spaCy/spacy/pipeline/entity_linker.py

from typing import Optional, Iterable, Callable, Dict, Sequence, Union, List, Any
from typing import cast
from numpy import dtype
from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
from pathlib import Path
from itertools import islice
import srsly
import random
from thinc.api import CosineDistance, Model, Optimizer, Config
from thinc.api import set_dropout_rate

from ..kb import KnowledgeBase, Candidate
from ..ml import empty_kb
from ..tokens import Doc, Span
from .pipe import deserialize_config
from .trainable_pipe import TrainablePipe
from ..language import Language
from ..vocab import Vocab
from ..training import Example, validate_examples, validate_get_examples
from ..errors import Errors
from ..util import SimpleFrozenList, registry
from .. import util
from ..scorer import Scorer


ActivationsT = Dict[str, Union[List[Ragged], List[str]]]

KNOWLEDGE_BASE_IDS = "kb_ids"

default_model_config = """
[model]
@architectures = "spacy.EntityLinker.v2"

[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v2"
pretrained_vectors = null
width = 96
depth = 2
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
"""
DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]


@Language.factory(
    "entity_linker",
    requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
    assigns=["token.ent_kb_id"],
    default_config={
        "model": DEFAULT_NEL_MODEL,
        "labels_discard": [],
        "n_sents": 0,
        "incl_prior": True,
        "incl_context": True,
        "entity_vector_length": 64,
        "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
        "get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
        "overwrite": False,
        "generate_empty_kb": {"@misc": "spacy.EmptyKB.v2"},
        "scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
        "use_gold_ents": True,
        "candidates_batch_size": 1,
        "threshold": None,
        "save_activations": False,
    },
    default_score_weights={
        "nel_micro_f": 1.0,
        "nel_micro_r": None,
        "nel_micro_p": None,
    },
)
def make_entity_linker(
    nlp: Language,
    name: str,
    model: Model,
    *,
    labels_discard: Iterable[str],
    n_sents: int,
    incl_prior: bool,
    incl_context: bool,
    entity_vector_length: int,
    get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
    get_candidates_batch: Callable[
        [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
    ],
    generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
    overwrite: bool,
    scorer: Optional[Callable],
    use_gold_ents: bool,
    candidates_batch_size: int,
    threshold: Optional[float] = None,
    save_activations: bool,
):
    """Construct an EntityLinker component.

    model (Model[List[Doc], Floats2d]): A model that learns document vector
        representations. Given a batch of Doc objects, it should return a single
        array, with one row per item in the batch.
    labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
    n_sents (int): The number of neighbouring sentences to take into account.
    incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
    incl_context (bool): Whether or not to include the local context in the model.
    entity_vector_length (int): Size of encoding vectors in the KB.
    get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
        produces a list of candidates, given a certain knowledge base and a textual mention.
    get_candidates_batch (
        Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
        ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
    generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
    scorer (Optional[Callable]): The scoring method.
    use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
        component must provide entity annotations.
    candidates_batch_size (int): Size of batches for entity candidate generation.
    threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
        prediction is discarded. If None, predictions are not filtered by any threshold.
    save_activations (bool): save model activations in Doc when annotating.
    """

    if not model.attrs.get("include_span_maker", False):
        try:
            from spacy_legacy.components.entity_linker import EntityLinker_v1
        except:
            raise ImportError(
                "In order to use v1 of the EntityLinker, you must use spacy-legacy>=3.0.12."
            )
        # The only difference in arguments here is that use_gold_ents and threshold aren't available.
        return EntityLinker_v1(
            nlp.vocab,
            model,
            name,
            labels_discard=labels_discard,
            n_sents=n_sents,
            incl_prior=incl_prior,
            incl_context=incl_context,
            entity_vector_length=entity_vector_length,
            get_candidates=get_candidates,
            overwrite=overwrite,
            scorer=scorer,
        )
    return EntityLinker(
        nlp.vocab,
        model,
        name,
        labels_discard=labels_discard,
        n_sents=n_sents,
        incl_prior=incl_prior,
        incl_context=incl_context,
        entity_vector_length=entity_vector_length,
        get_candidates=get_candidates,
        get_candidates_batch=get_candidates_batch,
        generate_empty_kb=generate_empty_kb,
        overwrite=overwrite,
        scorer=scorer,
        use_gold_ents=use_gold_ents,
        candidates_batch_size=candidates_batch_size,
        threshold=threshold,
        save_activations=save_activations,
    )


def entity_linker_score(examples, **kwargs):
    return Scorer.score_links(examples, negative_labels=[EntityLinker.NIL], **kwargs)


