Merge remote-tracking branch 'upstream/develop' into fix/cli-debug

# Conflicts:
#	pyproject.toml
#	requirements.txt
#	setup.cfg

bin/ud/ud_train.py

@@ -16,7 +16,7 @@ from bin.ud import conll17_ud_eval
 from spacy.tokens import Token, Doc
 from spacy.gold import Example
 from spacy.util import compounding, minibatch, minibatch_by_words
-from spacy.syntax.nonproj import projectivize
+from spacy.pipeline._parser_internals.nonproj import projectivize
 from spacy.matcher import Matcher
 from spacy import displacy
 from collections import defaultdict

examples/training/conllu.py

@@ -13,7 +13,7 @@ import spacy
 import spacy.util
 from spacy.tokens import Token, Doc
 from spacy.gold import Example
-from spacy.syntax.nonproj import projectivize
+from spacy.pipeline._parser_internals.nonproj import projectivize
 from collections import defaultdict
 from spacy.matcher import Matcher
 

setup.py

@@ -31,6 +31,7 @@ MOD_NAMES = [
     "spacy.vocab",
     "spacy.attrs",
     "spacy.kb",
+    "spacy.ml.parser_model",
     "spacy.morphology",
     "spacy.pipeline.dep_parser",
     "spacy.pipeline.morphologizer",
@@ -40,14 +41,14 @@ MOD_NAMES = [
     "spacy.pipeline.sentencizer",
     "spacy.pipeline.senter",
     "spacy.pipeline.tagger",
-    "spacy.syntax.stateclass",
+    "spacy.pipeline.transition_parser",
-    "spacy.syntax._state",
+    "spacy.pipeline._parser_internals.arc_eager",
+    "spacy.pipeline._parser_internals.ner",
+    "spacy.pipeline._parser_internals.nonproj",
+    "spacy.pipeline._parser_internals._state",
+    "spacy.pipeline._parser_internals.stateclass",
+    "spacy.pipeline._parser_internals.transition_system",
     "spacy.tokenizer",
-    "spacy.syntax.nn_parser",
-    "spacy.syntax._parser_model",
-    "spacy.syntax.nonproj",
-    "spacy.syntax.transition_system",
-    "spacy.syntax.arc_eager",
     "spacy.gold.gold_io",
     "spacy.tokens.doc",
     "spacy.tokens.span",
@@ -57,7 +58,6 @@ MOD_NAMES = [
     "spacy.matcher.matcher",
     "spacy.matcher.phrasematcher",
     "spacy.matcher.dependencymatcher",
-    "spacy.syntax.ner",
     "spacy.symbols",
     "spacy.vectors",
 ]

spacy/cli/debug_data.py

@@ -10,7 +10,7 @@ from thinc.api import Config
 from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
 from ._util import import_code, debug_cli
 from ..gold import Corpus, Example
-from ..syntax import nonproj
+from ..pipeline._parser_internals import nonproj
 from ..language import Language
 from .. import util
 

spacy/errors.py

@@ -63,8 +63,6 @@ class Warnings:
             "have the spacy-lookups-data package installed.")
     W024 = ("Entity '{entity}' - Alias '{alias}' combination already exists in "
             "the Knowledge Base.")
-    W025 = ("'{name}' requires '{attr}' to be assigned, but none of the "
-            "previous components in the pipeline declare that they assign it.")
     W026 = ("Unable to set all sentence boundaries from dependency parses.")
     W027 = ("Found a large training file of {size} bytes. Note that it may "
             "be more efficient to split your training data into multiple "

spacy/gold/example.pyx

@@ -10,7 +10,7 @@ from .align import Alignment
 from .iob_utils import biluo_to_iob, biluo_tags_from_offsets, biluo_tags_from_doc
 from .iob_utils import spans_from_biluo_tags
 from ..errors import Errors, Warnings
-from ..syntax import nonproj
+from ..pipeline._parser_internals import nonproj
 
 
 cpdef Doc annotations2doc(vocab, tok_annot, doc_annot):

spacy/language.py

@@ -18,7 +18,7 @@ from timeit import default_timer as timer
 
 from .tokens.underscore import Underscore
 from .vocab import Vocab, create_vocab
-from .pipe_analysis import analyze_pipes, analyze_all_pipes, validate_attrs
+from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
 from .gold import Example
 from .scorer import Scorer
 from .util import create_default_optimizer, registry
@@ -37,8 +37,6 @@ from . import util
 from . import about
 
