Metadata-Version: 2.1
Name: spacy-conll
Version: 3.1.0
Summary: A custom pipeline component for spaCy that can convert any parsed Doc and its sentences into CoNLL-U format. Also provides a command line entry point.
Home-page: https://github.com/BramVanroy/spacy_conll
Author: Bram Vanroy
Author-email: bramvanroy@hotmail.com
License: BSD 2
Project-URL: Bug Reports, https://github.com/BramVanroy/spacy_conll/issues
Project-URL: Source, https://github.com/BramVanroy/spacy_conll
Description: # Parsing to CoNLL with spaCy, spacy-stanza, and spacy-udpipe
        
        **The last version to support spaCy v2 can be found** [here](<https://github.com/BramVanroy/spacy_conll/tree/v2.1.0>).
         The current version only supports v3.
        
        This module allows you to parse text into CoNLL-U format\_. You can use it as a command line tool, or embed it in your
         own scripts by adding it as a custom pipeline component to a spaCy, `spacy-stanza`, or `spacy-udpipe` pipeline. It 
         also provides an easy-to-use function to quickly initialize a parser as well as a ConllParser class with built-in 
         functionality to parse files or text.
        
        Note that the module simply takes a parser's output and puts it in a formatted string adhering to the linked ConLL-U 
         format. The output tags depend on the spaCy model used. If you want Universal Depencies tags as output, I advise you 
         to use this library in combination with [spacy-stanza](https://github.com/explosion/spacy-stanza), which is a spaCy 
         interface using `stanza` and its models behind the scenes. Those models use the Universal Dependencies formalism and 
         yield state-of-the-art performance. `stanza` is a new and improved version of `stanfordnlp`. As an alternative to the 
         Stanford models, you can use the spaCy wrapper for `UDPipe`, [spacy-udpipe](https://github.com/TakeLab/spacy-udpipe), 
         which is slightly less accurate than `stanza` but much faster.
        
        
        ## Installation
        
        By default, this package automatically installs only [spaCy](https://spacy.io/usage/models#section-quickstart) as 
         dependency. Because [spaCy's models](https://spacy.io/usage/models) are not necessarily trained on Universal 
         Dependencies conventions, their output labels are not UD either. By using `spacy-stanza` or `spacy-udpipe`, we get 
         the easy-to-use interface of spaCy as a wrapper around `stanza` and `UDPipe` respectively, including their models that
         *are* trained on UD data.
        
        **NOTE**: `spacy-stanza` and `spacy-udpipe` are not installed automatically as a dependency for this library, because 
         it might be too much overhead for those who don't need UD. If you wish to use their functionality (e.g. better 
         performance, real UD output), you have to install them manually or use one of the available options as described 
         below.
        
        If you want to retrieve CoNLL info as a `pandas` DataFrame, this library will automatically export it if it detects 
         that `pandas` is installed. See the Usage section for more.
        
        To install the library, simply use pip.
        
        ```bash
        # only includes spacy by default
        pip install spacy_conll
        ```
        
        A number of options are available to make installation of additional dependencies easier:
        
        ```bash
        # include spacy-stanza and spacy-udpipe
        pip install spacy_conll[parsers]
        # include pandas
        pip install spacy_conll[pd]
        # include pandas, spacy-stanza and spacy-udpipe
        pip install spacy_conll[all]
        # include pandas, spacy-stanza and spacy-udpipe and additional libaries for testing and formatting
        pip install spacy_conll[dev]
        ```
        
        
        ## Usage
        
        When the ConllFormatter is added to a spaCy pipeline, it adds CoNLL properties for `Token`, sentence `Span` and `Doc`.
         Note that arbitrary Span's are not included and do not receive these properties.
        
        On all three of these levels, two custom properties are exposed by default, `._.conll` and its string 
         representation `._.conll_str`. However, if you have `pandas` installed, then `._.conll_pd` will
         be added automatically, too!
        
        -   `._.conll`: raw CoNLL format  
            -   in Token: a dictionary containing all the expected CoNLL fields as keys and the parsed properties as values.
            -   in sentence Span: a list of its tokens' `._.conll` dictionaries (list of dictionaries).
            -   in a Doc: a list of its sentences' `._.conll` lists (list of list of dictionaries).
        