@registry.scorers("spacy.entity_linker_scorer.v1")
def make_entity_linker_scorer():
    return entity_linker_score


class EntityLinker(TrainablePipe):
    """Pipeline component for named entity linking.

    DOCS: https://spacy.io/api/entitylinker
    """

    NIL = "NIL"  # string used to refer to a non-existing link

    def __init__(
        self,
        vocab: Vocab,
        model: Model,
        name: str = "entity_linker",
        *,
        labels_discard: Iterable[str],
        n_sents: int,
        incl_prior: bool,
        incl_context: bool,
        entity_vector_length: int,
        get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
        get_candidates_batch: Callable[
            [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
        ],
        generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
        overwrite: bool = False,
        scorer: Optional[Callable] = entity_linker_score,
        use_gold_ents: bool,
        candidates_batch_size: int,
        threshold: Optional[float] = None,
        save_activations: bool = False,
    ) -> None:
        """Initialize an entity linker.

        vocab (Vocab): The shared vocabulary.
        model (thinc.api.Model): The Thinc Model powering the pipeline component.
        name (str): The component instance name, used to add entries to the
            losses during training.
        labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
        n_sents (int): The number of neighbouring sentences to take into account.
        incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
        incl_context (bool): Whether or not to include the local context in the model.
        entity_vector_length (int): Size of encoding vectors in the KB.
        get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
            produces a list of candidates, given a certain knowledge base and a textual mention.
        get_candidates_batch (
            Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
            Iterable[Candidate]]
            ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
        generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
        overwrite (bool): Whether to overwrite existing non-empty annotations.
        scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
        use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
            component must provide entity annotations.
        candidates_batch_size (int): Size of batches for entity candidate generation.
        threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the
            threshold, prediction is discarded. If None, predictions are not filtered by any threshold.
        save_activations (bool): save model activations in Doc when annotating.
        DOCS: https://spacy.io/api/entitylinker#init
        """

        if threshold is not None and not (0 <= threshold <= 1):
            raise ValueError(
                Errors.E1043.format(
                    range_start=0,
                    range_end=1,
                    value=threshold,
                )
            )

        self.vocab = vocab
        self.model = model
        self.name = name
        self.labels_discard = list(labels_discard)
        # how many neighbour sentences to take into account
        self.n_sents = n_sents
        self.incl_prior = incl_prior
        self.incl_context = incl_context
        self.get_candidates = get_candidates
        self.get_candidates_batch = get_candidates_batch
        self.cfg: Dict[str, Any] = {"overwrite": overwrite}
        self.distance = CosineDistance(normalize=False)
        self.kb = generate_empty_kb(self.vocab, entity_vector_length)
        self.scorer = scorer
        self.use_gold_ents = use_gold_ents
        self.candidates_batch_size = candidates_batch_size
        self.threshold = threshold
        self.save_activations = save_activations

        if candidates_batch_size < 1:
            raise ValueError(Errors.E1044)

    def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
        """Define the KB of this pipe by providing a function that will
        create it using this object's vocab."""
        if not callable(kb_loader):
            raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))

        self.kb = kb_loader(self.vocab)  # type: ignore

    def validate_kb(self) -> None:
        # Raise an error if the knowledge base is not initialized.
        if self.kb is None:
            raise ValueError(Errors.E1018.format(name=self.name))
        if hasattr(self.kb, "is_empty") and self.kb.is_empty():
            raise ValueError(Errors.E139.format(name=self.name))

    def initialize(
        self,
        get_examples: Callable[[], Iterable[Example]],
        *,
        nlp: Optional[Language] = None,
        kb_loader: Optional[Callable[[Vocab], KnowledgeBase]] = None,
    ):
        """Initialize the pipe for training, using a representative set
        of data examples.

        get_examples (Callable[[], Iterable[Example]]): Function that
            returns a representative sample of gold-standard Example objects.
        nlp (Language): The current nlp object the component is part of.
        kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab
            instance. Note that providing this argument will overwrite all data accumulated in the current KB.
            Use this only when loading a KB as-such from file.