 
-# TODO: integrate pipeline analyis
-ENABLE_PIPELINE_ANALYSIS = False
 # This is the base config will all settings (training etc.)
 DEFAULT_CONFIG_PATH = Path(__file__).parent / "default_config.cfg"
 DEFAULT_CONFIG = Config().from_disk(DEFAULT_CONFIG_PATH)
@@ -522,6 +520,25 @@ class Language:
             return add_component(func)
         return add_component
 
+    def analyze_pipes(
+        self,
+        *,
+        keys: List[str] = ["assigns", "requires", "scores", "retokenizes"],
+        pretty: bool = False,
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze the current pipeline components, print a summary of what
+        they assign or require and check that all requirements are met.
+
+        keys (List[str]): The meta values to display in the table. Corresponds
+            to values in FactoryMeta, defined by @Language.factory decorator.
+        pretty (bool): Pretty-print the results.
+        RETURNS (dict): The data.
+        """
+        analysis = analyze_pipes(self, keys=keys)
+        if pretty:
+            print_pipe_analysis(analysis, keys=keys)
+        return analysis
+
     def get_pipe(self, name: str) -> Callable[[Doc], Doc]:
         """Get a pipeline component for a given component name.
 
@@ -666,8 +683,6 @@ class Language:
         pipe_index = self._get_pipe_index(before, after, first, last)
         self._pipe_meta[name] = self.get_factory_meta(factory_name)
         self.pipeline.insert(pipe_index, (name, pipe_component))
-        if ENABLE_PIPELINE_ANALYSIS:
-            analyze_pipes(self, name, pipe_index)
         return pipe_component
 
     def _get_pipe_index(
@@ -758,8 +773,6 @@ class Language:
             self.add_pipe(factory_name, name=name)
         else:
             self.add_pipe(factory_name, name=name, before=pipe_index)
-        if ENABLE_PIPELINE_ANALYSIS:
-            analyze_all_pipes(self)
 
     def rename_pipe(self, old_name: str, new_name: str) -> None:
         """Rename a pipeline component.
@@ -793,8 +806,6 @@ class Language:
         # because factory may be used for something else
         self._pipe_meta.pop(name)
         self._pipe_configs.pop(name)
-        if ENABLE_PIPELINE_ANALYSIS:
-            analyze_all_pipes(self)
         return removed
 
     def __call__(
@@ -1099,6 +1110,7 @@ class Language:
         batch_size: int = 256,
         scorer: Optional[Scorer] = None,
         component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
+        scorer_cfg: Optional[Dict[str, Any]] = None,
     ) -> Dict[str, Union[float, dict]]:
         """Evaluate a model's pipeline components.
 
@@ -1109,6 +1121,8 @@ class Language:
             will be created.
         component_cfg (dict): An optional dictionary with extra keyword
             arguments for specific components.
+        scorer_cfg (dict): An optional dictionary with extra keyword arguments
+            for the scorer.
         RETURNS (Scorer): The scorer containing the evaluation results.
 
         DOCS: https://spacy.io/api/language#evaluate
@@ -1126,8 +1140,10 @@ class Language:
             raise TypeError(err)
         if component_cfg is None:
             component_cfg = {}
+        if scorer_cfg is None:
+            scorer_cfg = {}
         if scorer is None:
-            kwargs = component_cfg.get("scorer", {})
+            kwargs = dict(scorer_cfg)
             kwargs.setdefault("verbose", verbose)
             kwargs.setdefault("nlp", self)
             scorer = Scorer(**kwargs)
@@ -1136,9 +1152,9 @@ class Language:
         start_time = timer()
         # tokenize the texts only for timing purposes
         if not hasattr(self.tokenizer, "pipe"):
-            _ = [self.tokenizer(text) for text in texts]
+            _ = [self.tokenizer(text) for text in texts]  # noqa: F841
         else:
-            _ = list(self.tokenizer.pipe(texts))
+            _ = list(self.tokenizer.pipe(texts))  # noqa: F841
         for name, pipe in self.pipeline:
             kwargs = component_cfg.get(name, {})
             kwargs.setdefault("batch_size", batch_size)

spacy/ml/_character_embed.py

@@ -1,6 +1,7 @@
 from typing import List
 from thinc.api import Model
 from thinc.types import Floats2d
+
 from ..tokens import Doc
 
 
@@ -15,14 +16,14 @@ def CharacterEmbed(nM: int, nC: int) -> Model[List[Doc], List[Floats2d]]:
     )
 