        -   `._.conll_str`: string representation of the CoNLL format  
            -   in Token: tab-separated representation of the contents of the CoNLL fields ending with a newline.
            -   in sentence Span: the expected CoNLL format where each row represents a token. When 
                `ConllFormatter(include_headers=True)` is used, two header lines are included as well, as per the
                [CoNLL format](https://universaldependencies.org/format.html#sentence-boundaries-and-comments).
            -   in Doc: all its sentences' `._.conll_str` combined and separated by new lines.
        
        -   `._.conll_pd`: `pandas` representation of the CoNLL format  
            -   in Token: a Series representation of this token's CoNLL properties.
            -   in sentence Span: a DataFrame representation of this sentence, with the CoNLL names as column headers.
            -   in Doc: a concatenation of its sentences' DataFrame's, leading to a new a DataFrame whose index is reset.
        
        You can use `spacy_conll` in your own Python code as a custom pipeline component, or you can use the built-in
         command-line script which offers typically needed functionality. See the following section for more.
        
        
        ### In Python
        
        This library offers the ConllFormatter class which serves as a custom spaCy pipeline component. It can be instantiated
         as follows. It is important that you import `spacy_conll` before adding the pipe!
        
        ```python
        import spacy_conll
        nlp = <initialise parser>
        nlp.add_pipe("conll_formatter", last=True)
        ```
        
        Because this library supports different spaCy wrappers (`spacy`, `stanza`, and `udpipe`), a convenience function is
         available as well. With `utils.init_parser` you can easily instantiate a parser with a single line. You can
         find the function's signature below. Have a look at the [source code](spacy_conll/utils.py) to read more about all the
         possible arguments or try out the [examples](examples/).
        
        **NOTE**: `is_tokenized` does not work for `spacy-udpipe` and `disable_sbd` only works for `spacy`. `spacy-udpipe` has
         made a change to allow pretokenized text, but it depends on the input format and cannot be fixed at initialisation of
         the parser. See release v0.3.0 of spacy-udpipe or [this PR](https://github.com/TakeLab/spacy-udpipe/pull/19). Using
         `is_tokenized` for `spacy-stanza` also effects sentence segmentation, effectively *only* splitting on new
         lines. With `spacy`, `is_tokenized` disables sentence splitting completely.
        
        ```python
        def init_parser(
            model_or_lang: str,
            parser: str,
            *,
            is_tokenized: bool = False,
            disable_sbd: bool = False,
            parser_opts: Optional[Dict] = None,
            **kwargs,
        ) -> Language:
        ```
        
        For instance, if you want to load a Dutch `stanza` model in silent mode with the CoNLL formatter already attached, you
         can simply use the following snippet. `parser_opts` is passed to the `stanza` pipeline initialisation automatically. 
         Any other keyword arguments (`kwargs`), on the other hand, are passed to the `ConllFormatter` initialisation.
        
        ```python
        from spacy_conll import init_parser
        
        nlp = init_parser("nl", "stanza", parser_opts={"verbose": False})
        ```
        
        The `ConllFormatter` allows you to customize the extension names, and you can also specify conversion maps for the
        output properties.
        
        To illustrate, here is an advanced example, showing the more complex options:
        
        - `ext_names`: changes the attribute names to a custom key by using a dictionary.
        -  `conversion_maps`: a two-level dictionary that looks like `{field_name: {tag_name: replacement}}`. In 
           other words, you can specify in which field a certain value should be replaced by another. This is especially useful
           when you are not satisfied with the tagset of a model and wish to change some tags to an alternative0. 
        
        The example below
        
        - shows how to manually add the component;
        - changes the custom attribute `conll_pd` to pandas (`conll_pd` only availabe if `pandas` is installed);
        - converts any `nsubj` deprel tag to `subj`.
        