        DOCS: https://spacy.io/api/entitylinker#initialize
        """
        validate_get_examples(get_examples, "EntityLinker.initialize")
        if kb_loader is not None:
            self.set_kb(kb_loader)
        self.validate_kb()
        nO = self.kb.entity_vector_length
        doc_sample = []
        vector_sample = []
        for eg in islice(get_examples(), 10):
            doc = eg.x
            if self.use_gold_ents:
                ents, _ = eg.get_aligned_ents_and_ner()
                doc.ents = ents
            doc_sample.append(doc)
            vector_sample.append(self.model.ops.alloc1f(nO))
        assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
        assert len(vector_sample) > 0, Errors.E923.format(name=self.name)

        # XXX In order for size estimation to work, there has to be at least
        # one entity. It's not used for training so it doesn't have to be real,
        # so we add a fake one if none are present.
        # We can't use Doc.has_annotation here because it can be True for docs
        # that have been through an NER component but got no entities.
        has_annotations = any([doc.ents for doc in doc_sample])
        if not has_annotations:
            doc = doc_sample[0]
            ent = doc[0:1]
            ent.label_ = "XXX"
            doc.ents = (ent,)

        self.model.initialize(
            X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
        )

        if not has_annotations:
            # Clean up dummy annotation
            doc.ents = []

    def batch_has_learnable_example(self, examples):
        """Check if a batch contains a learnable example.

        If one isn't present, then the update step needs to be skipped.
        """

        for eg in examples:
            for ent in eg.predicted.ents:
                candidates = list(self.get_candidates(self.kb, ent))
                if candidates:
                    return True

        return False

    def update(
        self,
        examples: Iterable[Example],
        *,
        drop: float = 0.0,
        sgd: Optional[Optimizer] = None,
        losses: Optional[Dict[str, float]] = None,
    ) -> Dict[str, float]:
        """Learn from a batch of documents and gold-standard information,
        updating the pipe's model. Delegates to predict and get_loss.

        examples (Iterable[Example]): A batch of Example objects.
        drop (float): The dropout rate.
        sgd (thinc.api.Optimizer): The optimizer.
        losses (Dict[str, float]): Optional record of the loss during training.
            Updated using the component name as the key.
        RETURNS (Dict[str, float]): The updated losses dictionary.

        DOCS: https://spacy.io/api/entitylinker#update
        """
        self.validate_kb()
        if losses is None:
            losses = {}
        losses.setdefault(self.name, 0.0)
        if not examples:
            return losses
        validate_examples(examples, "EntityLinker.update")

        set_dropout_rate(self.model, drop)
        docs = [eg.predicted for eg in examples]
        # save to restore later
        old_ents = [doc.ents for doc in docs]

        for doc, ex in zip(docs, examples):
            if self.use_gold_ents:
                ents, _ = ex.get_aligned_ents_and_ner()
                doc.ents = ents
            else:
                # only keep matching ents
                doc.ents = ex.get_matching_ents()

        # make sure we have something to learn from, if not, short-circuit
        if not self.batch_has_learnable_example(examples):
            return losses

        sentence_encodings, bp_context = self.model.begin_update(docs)