 
-def init(model, X=None, Y=None):
+def init(model: Model, X=None, Y=None):
     vectors_table = model.ops.alloc3f(
         model.get_dim("nC"), model.get_dim("nV"), model.get_dim("nM")
     )
     model.set_param("E", vectors_table)
 
 
-def forward(model, docs, is_train):
+def forward(model: Model, docs: List[Doc], is_train: bool):
     if docs is None:
         return []
     ids = []

spacy/ml/_iob.py

@@ -14,7 +14,7 @@ def IOB() -> Model[Padded, Padded]:
     )
 
 
-def init(model, X: Optional[Padded] = None, Y: Optional[Padded] = None):
+def init(model: Model, X: Optional[Padded] = None, Y: Optional[Padded] = None) -> None:
     if X is not None and Y is not None:
         if X.data.shape != Y.data.shape:
             # TODO: Fix error

spacy/ml/extract_ngrams.py

@@ -4,14 +4,14 @@ from thinc.api import Model
 from ..attrs import LOWER
 
 
-def extract_ngrams(ngram_size, attr=LOWER) -> Model:
+def extract_ngrams(ngram_size: int, attr: int = LOWER) -> Model:
     model = Model("extract_ngrams", forward)
     model.attrs["ngram_size"] = ngram_size
     model.attrs["attr"] = attr
     return model
 
 
-def forward(model, docs, is_train: bool):
+def forward(model: Model, docs, is_train: bool):
     batch_keys = []
     batch_vals = []
     for doc in docs:

spacy/ml/models/entity_linker.py

@@ -1,5 +1,4 @@
-from pathlib import Path
+from typing import Optional
-
 from thinc.api import chain, clone, list2ragged, reduce_mean, residual
 from thinc.api import Model, Maxout, Linear
 
@@ -9,7 +8,7 @@ from ...vocab import Vocab
 
 
 @registry.architectures.register("spacy.EntityLinker.v1")
-def build_nel_encoder(tok2vec, nO=None):
+def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
     with Model.define_operators({">>": chain, "**": clone}):
         token_width = tok2vec.get_dim("nO")
         output_layer = Linear(nO=nO, nI=token_width)
@@ -26,7 +25,7 @@ def build_nel_encoder(tok2vec, nO=None):
 
 
 @registry.assets.register("spacy.KBFromFile.v1")
-def load_kb(vocab_path, kb_path) -> KnowledgeBase:
+def load_kb(vocab_path: str, kb_path: str) -> KnowledgeBase:
     vocab = Vocab().from_disk(vocab_path)
     kb = KnowledgeBase(vocab=vocab)
     kb.load_bulk(kb_path)

spacy/ml/models/multi_task.py

@@ -1,10 +1,20 @@
+from typing import Optional, Iterable, Tuple, List, TYPE_CHECKING
 import numpy
-
 from thinc.api import chain, Maxout, LayerNorm, Softmax, Linear, zero_init, Model
 from thinc.api import MultiSoftmax, list2array
 
+if TYPE_CHECKING:
+    # This lets us add type hints for mypy etc. without causing circular imports
+    from ...vocab import Vocab  # noqa: F401
+    from ...tokens import Doc  # noqa: F401
 
-def build_multi_task_model(tok2vec, maxout_pieces, token_vector_width, nO=None):
+
+def build_multi_task_model(
+    tok2vec: Model,
+    maxout_pieces: int,
+    token_vector_width: int,
+    nO: Optional[int] = None,
+) -> Model:
     softmax = Softmax(nO=nO, nI=token_vector_width * 2)
     model = chain(
         tok2vec,
@@ -22,7 +32,13 @@ def build_multi_task_model(tok2vec, maxout_pieces, token_vector_width, nO=None):
     return model
 
 
-def build_cloze_multi_task_model(vocab, tok2vec, maxout_pieces, hidden_size, nO=None):
+def build_cloze_multi_task_model(
+    vocab: "Vocab",
+    tok2vec: Model,
+    maxout_pieces: int,
+    hidden_size: int,
+    nO: Optional[int] = None,
+) -> Model:
     # nO = vocab.vectors.data.shape[1]
     output_layer = chain(
         list2array(),
@@ -43,24 +59,24 @@ def build_cloze_multi_task_model(vocab, tok2vec, maxout_pieces, hidden_size, nO=
 
 
 def build_cloze_characters_multi_task_model(
-    vocab, tok2vec, maxout_pieces, hidden_size, nr_char
+    vocab: "Vocab", tok2vec: Model, maxout_pieces: int, hidden_size: int, nr_char: int
-):
+) -> Model:
     output_layer = chain(
         list2array(),
         Maxout(hidden_size, nP=maxout_pieces),
         LayerNorm(nI=hidden_size),
         MultiSoftmax([256] * nr_char, nI=hidden_size),
     )
-
     model = build_masked_language_model(vocab, chain(tok2vec, output_layer))
     model.set_ref("tok2vec", tok2vec)
     model.set_ref("output_layer", output_layer)
     return model
 