        ```python
        import spacy
        import spacy_conll
        
        
        nlp = spacy.load("en_core_web_sm")
        config = {"ext_names": {"conll_pd": "pandas"},
                  "conversion_maps": {"deprel": {"nsubj": "subj"}}}
        nlp.add_pipe("conll_formatter", config=config, last=True)
        doc = nlp("I like cookies.")
        print(doc._.pandas)
        ```
        
        This is the same as:
        
        ```python
        from spacy_conll import init_parser
        
        nlp = init_parser("en_core_web_sm",
                          "spacy",
                          ext_names={"conll_pd": "pandas"},
                          conversion_maps={"deprel": {"nsubj": "subj"}})
        doc = nlp("I like cookies.")
        print(doc._.pandas)
        ```
        
        
        The snippets above will output a pandas DataFrame by using `._.pandas` rather than the standard
        `._.conll_pd`, and all occurrences of `nsubj` in the deprel field are replaced by `subj`.
        
        ```
           id     form   lemma upostag xpostag                                       feats  head deprel deps           misc
        0   1        I       I    PRON     PRP  Case=Nom|Number=Sing|Person=1|PronType=Prs     2   subj    _              _
        1   2     like    like    VERB     VBP                     Tense=Pres|VerbForm=Fin     0   ROOT    _              _
        2   3  cookies  cookie    NOUN     NNS                                 Number=Plur     2   dobj    _  SpaceAfter=No
        3   4        .       .   PUNCT       .                              PunctType=Peri     2  punct    _  SpaceAfter=No
        ```
        
        
        #### Reading CoNLL into a spaCy object
        
        It is possible to read a CoNLL string or text file and parse it as a spaCy object. This can be useful if you have raw
        CoNLL data that you wish to process in different ways. The process is straightforward.
        
        ```python
        from spacy_conll import init_parser
        from spacy_conll.parser import ConllParser
        
        
        nlp = ConllParser(init_parser("en_core_web_sm", "spacy"))
        
        doc = nlp.parse_conll_file_as_spacy("path/to/your/conll-sample.txt")
        '''
        or straight from raw text:
        conllstr = """
        # text = From the AP comes this story :
        1	From	from	ADP	IN	_	3	case	3:case	_
        2	the	the	DET	DT	Definite=Def|PronType=Art	3	det	3:det	_
        3	AP	AP	PROPN	NNP	Number=Sing	4	obl	4:obl:from	_
        4	comes	come	VERB	VBZ	Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin	0	root	0:root	_
        5	this	this	DET	DT	Number=Sing|PronType=Dem	6	det	6:det	_
        6	story	story	NOUN	NN	Number=Sing	4	nsubj	4:nsubj	_
        """
        doc = nlp.parse_conll_text_as_spacy(conllstr)
        '''
        
        # Multiple CoNLL entries (separated by two newlines) will be included as different sentences in the resulting Doc
        for sent in doc.sents:
            for token in sent:
                print(token.text, token.dep_, token.pos_)
        ```
        
        ### Command line
        
        Upon installation, a command-line script is added under tha alias `parse-as-conll`. You can use it to parse a
        string or file into CoNLL format given a number of options.
        
        ```bash
        > parse-as-conll -h
        usage: parse-as-conll [-h] [-f INPUT_FILE] [-a INPUT_ENCODING] [-b INPUT_STR] [-o OUTPUT_FILE]
                          [-c OUTPUT_ENCODING] [-s] [-t] [-d] [-e] [-j N_PROCESS] [-v]
                          [--ignore_pipe_errors] [--no_split_on_newline]
                          model_or_lang {spacy,stanza,udpipe}
        
        Parse an input string or input file to CoNLL-U format using a spaCy-wrapped parser. The output
        can be written to stdout or a file, or both.
        
        positional arguments:
          model_or_lang         Model or language to use. SpaCy models must be pre-installed, stanza
                                and udpipe models will be downloaded automatically
          {spacy,stanza,udpipe}
                                Which parser to use. Parsers other than 'spacy' need to be installed
                                separately. For 'stanza' you need 'spacy-stanza', and for 'udpipe' the
                                'spacy-udpipe' library is required.
        