        # now restore the ents
        for doc, old in zip(docs, old_ents):
            doc.ents = old

        loss, d_scores = self.get_loss(
            sentence_encodings=sentence_encodings, examples=examples
        )
        bp_context(d_scores)
        if sgd is not None:
            self.finish_update(sgd)
        losses[self.name] += loss
        return losses

    def get_loss(self, examples: Iterable[Example], sentence_encodings: Floats2d):
        validate_examples(examples, "EntityLinker.get_loss")
        entity_encodings = []
        eidx = 0  # indices in gold entities to keep
        keep_ents = []  # indices in sentence_encodings to keep

        for eg in examples:
            kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)

            for ent in eg.get_matching_ents():
                kb_id = kb_ids[ent.start]
                if kb_id:
                    entity_encoding = self.kb.get_vector(kb_id)
                    entity_encodings.append(entity_encoding)
                    keep_ents.append(eidx)

                eidx += 1
        entity_encodings = self.model.ops.asarray2f(entity_encodings, dtype="float32")
        selected_encodings = sentence_encodings[keep_ents]

        # if there are no matches, short circuit
        if not keep_ents:
            out = self.model.ops.alloc2f(*sentence_encodings.shape)
            return 0, out

        if selected_encodings.shape != entity_encodings.shape:
            err = Errors.E147.format(
                method="get_loss", msg="gold entities do not match up"
            )
            raise RuntimeError(err)
        gradients = self.distance.get_grad(selected_encodings, entity_encodings)
        # to match the input size, we need to give a zero gradient for items not in the kb
        out = self.model.ops.alloc2f(*sentence_encodings.shape)
        out[keep_ents] = gradients

        loss = self.distance.get_loss(selected_encodings, entity_encodings)
        loss = loss / len(entity_encodings)
        return float(loss), out

    def predict(self, docs: Iterable[Doc]) -> ActivationsT:
        """Apply the pipeline's model to a batch of docs, without modifying them.
        Returns the KB IDs for each entity in each doc, including NIL if there is
        no prediction.

        docs (Iterable[Doc]): The documents to predict.
        RETURNS (List[str]): The models prediction for each document.

        DOCS: https://spacy.io/api/entitylinker#predict
        """
        self.validate_kb()
        entity_count = 0
        final_kb_ids: List[str] = []
        ops = self.model.ops
        xp = ops.xp
        docs_ents: List[Ragged] = []
        docs_scores: List[Ragged] = []
        if not docs:
            return {
                KNOWLEDGE_BASE_IDS: final_kb_ids,
                "ents": docs_ents,
                "scores": docs_scores,
            }
        if isinstance(docs, Doc):
            docs = [docs]
        for doc in docs:
            doc_ents: List[Ints1d] = []
            doc_scores: List[Floats1d] = []
            if len(doc) == 0:
                docs_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0)))
                docs_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0)))
                continue
            sentences = [s for s in doc.sents]

            # Loop over entities in batches.
            for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
                ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]

                # Look up candidate entities.
                valid_ent_idx = [
                    idx
                    for idx in range(len(ent_batch))
                    if ent_batch[idx].label_ not in self.labels_discard
                ]

                batch_candidates = list(
                    self.get_candidates_batch(
                        self.kb, [ent_batch[idx] for idx in valid_ent_idx]
                    )
                    if self.candidates_batch_size > 1
                    else [
                        self.get_candidates(self.kb, ent_batch[idx])
                        for idx in valid_ent_idx
                    ]
                )

                # Looping through each entity in batch (TODO: rewrite)
                for j, ent in enumerate(ent_batch):
                    sent_index = sentences.index(ent.sent)
                    assert sent_index >= 0

