spaCy/website/docs/api/lexeme.md

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---
title: Lexeme
teaser: An entry in the vocabulary
tag: class
source: spacy/lexeme.pyx
---
A `Lexeme` has no string context it's a word type, as opposed to a word token.
It therefore has no part-of-speech tag, dependency parse, or lemma (if
lemmatization depends on the part-of-speech tag).
## Lexeme.\_\_init\_\_ {#init tag="method"}
Create a `Lexeme` object.
| Name | Type | Description |
| ----------- | -------- | ----------------------------- |
| `vocab` | `Vocab` | The parent vocabulary. |
| `orth` | int | The orth id of the lexeme. |
| **RETURNS** | `Lexeme` | The newly constructed object. |
## Lexeme.set_flag {#set_flag tag="method"}
Change the value of a boolean flag.
> #### Example
>
> ```python
> COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
> nlp.vocab["spaCy"].set_flag(COOL_FLAG, True)
> ```
| Name | Type | Description |
| --------- | ---- | ------------------------------------ |
| `flag_id` | int | The attribute ID of the flag to set. |
| `value` | bool | The new value of the flag. |
## Lexeme.check_flag {#check_flag tag="method"}
Check the value of a boolean flag.
> #### Example
>
> ```python
> is_my_library = lambda text: text in ["spaCy", "Thinc"]
> MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
> assert nlp.vocab["spaCy"].check_flag(MY_LIBRARY) == True
> ```
| Name | Type | Description |
| ----------- | ---- | -------------------------------------- |
| `flag_id` | int | The attribute ID of the flag to query. |
| **RETURNS** | bool | The value of the flag. |
## Lexeme.similarity {#similarity tag="method" model="vectors"}
Compute a semantic similarity estimate. Defaults to cosine over vectors.
> #### Example
>
> ```python
> apple = nlp.vocab["apple"]
> orange = nlp.vocab["orange"]
> apple_orange = apple.similarity(orange)
> orange_apple = orange.similarity(apple)
> assert apple_orange == orange_apple
> ```
| Name | Type | Description |
| ----------- | ----- | -------------------------------------------------------------------------------------------- |
| other | - | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. |
| **RETURNS** | float | A scalar similarity score. Higher is more similar. |
## Lexeme.has_vector {#has_vector tag="property" model="vectors"}
A boolean value indicating whether a word vector is associated with the lexeme.
> #### Example
>
> ```python
> apple = nlp.vocab["apple"]
> assert apple.has_vector
> ```
| Name | Type | Description |
| ----------- | ---- | ---------------------------------------------- |
| **RETURNS** | bool | Whether the lexeme has a vector data attached. |
## Lexeme.vector {#vector tag="property" model="vectors"}
A real-valued meaning representation.
> #### Example
>
> ```python
> apple = nlp.vocab["apple"]
> assert apple.vector.dtype == "float32"
> assert apple.vector.shape == (300,)
> ```
| Name | Type | Description |
| ----------- | ---------------------------------------- | ----------------------------------------------------- |
| **RETURNS** | `numpy.ndarray[ndim=1, dtype='float32']` | A 1D numpy array representing the lexeme's semantics. |
## Lexeme.vector_norm {#vector_norm tag="property" model="vectors"}
The L2 norm of the lexeme's vector representation.
> #### Example
>
> ```python
> apple = nlp.vocab["apple"]
> pasta = nlp.vocab["pasta"]
> apple.vector_norm # 7.1346845626831055
> pasta.vector_norm # 7.759851932525635
> assert apple.vector_norm != pasta.vector_norm
> ```
| Name | Type | Description |
| ----------- | ----- | ----------------------------------------- |
| **RETURNS** | float | The L2 norm of the vector representation. |
## Attributes {#attributes}
| Name | Type | Description |
| -------------------------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `vocab` | `Vocab` | The lexeme's vocabulary. |
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| `text` | str | Verbatim text content. |
| `orth` | int | ID of the verbatim text content. |
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| `orth_` | str | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. |
| `rank` | int | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. |
| `flags` | int | Container of the lexeme's binary flags. |
| `norm` | int | The lexemes's norm, i.e. a normalized form of the lexeme text. |
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| `norm_` | str | The lexemes's norm, i.e. a normalized form of the lexeme text. |
| `lower` | int | Lowercase form of the word. |
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| `lower_` | str | Lowercase form of the word. |
| `shape` | int | Transform of the words's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
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| `shape_` | str | Transform of the word's string, to show orthographic features. Alphabetic characters are replaced by `x` or `X`, and numeric characters are replaced by d`, and sequences of the same character are truncated after length 4. For example,`"Xxxx"`or`"dd"`. |
| `prefix` | int | Length-N substring from the start of the word. Defaults to `N=1`. |
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| `prefix_` | str | Length-N substring from the start of the word. Defaults to `N=1`. |
| `suffix` | int | Length-N substring from the end of the word. Defaults to `N=3`. |
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| `suffix_` | str | Length-N substring from the start of the word. Defaults to `N=3`. |
| `is_alpha` | bool | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. |
| `is_ascii` | bool | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. |
| `is_digit` | bool | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. |
| `is_lower` | bool | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. |
| `is_upper` | bool | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. |
| `is_title` | bool | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. |
| `is_punct` | bool | Is the lexeme punctuation? |
| `is_left_punct` | bool | Is the lexeme a left punctuation mark, e.g. `(`? |
| `is_right_punct` | bool | Is the lexeme a right punctuation mark, e.g. `)`? |
| `is_space` | bool | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. |
| `is_bracket` | bool | Is the lexeme a bracket? |
| `is_quote` | bool | Is the lexeme a quotation mark? |
| `is_currency` <Tag variant="new">2.0.8</Tag> | bool | Is the lexeme a currency symbol? |
| `like_url` | bool | Does the lexeme resemble a URL? |
| `like_num` | bool | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. |
| `like_email` | bool | Does the lexeme resemble an email address? |
| `is_oov` | bool | Does the lexeme have a word vector? |
| `is_stop` | bool | Is the lexeme part of a "stop list"? |
| `lang` | int | Language of the parent vocabulary. |
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| `lang_` | str | Language of the parent vocabulary. |
| `prob` | float | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). |
| `cluster` | int | Brown cluster ID. |
| `sentiment` | float | A scalar value indicating the positivity or negativity of the lexeme. |