13, "LOC")]
> tags = biluo_tags_from_offsets(doc, entities)
> assert tags == ["O", "O", "U-LOC", "O"]
> ```
| Name | Type | Description |
| ----------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document. |
| `entities` | iterable | A sequence of `(start, end, label)` triples. `start` and `end` should be character-offset integers denoting the slice into the original string. |
| **RETURNS** | list | Unicode strings, describing the [BILUO](/api/annotation#biluo) tags. |
### gold.offsets_from_biluo_tags {#offsets_from_biluo_tags tag="function"}
Encode per-token tags following the [BILUO scheme](/api/annotation#biluo) into
entity offsets.
> #### Example
>
> ```python
> from spacy.gold import offsets_from_biluo_tags
>
> doc = nlp(u"I like London.")
> tags = ["O", "O", "U-LOC", "O"]
> entities = offsets_from_biluo_tags(doc, tags)
> assert entities == [(7, 13, "LOC")]
> ```
| Name | Type | Description |
| ----------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The document that the BILUO tags refer to. |
| `entities` | iterable | A sequence of [BILUO](/api/annotation#biluo) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
| **RETURNS** | list | A sequence of `(start, end, label)` triples. `start` and `end` will be character-offset integers denoting the slice into the original string. |
### gold.spans_from_biluo_tags {#spans_from_biluo_tags tag="function" new="2.1"}
Encode per-token tags following the [BILUO scheme](/api/annotation#biluo) into
[`Span`](/api/span) objects. This can be used to create entity spans from
token-based tags, e.g. to overwrite the `doc.ents`.
> #### Example
>
> ```python
> from spacy.gold import spans_from_biluo_tags
>
> doc = nlp(u"I like London.")
> tags = ["O", "O", "U-LOC", "O"]
> doc.ents = spans_from_biluo_tags(doc, tags)
> ```
| Name | Type | Description |
| ----------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `doc` | `Doc` | The document that the BILUO tags refer to. |
| `entities` | iterable | A sequence of [BILUO](/api/annotation#biluo) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
| **RETURNS** | list | A sequence of `Span` objects with added entity labels. |