 
-def build_masked_language_model(vocab, wrapped_model, mask_prob=0.15):
+def build_masked_language_model(
+    vocab: "Vocab", wrapped_model: Model, mask_prob: float = 0.15
+) -> Model:
     """Convert a model into a BERT-style masked language model"""
-
     random_words = _RandomWords(vocab)
 
     def mlm_forward(model, docs, is_train):
@@ -74,7 +90,7 @@ def build_masked_language_model(vocab, wrapped_model, mask_prob=0.15):
 
         return output, mlm_backward
 
-    def mlm_initialize(model, X=None, Y=None):
+    def mlm_initialize(model: Model, X=None, Y=None):
         wrapped = model.layers[0]
         wrapped.initialize(X=X, Y=Y)
         for dim in wrapped.dim_names:
@@ -90,12 +106,11 @@ def build_masked_language_model(vocab, wrapped_model, mask_prob=0.15):
         dims={dim: None for dim in wrapped_model.dim_names},
     )
     mlm_model.set_ref("wrapped", wrapped_model)
-
     return mlm_model
 
 
 class _RandomWords:
-    def __init__(self, vocab):
+    def __init__(self, vocab: "Vocab") -> None:
         self.words = [lex.text for lex in vocab if lex.prob != 0.0]
         self.probs = [lex.prob for lex in vocab if lex.prob != 0.0]
         self.words = self.words[:10000]
@@ -104,7 +119,7 @@ class _RandomWords:
         self.probs /= self.probs.sum()
         self._cache = []
 
-    def next(self):
+    def next(self) -> str:
         if not self._cache:
             self._cache.extend(
                 numpy.random.choice(len(self.words), 10000, p=self.probs)
@@ -113,9 +128,11 @@ class _RandomWords:
         return self.words[index]
 
 
-def _apply_mask(docs, random_words, mask_prob=0.15):
+def _apply_mask(
+    docs: Iterable["Doc"], random_words: _RandomWords, mask_prob: float = 0.15
+) -> Tuple[numpy.ndarray, List["Doc"]]:
     # This needs to be here to avoid circular imports
-    from ...tokens import Doc
+    from ...tokens import Doc  # noqa: F811
 
     N = sum(len(doc) for doc in docs)
     mask = numpy.random.uniform(0.0, 1.0, (N,))
@@ -141,7 +158,7 @@ def _apply_mask(docs, random_words, mask_prob=0.15):
     return mask, masked_docs
 
 
-def _replace_word(word, random_words, mask="[MASK]"):
+def _replace_word(word: str, random_words: _RandomWords, mask: str = "[MASK]") -> str:
     roll = numpy.random.random()
     if roll < 0.8:
         return mask

spacy/ml/models/parser.py

@@ -1,6 +1,5 @@
-from pydantic import StrictInt
+from typing import Optional
-from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops, with_array
+from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops
-from thinc.api import LayerNorm, Maxout, Mish
 
 from ...util import registry
 from .._precomputable_affine import PrecomputableAffine
@@ -10,16 +9,15 @@ from ..tb_framework import TransitionModel
 @registry.architectures.register("spacy.TransitionBasedParser.v1")
 def build_tb_parser_model(
     tok2vec: Model,
-    nr_feature_tokens: StrictInt,
+    nr_feature_tokens: int,
-    hidden_width: StrictInt,
+    hidden_width: int,
-    maxout_pieces: StrictInt,
+    maxout_pieces: int,
-    use_upper=True,
+    use_upper: bool = True,
-    nO=None,
+    nO: Optional[int] = None,
-):
+) -> Model:
     t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
     tok2vec = chain(tok2vec, list2array(), Linear(hidden_width, t2v_width),)
     tok2vec.set_dim("nO", hidden_width)
-
     lower = PrecomputableAffine(
         nO=hidden_width if use_upper else nO,
         nF=nr_feature_tokens,

spacy/ml/models/simple_ner.py

@@ -26,7 +26,6 @@ def BiluoTagger(
         with_array(softmax_activation()),
         padded2list(),
     )
-
     return Model(
         "biluo-tagger",
         forward,
@@ -52,7 +51,6 @@ def IOBTagger(
         with_array(softmax_activation()),
         padded2list(),
     )
-
     return Model(
         "iob-tagger",
         forward,

