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fix NEL config and IO, and n_sents functionality (#7100)
* fix NEL config and IO, and n_sents functionality * add docs * fix test
This commit is contained in:
Sofie Van Landeghem
GitHub
parent
113e8d082b
commit
b92f81d5da
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@ -45,6 +45,7 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
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default_config={
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"model": DEFAULT_NEL_MODEL,
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"labels_discard": [],
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"n_sents": 0,
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"incl_prior": True,
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"incl_context": True,
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"entity_vector_length": 64,
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@ -62,6 +63,7 @@ def make_entity_linker(
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model: Model,
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*,
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labels_discard: Iterable[str],
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n_sents: int,
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incl_prior: bool,
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incl_context: bool,
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entity_vector_length: int,
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@ -73,6 +75,7 @@ def make_entity_linker(
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representations. Given a batch of Doc objects, it should return a single
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array, with one row per item in the batch.
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labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
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n_sents (int): The number of neighbouring sentences to take into account.
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incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
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incl_context (bool): Whether or not to include the local context in the model.
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entity_vector_length (int): Size of encoding vectors in the KB.
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@ -84,6 +87,7 @@ def make_entity_linker(
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model,
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name,
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labels_discard=labels_discard,
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n_sents=n_sents,
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incl_prior=incl_prior,
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incl_context=incl_context,
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entity_vector_length=entity_vector_length,
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@ -106,6 +110,7 @@ class EntityLinker(TrainablePipe):
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name: str = "entity_linker",
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*,
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labels_discard: Iterable[str],
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n_sents: int,
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incl_prior: bool,
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incl_context: bool,
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entity_vector_length: int,
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@ -118,6 +123,7 @@ class EntityLinker(TrainablePipe):
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name (str): The component instance name, used to add entries to the
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losses during training.
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labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
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n_sents (int): The number of neighbouring sentences to take into account.
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incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
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incl_context (bool): Whether or not to include the local context in the model.
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entity_vector_length (int): Size of encoding vectors in the KB.
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@ -129,17 +135,14 @@ class EntityLinker(TrainablePipe):
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self.vocab = vocab
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self.model = model
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self.name = name
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cfg = {
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"labels_discard": list(labels_discard),
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"incl_prior": incl_prior,
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"incl_context": incl_context,
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"entity_vector_length": entity_vector_length,
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}
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self.labels_discard = list(labels_discard)
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self.n_sents = n_sents
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self.incl_prior = incl_prior
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self.incl_context = incl_context
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self.get_candidates = get_candidates
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self.cfg = dict(cfg)
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self.cfg = {}
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self.distance = CosineDistance(normalize=False)
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# how many neightbour sentences to take into account
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self.n_sents = cfg.get("n_sents", 0)
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# create an empty KB by default. If you want to load a predefined one, specify it in 'initialize'.
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self.kb = empty_kb(entity_vector_length)(self.vocab)
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@ -150,7 +153,6 @@ class EntityLinker(TrainablePipe):
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raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))
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self.kb = kb_loader(self.vocab)
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self.cfg["entity_vector_length"] = self.kb.entity_vector_length
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def validate_kb(self) -> None:
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# Raise an error if the knowledge base is not initialized.
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@ -312,14 +314,13 @@ class EntityLinker(TrainablePipe):
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sent_doc = doc[start_token:end_token].as_doc()
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# currently, the context is the same for each entity in a sentence (should be refined)
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xp = self.model.ops.xp
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if self.cfg.get("incl_context"):
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if self.incl_context:
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sentence_encoding = self.model.predict([sent_doc])[0]
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sentence_encoding_t = sentence_encoding.T
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sentence_norm = xp.linalg.norm(sentence_encoding_t)
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for ent in sent.ents:
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entity_count += 1
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to_discard = self.cfg.get("labels_discard", [])
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if to_discard and ent.label_ in to_discard:
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if ent.label_ in self.labels_discard:
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# ignoring this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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else:
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@ -337,13 +338,13 @@ class EntityLinker(TrainablePipe):
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prior_probs = xp.asarray(
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[c.prior_prob for c in candidates]
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)
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if not self.cfg.get("incl_prior"):
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if not self.incl_prior:
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prior_probs = xp.asarray(
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[0.0 for _ in candidates]
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)
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scores = prior_probs
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# add in similarity from the context
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if self.cfg.get("incl_context"):
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if self.incl_context:
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entity_encodings = xp.asarray(
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[c.entity_vector for c in candidates]
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)
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@ -250,6 +250,14 @@ def test_el_pipe_configuration(nlp):
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assert doc[2].ent_kb_id_ == "Q2"
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def test_nel_nsents(nlp):
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"""Test that n_sents can be set through the configuration"""
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entity_linker = nlp.add_pipe("entity_linker", config={})
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assert entity_linker.n_sents == 0
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entity_linker = nlp.replace_pipe("entity_linker", "entity_linker", config={"n_sents": 2})
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assert entity_linker.n_sents == 2
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def test_vocab_serialization(nlp):
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"""Test that string information is retained across storage"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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@ -83,9 +83,9 @@ def test_replace_last_pipe(nlp):
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def test_replace_pipe_config(nlp):
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nlp.add_pipe("entity_linker")
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nlp.add_pipe("sentencizer")
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assert nlp.get_pipe("entity_linker").cfg["incl_prior"] is True
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assert nlp.get_pipe("entity_linker").incl_prior is True
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nlp.replace_pipe("entity_linker", "entity_linker", config={"incl_prior": False})
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assert nlp.get_pipe("entity_linker").cfg["incl_prior"] is False
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assert nlp.get_pipe("entity_linker").incl_prior is False
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@pytest.mark.parametrize("old_name,new_name", [("old_pipe", "new_pipe")])
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@ -31,6 +31,7 @@ architectures and their arguments and hyperparameters.
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> from spacy.pipeline.entity_linker import DEFAULT_NEL_MODEL
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> config = {
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> "labels_discard": [],
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> "n_sents": 0,
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> "incl_prior": True,
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> "incl_context": True,
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> "model": DEFAULT_NEL_MODEL,
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@ -43,6 +44,7 @@ architectures and their arguments and hyperparameters.
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| Setting | Description |
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| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
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| `n_sents` | The number of neighbouring sentences to take into account. Defaults to 0. ~~int~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
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@ -89,6 +91,7 @@ custom knowledge base, you should either call
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| `entity_vector_length` | Size of encoding vectors in the KB. ~~int~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
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| `n_sents` | The number of neighbouring sentences to take into account. ~~int~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
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@ -248,7 +251,7 @@ pipe's entity linking model and context encoder. Delegates to
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> ```
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| Name | Description |
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| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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