--- 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 | Description | | ------- | ---------------------------------- | | `vocab` | The parent vocabulary. ~~Vocab~~ | | `orth` | The orth id of the lexeme. ~~int~~ | ## 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 | Description | | --------- | -------------------------------------------- | | `flag_id` | The attribute ID of the flag to set. ~~int~~ | | `value` | The new value of the flag. ~~bool~~ | ## 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 | Description | | ----------- | ---------------------------------------------- | | `flag_id` | The attribute ID of the flag to query. ~~int~~ | | **RETURNS** | The value of the flag. ~~bool~~ | ## 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 | Description | | ----------- | -------------------------------------------------------------------------------------------------------------------------------- | | other | The object to compare with. By default, accepts `Doc`, `Span`, `Token` and `Lexeme` objects. ~~Union[Doc, Span, Token, Lexeme]~~ | | **RETURNS** | A scalar similarity score. Higher is more similar. ~~float~~ | ## 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 | Description | | ----------- | ------------------------------------------------------- | | **RETURNS** | Whether the lexeme has a vector data attached. ~~bool~~ | ## 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 | Description | | ----------- | ------------------------------------------------------------------------------------------------ | | **RETURNS** | A 1-dimensional array representing the lexeme's vector. ~~numpy.ndarray[ndim=1, dtype=float32]~~ | ## 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 | Description | | ----------- | --------------------------------------------------- | | **RETURNS** | The L2 norm of the vector representation. ~~float~~ | ## Attributes {#attributes} | Name | Description | | -------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `vocab` | The lexeme's vocabulary. ~~Vocab~~ | | `text` | Verbatim text content. ~~str~~ | | `orth` | ID of the verbatim text content. ~~int~~ | | `orth_` | Verbatim text content (identical to `Lexeme.text`). Exists mostly for consistency with the other attributes. ~~str~~ | | `rank` | Sequential ID of the lexemes's lexical type, used to index into tables, e.g. for word vectors. ~~int~~ | | `flags` | Container of the lexeme's binary flags. ~~int~~ | | `norm` | The lexemes's norm, i.e. a normalized form of the lexeme text. ~~int~~ | | `norm_` | The lexemes's norm, i.e. a normalized form of the lexeme text. ~~str~~ | | `lower` | Lowercase form of the word. ~~int~~ | | `lower_` | Lowercase form of the word. ~~str~~ | | `shape` | 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"`. ~~int~~ | | `shape_` | 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"`. ~~str~~ | | `prefix` | Length-N substring from the start of the word. Defaults to `N=1`. ~~int~~ | | `prefix_` | Length-N substring from the start of the word. Defaults to `N=1`. ~~str~~ | | `suffix` | Length-N substring from the end of the word. Defaults to `N=3`. ~~int~~ | | `suffix_` | Length-N substring from the start of the word. Defaults to `N=3`. ~~str~~ | | `is_alpha` | Does the lexeme consist of alphabetic characters? Equivalent to `lexeme.text.isalpha()`. ~~bool~~ | | `is_ascii` | Does the lexeme consist of ASCII characters? Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`. ~~bool~~ | | `is_digit` | Does the lexeme consist of digits? Equivalent to `lexeme.text.isdigit()`. ~~bool~~ | | `is_lower` | Is the lexeme in lowercase? Equivalent to `lexeme.text.islower()`. ~~bool~~ | | `is_upper` | Is the lexeme in uppercase? Equivalent to `lexeme.text.isupper()`. ~~bool~~ | | `is_title` | Is the lexeme in titlecase? Equivalent to `lexeme.text.istitle()`. ~~bool~~ | | `is_punct` | Is the lexeme punctuation? ~~bool~~ | | `is_left_punct` | Is the lexeme a left punctuation mark, e.g. `(`? ~~bool~~ | | `is_right_punct` | Is the lexeme a right punctuation mark, e.g. `)`? ~~bool~~ | | `is_space` | Does the lexeme consist of whitespace characters? Equivalent to `lexeme.text.isspace()`. ~~bool~~ | | `is_bracket` | Is the lexeme a bracket? ~~bool~~ | | `is_quote` | Is the lexeme a quotation mark? ~~bool~~ | | `is_currency` 2.0.8 | Is the lexeme a currency symbol? ~~bool~~ | | `like_url` | Does the lexeme resemble a URL? ~~bool~~ | | `like_num` | Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc. ~~bool~~ | | `like_email` | Does the lexeme resemble an email address? ~~bool~~ | | `is_oov` | Does the lexeme have a word vector? ~~bool~~ | | `is_stop` | Is the lexeme part of a "stop list"? ~~bool~~ | | `lang` | Language of the parent vocabulary. ~~int~~ | | `lang_` | Language of the parent vocabulary. ~~str~~ | | `prob` | Smoothed log probability estimate of the lexeme's word type (context-independent entry in the vocabulary). ~~float~~ | | `cluster` | Brown cluster ID. ~~int~~ | | `sentiment` | A scalar value indicating the positivity or negativity of the lexeme. ~~float~~ |