spacy/ml/models/tagger.py

@@ -1,10 +1,11 @@
+from typing import Optional
 from thinc.api import zero_init, with_array, Softmax, chain, Model
 
 from ...util import registry
 
 
 @registry.architectures.register("spacy.Tagger.v1")
-def build_tagger_model(tok2vec, nO=None) -> Model:
+def build_tagger_model(tok2vec: Model, nO: Optional[int] = None) -> Model:
     # TODO: glorot_uniform_init seems to work a bit better than zero_init here?!
     t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
     output_layer = Softmax(nO, t2v_width, init_W=zero_init)

spacy/ml/models/textcat.py

@@ -2,10 +2,9 @@ from typing import Optional
 from thinc.api import Model, reduce_mean, Linear, list2ragged, Logistic
 from thinc.api import chain, concatenate, clone, Dropout, ParametricAttention
 from thinc.api import SparseLinear, Softmax, softmax_activation, Maxout, reduce_sum
-from thinc.api import HashEmbed, with_ragged, with_array, with_cpu, uniqued
+from thinc.api import HashEmbed, with_array, with_cpu, uniqued
 from thinc.api import Relu, residual, expand_window, FeatureExtractor
 
-from ... import util
 from ...attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
 from ...util import registry
 from ..extract_ngrams import extract_ngrams
@@ -40,7 +39,12 @@ def build_simple_cnn_text_classifier(
 
 
 @registry.architectures.register("spacy.TextCatBOW.v1")
-def build_bow_text_classifier(exclusive_classes, ngram_size, no_output_layer, nO=None):
+def build_bow_text_classifier(
+    exclusive_classes: bool,
+    ngram_size: int,
+    no_output_layer: bool,
+    nO: Optional[int] = None,
+) -> Model:
     with Model.define_operators({">>": chain}):
         sparse_linear = SparseLinear(nO)
         model = extract_ngrams(ngram_size, attr=ORTH) >> sparse_linear
@@ -55,16 +59,16 @@ def build_bow_text_classifier(exclusive_classes, ngram_size, no_output_layer, nO
 
 @registry.architectures.register("spacy.TextCatEnsemble.v1")
 def build_text_classifier(
-    width,
+    width: int,
-    embed_size,
+    embed_size: int,
-    pretrained_vectors,
+    pretrained_vectors: Optional[bool],
-    exclusive_classes,
+    exclusive_classes: bool,
-    ngram_size,
+    ngram_size: int,
-    window_size,
+    window_size: int,
-    conv_depth,
+    conv_depth: int,
-    dropout,
+    dropout: Optional[float],
-    nO=None,
+    nO: Optional[int] = None,
-):
+) -> Model:
     cols = [ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
     with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
         lower = HashEmbed(
@@ -91,7 +95,6 @@ def build_text_classifier(
             dropout=dropout,
             seed=13,
         )
-
         width_nI = sum(layer.get_dim("nO") for layer in [lower, prefix, suffix, shape])
         trained_vectors = FeatureExtractor(cols) >> with_array(
             uniqued(
@@ -100,7 +103,6 @@ def build_text_classifier(
                 column=cols.index(ORTH),
             )
         )
-
         if pretrained_vectors:
             static_vectors = StaticVectors(width)
             vector_layer = trained_vectors | static_vectors
@@ -152,7 +154,12 @@ def build_text_classifier(
 
 
 @registry.architectures.register("spacy.TextCatLowData.v1")
-def build_text_classifier_lowdata(width, pretrained_vectors, dropout, nO=None):
+def build_text_classifier_lowdata(
+    width: int,
+    pretrained_vectors: Optional[bool],
+    dropout: Optional[float],
+    nO: Optional[int] = None,
+) -> Model:
     # Note, before v.3, this was the default if setting "low_data" and "pretrained_dims"
     with Model.define_operators({">>": chain, "**": clone}):
         model = (

spacy/ml/models/tok2vec.py

@@ -6,16 +6,15 @@ from thinc.api import expand_window, residual, Maxout, Mish, PyTorchLSTM
 from thinc.types import Floats2d
 
 from ...tokens import Doc
-from ... import util
 from ...util import registry
 from ...ml import _character_embed
 from ..staticvectors import StaticVectors
 from ...pipeline.tok2vec import Tok2VecListener
-from ...attrs import ID, ORTH, NORM, PREFIX, SUFFIX, SHAPE
+from ...attrs import ORTH, NORM, PREFIX, SUFFIX, SHAPE
 
