Metadata-Version: 2.1
Name: spacy_crfsuite
Version: 1.3.0
Summary: spaCy pipeline component for CRF entity extraction
Home-page: https://github.com/talmago/spacy_crfsuite
Author: Tal Almagor
Author-email: almagoric@gmail.com
License: MIT
Description: 
        # spacy_crfsuite: CRF tagger for spaCy.
        
        Sequence tagging with spaCy and crfsuite.
        
        Copied from [Rasa NLU](https://github.com/RasaHQ/rasa/blob/master/rasa/nlu/extractors/crf_entity_extractor.py).
        
        ## ✨ Features
        
        - Simple but tough to beat **CRF entity tagger** (via [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite))
        - **spaCy NER component**
        - **Command line interface** for training & evaluation and **example notebook**
        - [CoNLL](https://www.aclweb.org/anthology/W03-0419/), JSON and [Markdown](https://rasa.com/docs/rasa/nlu/training-data-format/#id5) **annotations**
        - Pre-trained NER component 
        
        ## ⏳ Installation
        
        ```bash
        pip install spacy_crfsuite
        ```
        
        ## 🚀 Quickstart
        
        ### Usage as a spaCy pipeline component
        
        ```python
        import spacy
        
        from spacy_crfsuite import CRFEntityExtractor, CRFExtractor
        
        @Language.factory("ner-crf")
        def create_my_component(nlp, name):
            crf_extractor = CRFExtractor().from_disk("spacy_crfsuite_conll03_sm.bz2")
            return CRFEntityExtractor(nlp, crf_extractor=crf_extractor)
        
        
        nlp = spacy.load("en_core_web_sm", disable=["ner"])
        nlp.add_pipe("ner-crf")
        
        doc = nlp(
            "George Walker Bush (born July 6, 1946) is an American politician and businessman "
            "who served as the 43rd president of the United States from 2001 to 2009.")
        
        for ent in doc.ents:
            print(ent, "-", ent.label_)
        
        # Output:
        # George Walker Bush - PER
        # American - MISC
        # United States - LOC
        ```
        
        ### Pre-trained models
        
        You can download a pre-trained model.
        
        | Dataset              |  F1   | 📥 Download                                                                                                                                                                                                                                                                                                   |
        | -------------------- | ------  | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
        | [CoNLL03](https://github.com/talmago/spacy_crfsuite/blob/master/examples/02%20-%20CoNLL%202003.ipynb)            |   82% | [spacy_crfsuite_conll03_sm.bz2](https://github.com/talmago/spacy_crfsuite/releases/download/v1.1.0/spacy_crfsuite_conll03_sm.bz2) |
        
        
        ### Train your own model
        
        Let's train a simple model for restaurent search bot with [markdown 
        annotations](https://github.com/talmago/spacy_crfsuite/blob/master/examples/restaurent_search.md) and the command line. 
        You can also try this [notebook](https://github.com/talmago/spacy_crfsuite/blob/master/examples/01%20-%20Custom%20Component.ipynb).
        
        So we start by training a model and saving it to disk.
        
        ```sh
        $ python -m spacy_crfsuite.train examples/restaurent_search.md -c examples/default-config.json -o model/ -lm en_core_web_sm
        ℹ Loading config from disk
        ✔ Successfully loaded config from file.
        examples/default-config.json
        ℹ Loading training examples.
        ✔ Successfully loaded 15 training examples from file.
        examples/restaurent_search.md
        ℹ Using spaCy model: en_core_web_sm
        ℹ Training entity tagger with CRF.
        ℹ Saving model to disk
        ✔ Successfully saved model to file.
        model/model.pkl
        ```
        
        We can also evaluate on a dev set to get f1 & classification report. Below we use the training examples.
        
