---
title: PhraseMatcher
teaser: Match sequences of tokens, based on documents
tag: class
source: spacy/matcher/phrasematcher.pyx
new: 2
---
The `PhraseMatcher` lets you efficiently match large terminology lists. While
the [`Matcher`](/api/matcher) lets you match sequences based on lists of token
descriptions, the `PhraseMatcher` accepts match patterns in the form of `Doc`
objects.
## PhraseMatcher.\_\_init\_\_ {#init tag="method"}
Create the rule-based `PhraseMatcher`. Setting a different `attr` to match on
will change the token attributes that will be compared to determine a match. By
default, the incoming `Doc` is checked for sequences of tokens with the same
`ORTH` value, i.e. the verbatim token text. Matching on the attribute `LOWER`
will result in case-insensitive matching, since only the lowercase token texts
are compared. In theory, it's also possible to match on sequences of the same
part-of-speech tags or dependency labels.
If `validate=True` is set, additional validation is performed when pattern are
added. At the moment, it will check whether a `Doc` has attributes assigned that
aren't necessary to produce the matches (for example, part-of-speech tags if the
`PhraseMatcher` matches on the token text). Since this can often lead to
significantly worse performance when creating the pattern, a `UserWarning` will
be shown.
> #### Example
>
> ```python
> from spacy.matcher import PhraseMatcher
> matcher = PhraseMatcher(nlp.vocab)
> ```
| Name | Type | Description |
| --------------------------------------- | --------------- | ------------------------------------------------------------------------------------------- |
| `vocab` | `Vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. |
| `attr` <Tag variant="new">2.1</Tag> | int / str | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. |
| `validate` <Tag variant="new">2.1</Tag> | bool | Validate patterns added to the matcher. |
| **RETURNS** | `PhraseMatcher` | The newly constructed object. |
## PhraseMatcher.\_\_call\_\_ {#call tag="method"}
Find all token sequences matching the supplied patterns on the `Doc`.
> #### Example
>
> ```python
> from spacy.matcher import PhraseMatcher
>
> matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", [nlp("Barack Obama")])
> doc = nlp("Barack Obama lifts America one last time in emotional farewell")
> matches = matcher(doc)
> ```
| Name | Type | Description |
| ----------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `doc` | `Doc` | The document to match over. |
| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. |
<Infobox title="Note on retrieving the string representation of the match_id" variant="warning">
Because spaCy stores all strings as integers, the `match_id` you get back will
be an integer, too – but you can always get the string representation by looking
it up in the vocabulary's `StringStore`, i.e. `nlp.vocab.strings`:
```python
match_id_string = nlp.vocab.strings[match_id]
```
</Infobox>
## PhraseMatcher.pipe {#pipe tag="method"}
Match a stream of documents, yielding them in turn.
> #### Example
>
> ```python
> from spacy.matcher import PhraseMatcher
> matcher = PhraseMatcher(nlp.vocab)
> for doc in matcher.pipe(docs, batch_size=50):
> pass
> ```
| Name | Type | Description |
| ------------ | -------- | --------------------------------------------------------- |
| `docs` | iterable | A stream of documents. |
| `batch_size` | int | The number of documents to accumulate into a working set. |
| **YIELDS** | `Doc` | Documents, in order. |
## PhraseMatcher.\_\_len\_\_ {#len tag="method"}
Get the number of rules added to the matcher. Note that this only returns the
number of rules (identical with the number of IDs), not the number of individual
patterns.
> #### Example
>
> ```python
> matcher = PhraseMatcher(nlp.vocab)
> assert len(matcher) == 0
> matcher.add("OBAMA", [nlp("Barack Obama")])
> assert len(matcher) == 1
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------- |
| **RETURNS** | int | The number of rules. |
## PhraseMatcher.\_\_contains\_\_ {#contains tag="method"}
Check whether the matcher contains rules for a match ID.
> #### Example
>
> ```python
> matcher = PhraseMatcher(nlp.vocab)
> assert "OBAMA" not in matcher
> matcher.add("OBAMA", [nlp("Barack Obama")])
> assert "OBAMA" in matcher
> ```
| Name | Type | Description |
| ----------- | ---- | ----------------------------------------------------- |
| `key` | str | The match ID. |
| **RETURNS** | bool | Whether the matcher contains rules for this match ID. |
## PhraseMatcher.add {#add tag="method"}
Add a rule to the matcher, consisting of an ID key, one or more patterns, and a
callback function to act on the matches. The callback function will receive the
arguments `matcher`, `doc`, `i` and `matches`. If a pattern already exists for
the given ID, the patterns will be extended. An `on_match` callback will be
overwritten.
> #### Example
>
> ```python
> def on_match(matcher, doc, id, matches):
> print('Matched!', matches)
>
> matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", [nlp("Barack Obama")], on_match=on_match)
> matcher.add("HEALTH", [nlp("health care reform"), nlp("healthcare reform")], on_match=on_match)
> doc = nlp("Barack Obama urges Congress to find courage to defend his healthcare reforms")
> matches = matcher(doc)
> ```
<Infobox title="Changed in v3.0" variant="warning">
As of spaCy v3.0, `PhraseMatcher.add` takes a list of patterns as the second
argument (instead of a variable number of arguments). The `on_match` callback
becomes an optional keyword argument.
```diff
patterns = [nlp("health care reform"), nlp("healthcare reform")]
- matcher.add("HEALTH", on_match, *patterns)
+ matcher.add("HEALTH", patterns, on_match=on_match)
```
</Infobox>
| Name | Type | Description |
| ---------- | ------------------ | --------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. |
| `docs` | list | `Doc` objects of the phrases to match. |
| `on_match` | callable or `None` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. |
## PhraseMatcher.remove {#remove tag="method" new="2.2"}
Remove a rule from the matcher by match ID. A `KeyError` is raised if the key
does not exist.
> #### Example
>
> ```python
> matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", [nlp("Barack Obama")])
> assert "OBAMA" in matcher
> matcher.remove("OBAMA")
> assert "OBAMA" not in matcher
> ```
| Name | Type | Description |
| ----- | ---- | ------------------------- |
| `key` | str | The ID of the match rule. |