# Using a custom component in NER **URL:** \ **Category:** Uncategorized\ **Tags:** done, spacy\ **Created:** [January 21, 2018, 10:41pm UTC](https://support.prodi.gy/t/using-a-custom-component-in-ner/239 "2018-01-21T22:41:43Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![jamesdunham](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/jamesdunham/32/158_2.png) [@jamesdunham](https://support.prodi.gy/u/jamesdunham)\ **Post date:** [January 21, 2018, 10:41pm UTC](https://support.prodi.gy/t/using-a-custom-component-in-ner/239/1 "2018-01-21T22:41:44Z")
I’d like to use a model with a custom component that applies a spacy `Matcher`, but I’m running into an error that I can’t make sense of. Is there a correct way to do this? Thanks in advance. Starting from the [recipe](https://prodi.gy/docs/workflow-named-entity-recognition#recipe) in the NER workflow, I import `split_sentences` from `prodigy.components.preprocess` instead of `prodigy.preprocess`, and then add the lines marked by comments. ```python import spacy from prodigy import recipe, get_stream from prodigy.components.sorters import prefer_uncertain from prodigy.models.ner import EntityRecognizer from prodigy.components.preprocess import split_sentences from spacy.matcher import Matcher # Added @recipe('ner.teach', dataset=("Dataset ID", "positional"), spacy_model=("Loadable spaCy model (for tokenization)"), source=("Source data (file path or API query)"), api=("Optional API loader to use", "option", "a", str), loader=("Optional file loader to use", "option", "lo", str), label=("Label to annotate", "option", "l", str), # Works when False; breaks when True match=("Try to include the custom component", "flag", "m", bool) ) def teach(dataset, spacy_model, source, api=None, loader=None, label='', match=False): """Annotate texts to train a NER model""" nlp = spacy.load(spacy_model) if match: # Added # Try to add a custom component that instantiates a Matcher do_nothing = DoNothing(nlp.vocab) nlp.add_pipe(do_nothing, last=True) model = EntityRecognizer(nlp, label=label) stream = get_stream(source, api, loader) stream = split_sentences(model.orig_nlp, stream) return { 'dataset': dataset, 'view_id': 'ner', 'stream': prefer_uncertain(model(stream), bias=0.8), 'update': model.update, 'config': {'lang': nlp.lang, 'label': label} } class DoNothing(object): """A custom component that uses a Matcher.""" name = 'do_nothing' def __init__ (self, vocab): self.matcher = Matcher(vocab) def __call__ (self, doc): return doc ``` Invoking this recipe with the `-m` flag to put the custom component in the processing pipeline gives: ```python Traceback (most recent call last): File "/usr/lib64/python3.6/runpy.py", line 193, in _run_module_as_main " __main__", mod_spec) File "/usr/lib64/python3.6/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/james/.virtualenvs/pipeline/lib/python3.6/site-packages/prodigy/ __main__.py", line 248, in controller = recipe(*args, use_plac=True) File "cython_src/prodigy/core.pyx", line 150, in prodigy.core.recipe.recipe_decorator.recipe_proxy File "/home/james/.virtualenvs/pipeline/lib/python3.6/site-packages/plac_core.py", line 328, in call cmd, result = parser.consume(arglist) File "/home/james/.virtualenvs/pipeline/lib/python3.6/site-packages/plac_core.py", line 207, in consume return cmd, self.func(*(args + varargs + extraopts), **kwargs) File "mre.py", line 25, in teach model = EntityRecognizer(nlp, label=label) File "cython_src/prodigy/models/ner.pyx", line 143, in prodigy.models.ner.EntityRecognizer. __init__ File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 280, in _reconstruct state = deepcopy(state, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 150, in deepcopy y = copier(x, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 240, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 150, in deepcopy y = copier(x, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 215, in _deepcopy_list append(deepcopy(a, memo)) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 150, in deepcopy y = copier(x, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 220, in _deepcopy_tuple y = [deepcopy(a, memo) for a in x] File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 220, in y = [deepcopy(a, memo) for a in x] File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 280, in _reconstruct state = deepcopy(state, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 150, in deepcopy y = copier(x, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 240, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv) File "/home/james/.virtualenvs/pipeline/lib64/python3.6/copy.py", line 274, in _reconstruct y = func(*args) File "matcher.pyx", line 195, in spacy.matcher.Matcher. __init__ TypeError: __init__ () takes exactly 1 positional argument (2 given) ``` Prodigy info: ```python Version 1.2.0 Location /home/james/.virtualenvs/pipeline/lib/python3.6/site-packages/prodigy Prodigy Home /home/james/.prodigy Platform Linux-4.14.13-300.fc27.x86_64-x86_64-with-fedora-27-Twenty_Seven Python Version 3.6.3 Database Name PostgreSQL Database Id postgresql Total Datasets 6 Total Sessions 67 ``` Spacy info: ```python spaCy version 2.0.5 Location /home/james/.virtualenvs/pipeline/lib/python3.6/site-packages/spacy Platform Linux-4.14.13-300.fc27.x86_64-x86_64-with-fedora-27-Twenty_Seven Python version 3.6.3 Models en ``` ---
**Author:** ![honnibal](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/honnibal/32/35_2.png) [@honnibal](https://support.prodi.gy/u/honnibal)\ **Post date:** [January 24, 2018, 2:46pm UTC](https://support.prodi.gy/t/using-a-custom-component-in-ner/239/2 "2018-01-24T14:46:51Z")
Thanks. This seems to be a spaCy bug related to unpickling the `Matcher` class. The pickle protocol is used in `copy.deepcopy`, which is called to within the entity recognizer. I’ve just fixed the bug in spaCy: [https://github.com/explosion/spaCy/issues/1883](https://github.com/explosion/spaCy/issues/1883) In the meantime, you could work around the bug by not adding the matcher component to your pipeline until you’ve already created the Prodigy `EntityRecognizer` object. ---
**Author:** ![jamesdunham](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/jamesdunham/32/158_2.png) [@jamesdunham](https://support.prodi.gy/u/jamesdunham)\ **Post date:** [January 26, 2018, 5:24am UTC](https://support.prodi.gy/t/using-a-custom-component-in-ner/239/3 "2018-01-26T05:24:35Z")
Okay, thanks! The workaround did it. ---
**Author:** ![akshitasood63](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/akshitasood63/32/455_2.png) [@akshitasood63](https://support.prodi.gy/u/akshitasood63)\ **Post date:** [February 23, 2018, 7:36am UTC](https://support.prodi.gy/t/using-a-custom-component-in-ner/239/4 "2018-02-23T07:36:15Z")
What does bias=0.8 in stream signify? ---
**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\ **Post date:** [February 23, 2018, 11:00am UTC](https://support.prodi.gy/t/using-a-custom-component-in-ner/239/5 "2018-02-23T11:00:35Z")
> [@akshitasood63](#): > > What does bias=0.8 in stream signify? The bias is an optional keyword argument on the sorter functions and lets you tune the bias towards high or low scores – depending on the sorter you're using. The sorter takes a stream of `(score, example)` tuples (usually returned by a model) and yields annotation examples. For example: ```python stream = prefer_uncertain(model(stream), bias=0.8) ``` You can find more details on this in the "Sorters" section of the `PRODIGY_README.html`, which is available for download with Prodigy.