        ```sh
        $ python -m spacy_crfsuite.eval examples/restaurent_search.md -m model/model.pkl -lm en_core_web_sm
        ℹ Loading model from file
        model/model.pkl
        ✔ Successfully loaded CRF tagger
        <spacy_crfsuite.crf_extractor.CRFExtractor object at 0x126e5f438>
        ℹ Loading dev dataset from file
        examples/example.md
        ✔ Successfully loaded 15 dev examples.
        ℹ Using spaCy model: en_core_web_sm
        ⚠ f1 score: 1.0
                      precision    recall  f1-score   support
        
           B-cuisine      1.000     1.000     1.000         2
           I-cuisine      1.000     1.000     1.000         1
           L-cuisine      1.000     1.000     1.000         2
           U-cuisine      1.000     1.000     1.000         5
          U-location      1.000     1.000     1.000         7
        
           micro avg      1.000     1.000     1.000        17
           macro avg      1.000     1.000     1.000        17
        weighted avg      1.000     1.000     1.000        17
        ```
        
        Now we can use the tagger in a spaCy pipeline!
        
        ```python
        import spacy
        
        from spacy_crfsuite import CRFEntityExtractor
        
        nlp = spacy.load('en_core_web_sm')
        pipe = CRFEntityExtractor(nlp).from_disk("model/model.pkl")
        nlp.add_pipe(pipe)
        
        doc = nlp("show mexican restaurents up north")
        for ent in doc.ents:
            print(ent.text, "--", ent.label_)
        
        # Output:
        # mexican -- cuisine
        # north -- location
        ```
        
        Or alternatively as a standalone component.
        
        ```python
        from spacy_crfsuite import CRFExtractor
        from spacy_crfsuite.tokenizer import SpacyTokenizer
        
        crf_extractor = CRFExtractor().from_disk("model/model.pkl")
        tokenizer = SpacyTokenizer()
        
        example = {"text": "show mexican restaurents up north"}
        tokenizer.tokenize(example, attribute="text")
        crf_extractor.process(example)
        
        # Output:
        # [{'start': 5,
        #   'end': 12,
        #   'value': 'mexican',
        #   'entity': 'cuisine',
        #   'confidence': 0.5823148506311286},
        #  {'start': 28,
        #   'end': 33,
        #   'value': 'north',
        #   'entity': 'location',
        #   'confidence': 0.8863076478494413}]
        ```
        
        We can also take a look at what model learned.
        
        Use the `.explain()` method to understand model decision.
        
        ```python
        print(crf_extractor.explain())
        
        # Output:
        #
        # Most likely transitions:
        # O          -> O          1.637338
        # B-cuisine  -> I-cuisine  1.373766
        # U-cuisine  -> O          1.306077
        # I-cuisine  -> L-cuisine  0.915989
        # O          -> U-location 0.751463
        # B-cuisine  -> L-cuisine  0.698893
        # O          -> U-cuisine  0.480360
        # U-location -> U-cuisine  0.403487
        # O          -> B-cuisine  0.261450
        # L-cuisine  -> O          0.182695
        # 
        # Positive features:
        # 1.976502 O          0:bias:bias
        # 1.957180 U-location -1:low:the
        # 1.216547 B-cuisine  -1:low:for
        # 1.153924 U-location 0:prefix5:centr
        # 1.153924 U-location 0:prefix2:ce
        # 1.110536 U-location 0:digit
        # 1.058294 U-cuisine  0:prefix5:chine
        # 1.058294 U-cuisine  0:prefix2:ch
        # 1.051457 U-cuisine  0:suffix2:an
        # 0.999976 U-cuisine  -1:low:me
        ```
        
        >**Notice**: You can also access the `crf_extractor` directly with ```nlp.get_pipe("crf_ner").crf_extractor```.
        
        ## Development
        
        Set up virtualenv
        
        ```sh
        $ pipenv sync -d
        ```
        
        Run unit test
        
        ```sh
        $ pipenv run pytest
        ```
        
        Run black (code formatting)
        
        ```sh
        $ pipenv run black spacy_crfsuite/ --config=pyproject.toml
        ```
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3
Description-Content-Type: text/markdown
