spacy-crfsuite 1.3.0

spaCy pipeline component for CRF entity extraction

spacy_crfsuite: CRF tagger for spaCy.

Sequence tagging with spaCy and crfsuite.

Copied from Rasa NLU.

✨ Features

• Simple but tough to beat CRF entity tagger (via sklearn-crfsuite)
• spaCy NER component
• Command line interface for training & evaluation and example notebook
• CoNLL, JSON and Markdown annotations
• Pre-trained NER component

⏳ Installation

pip install spacy_crfsuite

🚀 Quickstart

Usage as a spaCy pipeline component

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	82%	spacy_crfsuite_conll03_sm.bz2

Train your own model

Let's train a simple model for restaurent search bot with markdown annotations and the command line. You can also try this notebook.

So we start by training a model and saving it to disk.

$ 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.

$ 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!

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.

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.

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

$ pipenv sync -d

Run unit test

$ pipenv run pytest

Run black (code formatting)

$ pipenv run black spacy_crfsuite/ --config=pyproject.toml