Can't add custom component using nlp.add_pipe()

[Larsdegroot]

So i'm using the spacy project for relationship extraction. i've figured out how to train the model with my own training data but now i want to add the trained component using nlp.add_pipe()
the error that i'm getting is:
code:

import scripts.rel_pipe
nlp = spacy.load('en_tox')
nlp.add_pipe("relation_extractor")

error:

ConfigValidationError                     Traceback (most recent call last)
Input In [12], in <cell line: 2>()
      1 nlp = spacy.load('en_tox')
----> 2 nlp.add_pipe("relation_extractor")
      3 nlp.pipe_names

File ~\anaconda3\lib\site-packages\spacy\language.py:792, in Language.add_pipe(self, factory_name, name, before, after, first, last, source, config, raw_config, validate)
    784     if not self.has_factory(factory_name):
    785         err = Errors.E002.format(
    786             name=factory_name,
    787             opts=", ".join(self.factory_names),
   (...)
    790             lang_code=self.lang,
    791         )
--> 792     pipe_component = self.create_pipe(
    793         factory_name,
    794         name=name,
    795         config=config,
    796         raw_config=raw_config,
    797         validate=validate,
    798     )
    799 pipe_index = self._get_pipe_index(before, after, first, last)
    800 self._pipe_meta[name] = self.get_factory_meta(factory_name)

File ~\anaconda3\lib\site-packages\spacy\language.py:674, in Language.create_pipe(self, factory_name, name, config, raw_config, validate)
    671 cfg = {factory_name: config}
    672 # We're calling the internal _fill here to avoid constructing the
    673 # registered functions twice
--> 674 resolved = registry.resolve(cfg, validate=validate)
    675 filled = registry.fill({"cfg": cfg[factory_name]}, validate=validate)["cfg"]
    676 filled = Config(filled)

File ~\anaconda3\lib\site-packages\thinc\config.py:746, in registry.resolve(cls, config, schema, overrides, validate)
    737 @classmethod
    738 def resolve(
    739     cls,
   (...)
    744     validate: bool = True,
    745 ) -> Dict[str, Any]:
--> 746     resolved, _ = cls._make(
    747         config, schema=schema, overrides=overrides, validate=validate, resolve=True
    748     )
    749     return resolved

File ~\anaconda3\lib\site-packages\thinc\config.py:795, in registry._make(cls, config, schema, overrides, resolve, validate)
    793 if not is_interpolated:
    794     config = Config(orig_config).interpolate()
--> 795 filled, _, resolved = cls._fill(
    796     config, schema, validate=validate, overrides=overrides, resolve=resolve
    797 )
    798 filled = Config(filled, section_order=section_order)
    799 # Check that overrides didn't include invalid properties not in config

File ~\anaconda3\lib\site-packages\thinc\config.py:850, in registry._fill(cls, config, schema, validate, resolve, parent, overrides)
    848     schema.__fields__[key] = copy_model_field(field, Any)
    849 promise_schema = cls.make_promise_schema(value, resolve=resolve)
--> 850 filled[key], validation[v_key], final[key] = cls._fill(
    851     value,
    852     promise_schema,
    853     validate=validate,
    854     resolve=resolve,
    855     parent=key_parent,
    856     overrides=overrides,
    857 )
    858 reg_name, func_name = cls.get_constructor(final[key])
    859 args, kwargs = cls.parse_args(final[key])

File ~\anaconda3\lib\site-packages\thinc\config.py:916, in registry._fill(cls, config, schema, validate, resolve, parent, overrides)
    914         result = schema.parse_obj(validation)
    915     except ValidationError as e:
--> 916         raise ConfigValidationError(
    917             config=config, errors=e.errors(), parent=parent
    918         ) from None
    919 else:
    920     # Same as parse_obj, but without validation
    921     result = schema.construct(**validation)

ConfigValidationError: 

Config validation error

relation_extractor -> model      field required
relation_extractor -> threshold   field required

{'nlp': <spacy.lang.en.English object at 0x0000028F87BCEF10>, 'name': 'relation_extractor', '@factories': 'relation_extractor'}

what i think is happening is that i've just imported the rel_pipe.py file, while the arguments for model and threshold reside in the config file. however i don't know the proper way to load those in.

[polm]

Sorry this isn't very clear. What's happening is that for built-in spaCy components, there's a default config that will be used as a fallback for values, but the rel component doesn't have a default config, so you have to provide everything. Here's one way to do that - I used the sample config as a base and set this up.

#import scripts.rel_pipe
import spacy
import scripts.custom_functions
from thinc.api import Config

config_str = """
[components]
[components.relation_extractor]
threshold = 0.5

[components.relation_extractor.model]
@architectures = "rel_model.v1"

[components.relation_extractor.model.create_instance_tensor]
@architectures = "rel_instance_tensor.v1"

[components.relation_extractor.model.create_instance_tensor.tok2vec]
@architectures = "spacy.Tok2VecListener.v1"
width = 96

[components.relation_extractor.model.create_instance_tensor.pooling]
@layers = "reduce_mean.v1"

[components.relation_extractor.model.create_instance_tensor.get_instances]
@misc = "rel_instance_generator.v1"
max_length = 100

[components.relation_extractor.model.classification_layer]
@architectures = "rel_classification_layer.v1"
nI = null
nO = null
"""


config = Config().from_str(config_str)
rel_config = config["components"]["relation_extractor"]
nlp = spacy.blank("en")
nlp.add_pipe("relation_extractor", config=rel_config)
print("ok")

Some changes I had to make:

1. remove the factory
2. remove the interpolated width and replace it with the literal 96