                    if self.incl_context:
                        # get n_neighbour sentences, clipped to the length of the document
                        start_sentence = max(0, sent_index - self.n_sents)
                        end_sentence = min(
                            len(sentences) - 1, sent_index + self.n_sents
                        )
                        start_token = sentences[start_sentence].start
                        end_token = sentences[end_sentence].end
                        sent_doc = doc[start_token:end_token].as_doc()
                        # currently, the context is the same for each entity in a sentence (should be refined)
                        sentence_encoding = self.model.predict([sent_doc])[0]
                        sentence_encoding_t = sentence_encoding.T
                        sentence_norm = xp.linalg.norm(sentence_encoding_t)
                    entity_count += 1
                    if ent.label_ in self.labels_discard:
                        # ignoring this entity - setting to NIL
                        final_kb_ids.append(self.NIL)
                        self._add_activations(
                            doc_scores=doc_scores,
                            doc_ents=doc_ents,
                            scores=[0.0],
                            ents=[0],
                        )
                    else:
                        candidates = list(batch_candidates[j])
                        if not candidates:
                            # no prediction possible for this entity - setting to NIL
                            final_kb_ids.append(self.NIL)
                            self._add_activations(
                                doc_scores=doc_scores,
                                doc_ents=doc_ents,
                                scores=[0.0],
                                ents=[0],
                            )
                        elif len(candidates) == 1 and self.threshold is None:
                            # shortcut for efficiency reasons: take the 1 candidate
                            final_kb_ids.append(candidates[0].entity_)
                            self._add_activations(
                                doc_scores=doc_scores,
                                doc_ents=doc_ents,
                                scores=[1.0],
                                ents=[candidates[0].entity_],
                            )
                        else:
                            random.shuffle(candidates)
                            # set all prior probabilities to 0 if incl_prior=False
                            prior_probs = xp.asarray([c.prior_prob for c in candidates])
                            if not self.incl_prior:
                                prior_probs = xp.asarray([0.0 for _ in candidates])
                            scores = prior_probs
                            # add in similarity from the context
                            if self.incl_context:
                                entity_encodings = xp.asarray(
                                    [c.entity_vector for c in candidates]
                                )
                                entity_norm = xp.linalg.norm(entity_encodings, axis=1)
                                if len(entity_encodings) != len(prior_probs):
                                    raise RuntimeError(
                                        Errors.E147.format(
                                            method="predict",
                                            msg="vectors not of equal length",
                                        )
                                    )
                                # cosine similarity
                                sims = xp.dot(entity_encodings, sentence_encoding_t) / (
                                    sentence_norm * entity_norm
                                )
                                if sims.shape != prior_probs.shape:
                                    raise ValueError(Errors.E161)
                                scores = prior_probs + sims - (prior_probs * sims)
                            final_kb_ids.append(
                                candidates[scores.argmax().item()].entity_
                                if self.threshold is None
                                or scores.max() >= self.threshold
                                else EntityLinker.NIL
                            )
                            self._add_activations(
                                doc_scores=doc_scores,
                                doc_ents=doc_ents,
                                scores=scores,
                                ents=[c.entity for c in candidates],
                            )
            self._add_doc_activations(
                docs_scores=docs_scores,
                docs_ents=docs_ents,
                doc_scores=doc_scores,
                doc_ents=doc_ents,
            )
        if not (len(final_kb_ids) == entity_count):
            err = Errors.E147.format(
                method="predict", msg="result variables not of equal length"
            )
            raise RuntimeError(err)
        return {
            KNOWLEDGE_BASE_IDS: final_kb_ids,
            "ents": docs_ents,
            "scores": docs_scores,
        }

    def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None:
        """Modify a batch of documents, using pre-computed scores.

        docs (Iterable[Doc]): The documents to modify.
        activations (ActivationsT): The activations used for setting annotations, produced
                                 by EntityLinker.predict.

        DOCS: https://spacy.io/api/entitylinker#set_annotations
        """
        kb_ids = cast(List[str], activations[KNOWLEDGE_BASE_IDS])
        count_ents = len([ent for doc in docs for ent in doc.ents])
        if count_ents != len(kb_ids):
            raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
        i = 0
        overwrite = self.cfg["overwrite"]
        for j, doc in enumerate(docs):
            if self.save_activations:
                doc.activations[self.name] = {}
                for act_name, acts in activations.items():
                    if act_name != KNOWLEDGE_BASE_IDS:
                        # We only copy activations that are Ragged.
                        doc.activations[self.name][act_name] = cast(Ragged, acts[j])

            for ent in doc.ents:
                kb_id = kb_ids[i]
                i += 1
                for token in ent:
                    if token.ent_kb_id == 0 or overwrite:
                        token.ent_kb_id_ = kb_id

    def to_bytes(self, *, exclude=tuple()):
        """Serialize the pipe to a bytestring.

        exclude (Iterable[str]): String names of serialization fields to exclude.
        RETURNS (bytes): The serialized object.