        optional arguments:
          -h, --help            show this help message and exit
          -f INPUT_FILE, --input_file INPUT_FILE
                                Path to file with sentences to parse. Has precedence over 'input_str'.
                                (default: None)
          -a INPUT_ENCODING, --input_encoding INPUT_ENCODING
                                Encoding of the input file. Default value is system default. (default:
                                cp1252)
          -b INPUT_STR, --input_str INPUT_STR
                                Input string to parse. (default: None)
          -o OUTPUT_FILE, --output_file OUTPUT_FILE
                                Path to output file. If not specified, the output will be printed on
                                standard output. (default: None)
          -c OUTPUT_ENCODING, --output_encoding OUTPUT_ENCODING
                                Encoding of the output file. Default value is system default. (default:
                                cp1252)
          -s, --disable_sbd     Whether to disable spaCy automatic sentence boundary detection. In
                                practice, disabling means that every line will be parsed as one
                                sentence, regardless of its actual content. When 'is_tokenized' is
                                enabled, 'disable_sbd' is enabled automatically (see 'is_tokenized').
                                Only works when using 'spacy' as 'parser'. (default: False)
          -t, --is_tokenized    Whether your text has already been tokenized (space-seperated). Setting
                                this option has as an important consequence that no sentence splitting
                                at all will be done except splitting on new lines. So if your input is
                                a file, and you want to use pretokenised text, make sure that each line
                                contains exactly one sentence. (default: False)
          -d, --include_headers
                                Whether to include headers before the output of every sentence. These
                                headers include the sentence text and the sentence ID as per the CoNLL
                                format. (default: False)
          -e, --no_force_counting
                                Whether to disable force counting the 'sent_id', starting from 1 and
                                increasing for each sentence. Instead, 'sent_id' will depend on how
                                spaCy returns the sentences. Must have 'include_headers' enabled.
                                (default: False)
          -j N_PROCESS, --n_process N_PROCESS
                                Number of processes to use in nlp.pipe(). -1 will use as many cores as
                                available. Might not work for a 'parser' other than 'spacy' depending
                                on your environment. (default: 1)
          -v, --verbose         Whether to always print the output to stdout, regardless of
                                'output_file'. (default: False)
          --ignore_pipe_errors  Whether to ignore a priori errors concerning 'n_process' By default we
                                try to determine whether processing works on your system and stop
                                execution if we think it doesn't. If you know what you are doing, you
                                can ignore such pre-emptive errors, though, and run the code as-is,
                                which will then throw the default Python errors when applicable.
                                (default: False)
          --no_split_on_newline
                                By default, the input file or string is split on newlines for faster
                                processing of the split up parts. If you want to disable that behavior,
                                you can use this flag. (default: False)
        ```
        
        
        For example, parsing a single line, multi-sentence string:
        
        ```bash
        >  parse-as-conll en_core_web_sm spacy --input_str "I like cookies. What about you?" --include_headers
        
        # sent_id = 1
        # text = I like cookies.
        1       I       I       PRON    PRP     Case=Nom|Number=Sing|Person=1|PronType=Prs      2       nsubj   _       _
        2       like    like    VERB    VBP     Tense=Pres|VerbForm=Fin 0       ROOT    _       _
        3       cookies cookie  NOUN    NNS     Number=Plur     2       dobj    _       SpaceAfter=No
        4       .       .       PUNCT   .       PunctType=Peri  2       punct   _       _
        
        # sent_id = 2
        # text = What about you?
        1       What    what    PRON    WP      _       2       dep     _       _
        2       about   about   ADP     IN      _       0       ROOT    _       _
        3       you     you     PRON    PRP     Case=Acc|Person=2|PronType=Prs  2       pobj    _       SpaceAfter=No
        4       ?       ?       PUNCT   .       PunctType=Peri  2       punct   _       SpaceAfter=No
        ```
        
        For example, parsing a large input file and writing output to a given output file, using four processes:
        
        ```bash
        > parse-as-conll en_core_web_sm spacy --input_file large-input.txt --output_file large-conll-output.txt --include_headers --disable_sbd -j 4
        ```
        
        
        ## Credits
        
        The first version of this library was inspired by initial work by [rgalhama](https://github.com/rgalhama/spaCy2CoNLLU)
         and has evolved a lot since then.
        
Keywords: nlp spacy spacy-extension conll conllu tagging parsing stanza spacy_stanza udpipe spacy_udpipe
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Text Processing
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: pd
Provides-Extra: parsers
Provides-Extra: all
Provides-Extra: dev
