spacy-crfsuite 1.1.0

spaCy pipeline component for CRF entity extraction

spacy_crfsuite: CRF entity tagger for spaCy.

✨ 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

Standalone usage

from spacy_crfsuite import CRFExtractor, prepare_example

crf_extractor = CRFExtractor().from_disk("model.pkl")
raw_example = {"text": "show mexican restaurents up north"}
example = prepare_example(raw_example, crf_extractor=crf_extractor)
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}]

Usage as a spaCy pipeline component

import spacy

from spacy_crfsuite import CRFEntityExtractor

nlp = spacy.blank('en')
pipe = CRFEntityExtractor(nlp).from_disk("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

Follow this notebook to learn how to train a entity tagger from few restaurant search examples.

Pre-trained model

You can download a pre-trained model.

Dataset	Size	📥 Download (zipped)
CoNLL03	1.2 MB	part 1

Below is another usage example.

import spacy

from spacy_crfsuite import CRFEntityExtractor, CRFExtractor

crf_extractor = CRFExtractor().from_disk("spacy_crfsuite_conll03.bz2")

nlp = spacy.blank("en")

pipe = CRFEntityExtractor(nlp, crf_extractor=crf_extractor)
nlp.add_pipe(pipe)

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:

Command Line Interface

Model training

$ python -m spacy_crfsuite.train examples/restaurent_search.md -c examples/default-config.json -o model/
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 blank: 'en'
ℹ Training entity tagger with CRF.
ℹ Saving model to disk
✔ Successfully saved model to file.
model/model.pkl

Evaluation (F1 & Classification report)

$ python -m spacy_crfsuite.eval examples/restaurent_search.md -m model/model.pkl
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.
⚠ 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

Tips & tricks

Use the .explain() method to understand model decision.

print(crf_extractor.explain())

# Output:
#
# Most likely transitions:
# O          -> O          1.617362
# U-cuisine  -> O          1.277659
# B-cuisine  -> I-cuisine  1.206597
# I-cuisine  -> L-cuisine  0.800963
# O          -> U-location 0.719703
# B-cuisine  -> L-cuisine  0.589600
# O          -> U-cuisine  0.402591
# U-location -> U-cuisine  0.325804
# O          -> B-cuisine  0.150878
# L-cuisine  -> O          0.087336
# 
# Positive features:
# 2.186071 O          0:bias:bias
# 1.973212 U-location -1:low:the
# 1.135395 B-cuisine  -1:low:for
# 1.121395 U-location 0:prefix5:centr
# 1.121395 U-location 0:prefix2:ce
# 1.106081 U-location 0:digit
# 1.019241 U-cuisine  0:prefix5:chine
# 1.019241 U-cuisine  0:prefix2:ch
# 1.011240 U-cuisine  0:suffix2:an
# 0.945071 U-cuisine  -1:low:me

Development

Set up pip & virtualenv

$ pipenv sync -d

Run unit test

$ pipenv run pytest

Run black (code formatter)

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