        DOCS: https://spacy.io/api/entitylinker#to_bytes
        """
        self._validate_serialization_attrs()
        serialize = {}
        if hasattr(self, "cfg") and self.cfg is not None:
            serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
        serialize["vocab"] = lambda: self.vocab.to_bytes(exclude=exclude)
        serialize["kb"] = self.kb.to_bytes
        serialize["model"] = self.model.to_bytes
        return util.to_bytes(serialize, exclude)

    def from_bytes(self, bytes_data, *, exclude=tuple()):
        """Load the pipe from a bytestring.

        exclude (Iterable[str]): String names of serialization fields to exclude.
        RETURNS (TrainablePipe): The loaded object.

        DOCS: https://spacy.io/api/entitylinker#from_bytes
        """
        self._validate_serialization_attrs()

        def load_model(b):
            try:
                self.model.from_bytes(b)
            except AttributeError:
                raise ValueError(Errors.E149) from None

        deserialize = {}
        if hasattr(self, "cfg") and self.cfg is not None:
            deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b))
        deserialize["vocab"] = lambda b: self.vocab.from_bytes(b, exclude=exclude)
        deserialize["kb"] = lambda b: self.kb.from_bytes(b)
        deserialize["model"] = load_model
        util.from_bytes(bytes_data, deserialize, exclude)
        return self

    def to_disk(
        self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
    ) -> None:
        """Serialize the pipe to disk.

        path (str / Path): Path to a directory.
        exclude (Iterable[str]): String names of serialization fields to exclude.

        DOCS: https://spacy.io/api/entitylinker#to_disk
        """
        serialize = {}
        serialize["vocab"] = lambda p: self.vocab.to_disk(p, exclude=exclude)
        serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
        serialize["kb"] = lambda p: self.kb.to_disk(p)
        serialize["model"] = lambda p: self.model.to_disk(p)
        util.to_disk(path, serialize, exclude)

    def from_disk(
        self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
    ) -> "EntityLinker":
        """Load the pipe from disk. Modifies the object in place and returns it.

        path (str / Path): Path to a directory.
        exclude (Iterable[str]): String names of serialization fields to exclude.
        RETURNS (EntityLinker): The modified EntityLinker object.

        DOCS: https://spacy.io/api/entitylinker#from_disk
        """

        def load_model(p):
            try:
                with p.open("rb") as infile:
                    self.model.from_bytes(infile.read())
            except AttributeError:
                raise ValueError(Errors.E149) from None

        deserialize: Dict[str, Callable[[Any], Any]] = {}
        deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p))
        deserialize["vocab"] = lambda p: self.vocab.from_disk(p, exclude=exclude)
        deserialize["kb"] = lambda p: self.kb.from_disk(p)
        deserialize["model"] = load_model
        util.from_disk(path, deserialize, exclude)
        return self

    def rehearse(self, examples, *, sgd=None, losses=None, **config):
        raise NotImplementedError

    def add_label(self, label):
        raise NotImplementedError

    def _add_doc_activations(
        self,
        *,
        docs_scores: List[Ragged],
        docs_ents: List[Ragged],
        doc_scores: List[Floats1d],
        doc_ents: List[Ints1d],
    ):
        if not self.save_activations:
            return
        ops = self.model.ops
        lengths = ops.asarray1i([s.shape[0] for s in doc_scores])
        docs_scores.append(Ragged(ops.flatten(doc_scores), lengths))
        docs_ents.append(Ragged(ops.flatten(doc_ents), lengths))

    def _add_activations(
        self,
        *,
        doc_scores: List[Floats1d],
        doc_ents: List[Ints1d],
        scores: Sequence[float],
        ents: Sequence[int],
    ):
        if not self.save_activations:
            return
        ops = self.model.ops
        doc_scores.append(ops.asarray1f(scores))
        doc_ents.append(ops.asarray1i(ents, dtype="uint64"))