 
 @registry.architectures.register("spacy.Tok2VecListener.v1")
-def tok2vec_listener_v1(width, upstream="*"):
+def tok2vec_listener_v1(width: int, upstream: str = "*"):
     tok2vec = Tok2VecListener(upstream_name=upstream, width=width)
     return tok2vec
 
@@ -45,10 +44,11 @@ def build_hash_embed_cnn_tok2vec(
             width=width,
             depth=depth,
             window_size=window_size,
-            maxout_pieces=maxout_pieces
+            maxout_pieces=maxout_pieces,
-        )
+        ),
     )
 
+
 @registry.architectures.register("spacy.Tok2Vec.v1")
 def build_Tok2Vec_model(
     embed: Model[List[Doc], List[Floats2d]],
@@ -68,7 +68,6 @@ def MultiHashEmbed(
     width: int, rows: int, also_embed_subwords: bool, also_use_static_vectors: bool
 ):
     cols = [NORM, PREFIX, SUFFIX, SHAPE, ORTH]
-
     seed = 7
 
     def make_hash_embed(feature):
@@ -124,11 +123,11 @@ def CharacterEmbed(width: int, rows: int, nM: int, nC: int):
             chain(
                 FeatureExtractor([NORM]),
                 list2ragged(),
-                with_array(HashEmbed(nO=width, nV=rows, column=0, seed=5))
+                with_array(HashEmbed(nO=width, nV=rows, column=0, seed=5)),
-            )
+            ),
         ),
         with_array(Maxout(width, nM * nC + width, nP=3, normalize=True, dropout=0.0)),
-        ragged2list()
+        ragged2list(),
     )
     return model
 
@@ -155,12 +154,7 @@ def MaxoutWindowEncoder(width: int, window_size: int, maxout_pieces: int, depth:
 def MishWindowEncoder(width, window_size, depth):
     cnn = chain(
         expand_window(window_size=window_size),
-        Mish(
+        Mish(nO=width, nI=width * ((window_size * 2) + 1), dropout=0.0, normalize=True),
-            nO=width,
-            nI=width * ((window_size * 2) + 1),
-            dropout=0.0,
-            normalize=True
-        ),
     )
     model = clone(residual(cnn), depth)
     model.set_dim("nO", width)

spacy/ml/parser_model.pxd

@@ -1,8 +1,6 @@
 from libc.string cimport memset, memcpy
-from libc.stdlib cimport calloc, free, realloc
+from ..typedefs cimport weight_t, hash_t
-from ..typedefs cimport weight_t, class_t, hash_t
+from ..pipeline._parser_internals._state cimport StateC
-
-from ._state cimport StateC
 
 
 cdef struct SizesC:

spacy/ml/parser_model.pyx

@@ -1,29 +1,18 @@
 # cython: infer_types=True, cdivision=True, boundscheck=False
-cimport cython.parallel
 cimport numpy as np
 from libc.math cimport exp
-from libcpp.vector cimport vector
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport calloc, free, realloc
-from cymem.cymem cimport Pool
-from thinc.extra.search cimport Beam
 from thinc.backends.linalg cimport Vec, VecVec
 cimport blis.cy
 
 import numpy
 import numpy.random
-from thinc.api import Linear, Model, CupyOps, NumpyOps, use_ops, noop
+from thinc.api import Model, CupyOps, NumpyOps
 
-from ..typedefs cimport weight_t, class_t, hash_t
-from ..tokens.doc cimport Doc
-from .stateclass cimport StateClass
-from .transition_system cimport Transition
-
-from ..compat import copy_array
-from ..errors import Errors, TempErrors
-from ..util import create_default_optimizer
 from .. import util
-from . import nonproj
+from ..typedefs cimport weight_t, class_t, hash_t
+from ..pipeline._parser_internals.stateclass cimport StateClass
 
 
 cdef WeightsC get_c_weights(model) except *:

spacy/ml/tb_framework.py

@@ -1,5 +1,5 @@
 from thinc.api import Model, noop, use_ops, Linear
-from ..syntax._parser_model import ParserStepModel
+from .parser_model import ParserStepModel
 
 
 def TransitionModel(tok2vec, lower, upper, dropout=0.2, unseen_classes=set()):

spacy/pipe_analysis.py

@@ -1,9 +1,8 @@
 from typing import List, Dict, Iterable, Optional, Union, TYPE_CHECKING
-from wasabi import Printer
+from wasabi import msg
-import warnings
 
 from .tokens import Doc, Token, Span
-from .errors import Errors, Warnings
+from .errors import Errors
 from .util import dot_to_dict
 
 if TYPE_CHECKING:
@@ -11,48 +10,7 @@ if TYPE_CHECKING:
     from .language import Language  # noqa: F401
 
