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