 
-def analyze_pipes(
+DEFAULT_KEYS = ["requires", "assigns", "scores", "retokenizes"]
-    nlp: "Language", name: str, index: int, warn: bool = True
-) -> List[str]:
-    """Analyze a pipeline component with respect to its position in the current
-    pipeline and the other components. Will check whether requirements are
-    fulfilled (e.g. if previous components assign the attributes).
-
-    nlp (Language): The current nlp object.
-    name (str): The name of the pipeline component to analyze.
-    index (int): The index of the component in the pipeline.
-    warn (bool): Show user warning if problem is found.
-    RETURNS (List[str]): The problems found for the given pipeline component.
-    """
-    assert nlp.pipeline[index][0] == name
-    prev_pipes = nlp.pipeline[:index]
-    meta = nlp.get_pipe_meta(name)
-    requires = {annot: False for annot in meta.requires}
-    if requires:
-        for prev_name, prev_pipe in prev_pipes:
-            prev_meta = nlp.get_pipe_meta(prev_name)
-            for annot in prev_meta.assigns:
-                requires[annot] = True
-    problems = []
-    for annot, fulfilled in requires.items():
-        if not fulfilled:
-            problems.append(annot)
-            if warn:
-                warnings.warn(Warnings.W025.format(name=name, attr=annot))
-    return problems
-
-
-def analyze_all_pipes(nlp: "Language", warn: bool = True) -> Dict[str, List[str]]:
-    """Analyze all pipes in the pipeline in order.
-
-    nlp (Language): The current nlp object.
-    warn (bool): Show user warning if problem is found.
-    RETURNS (Dict[str, List[str]]): The problems found, keyed by component name.
-    """
-    problems = {}
-    for i, name in enumerate(nlp.pipe_names):
-        problems[name] = analyze_pipes(nlp, name, i, warn=warn)
-    return problems
 
 
 def validate_attrs(values: Iterable[str]) -> Iterable[str]:
@@ -101,89 +59,77 @@ def validate_attrs(values: Iterable[str]) -> Iterable[str]:
     return values
 
 
-def _get_feature_for_attr(nlp: "Language", attr: str, feature: str) -> List[str]:
+def get_attr_info(nlp: "Language", attr: str) -> Dict[str, List[str]]:
-    assert feature in ["assigns", "requires"]
+    """Check which components in the pipeline assign or require an attribute.
-    result = []
+
+    nlp (Language): The current nlp object.
+    attr (str): The attribute, e.g. "doc.tensor".
+    RETURNS (Dict[str, List[str]]): A dict keyed by "assigns" and "requires",
+        mapped to a list of component names.
+    """
+    result = {"assigns": [], "requires": []}
     for pipe_name in nlp.pipe_names:
         meta = nlp.get_pipe_meta(pipe_name)
-        pipe_assigns = getattr(meta, feature, [])
+        if attr in meta.assigns:
-        if attr in pipe_assigns:
+            result["assigns"].append(pipe_name)
-            result.append(pipe_name)
+        if attr in meta.requires:
+            result["requires"].append(pipe_name)
     return result
 
 
-def get_assigns_for_attr(nlp: "Language", attr: str) -> List[str]:
+def analyze_pipes(
-    """Get all pipeline components that assign an attr, e.g. "doc.tensor".
+    nlp: "Language", *, keys: List[str] = DEFAULT_KEYS,
-
+) -> Dict[str, Union[List[str], Dict[str, List[str]]]]:
-    pipeline (Language): The current nlp object.
-    attr (str): The attribute to check.
-    RETURNS (List[str]): Names of components that require the attr.
-    """
-    return _get_feature_for_attr(nlp, attr, "assigns")
-
-
-def get_requires_for_attr(nlp: "Language", attr: str) -> List[str]:
-    """Get all pipeline components that require an attr, e.g. "doc.tensor".
-
-    pipeline (Language): The current nlp object.
-    attr (str): The attribute to check.
-    RETURNS (List[str]): Names of components that require the attr.
-    """
-    return _get_feature_for_attr(nlp, attr, "requires")
-
-
-def print_summary(
-    nlp: "Language", pretty: bool = True, no_print: bool = False
-) -> Optional[Dict[str, Union[List[str], Dict[str, List[str]]]]]:
     """Print a formatted summary for the current nlp object's pipeline. Shows
     a table with the pipeline components and why they assign and require, as
     well as any problems if available.
 
     nlp (Language): The nlp object.
-    pretty (bool): Pretty-print the results (color etc).
+    keys (List[str]): The meta keys to show in the table.
-    no_print (bool): Don't print anything, just return the data.
+    RETURNS (dict): A dict with "summary" and "problems".
-    RETURNS (dict): A dict with "overview" and "problems".
     """
-    msg = Printer(pretty=pretty, no_print=no_print)
+    result = {"summary": {}, "problems": {}}
-    overview = []
+    all_attrs = set()
-    problems = {}
     for i, name in enumerate(nlp.pipe_names):
         meta = nlp.get_pipe_meta(name)
-        overview.append((i, name, meta.requires, meta.assigns, meta.retokenizes))
+        all_attrs.update(meta.assigns)
-        problems[name] = analyze_pipes(nlp, name, i, warn=False)
+        all_attrs.update(meta.requires)
+        result["summary"][name] = {key: getattr(meta, key, None) for key in keys}
+        prev_pipes = nlp.pipeline[:i]
+        requires = {annot: False for annot in meta.requires}
+        if requires:
+            for prev_name, prev_pipe in prev_pipes:
+                prev_meta = nlp.get_pipe_meta(prev_name)
+                for annot in prev_meta.assigns:
+                    requires[annot] = True
+        result["problems"][name] = []
+        for annot, fulfilled in requires.items():
+            if not fulfilled:
+                result["problems"][name].append(annot)
+    result["attrs"] = {attr: get_attr_info(nlp, attr) for attr in all_attrs}
+    return result
+
+
+def print_pipe_analysis(
+    analysis: Dict[str, Union[List[str], Dict[str, List[str]]]],
+    *,
+    keys: List[str] = DEFAULT_KEYS,
+) -> Optional[Dict[str, Union[List[str], Dict[str, List[str]]]]]:
+    """Print a formatted version of the pipe analysis produced by analyze_pipes.
+
+    analysis (Dict[str, Union[List[str], Dict[str, List[str]]]]): The analysis.
+    keys (List[str]): The meta keys to show in the table.
+    """
     msg.divider("Pipeline Overview")
-    header = ("#", "Component", "Requires", "Assigns", "Retokenizes")
+    header = ["#", "Component", *[key.capitalize() for key in keys]]
-    msg.table(overview, header=header, divider=True, multiline=True)
+    summary = analysis["summary"].items()
-    n_problems = sum(len(p) for p in problems.values())
+    body = [[i, n, *[v for v in m.values()]] for i, (n, m) in enumerate(summary)]
-    if any(p for p in problems.values()):
+    msg.table(body, header=header, divider=True, multiline=True)
+    n_problems = sum(len(p) for p in analysis["problems"].values())
+    if any(p for p in analysis["problems"].values()):
         msg.divider(f"Problems ({n_problems})")
-        for name, problem in problems.items():
+        for name, problem in analysis["problems"].items():
             if problem:
                 msg.warn(f"'{name}' requirements not met: {', '.join(problem)}")
     else:
         msg.good("No problems found.")
-    if no_print:
-        return {"overview": overview, "problems": problems}
-
-
-def count_pipeline_interdependencies(nlp: "Language") -> List[int]:
-    """Count how many subsequent components require an annotation set by each
-    component in the pipeline.
-
-    nlp (Language): The current nlp object.
-    RETURNS (List[int]): The interdependency counts.
-    """
-    pipe_assigns = []
-    pipe_requires = []
-    for name in nlp.pipe_names:
-        meta = nlp.get_pipe_meta(name)
-        pipe_assigns.append(set(meta.assigns))
-        pipe_requires.append(set(meta.requires))
-    counts = []
-    for i, assigns in enumerate(pipe_assigns):
-        count = 0
-        for requires in pipe_requires[i + 1 :]:
-            if assigns.intersection(requires):
-                count += 1
-        counts.append(count)
-    return counts

spacy/pipeline/_parser_internals/_state.pxd

@@ -1,15 +1,14 @@
-from libc.string cimport memcpy, memset, memmove
+from libc.string cimport memcpy, memset
-from libc.stdlib cimport malloc, calloc, free
+from libc.stdlib cimport calloc, free
 from libc.stdint cimport uint32_t, uint64_t
 from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
 from murmurhash.mrmr cimport hash64
 
-from ..vocab cimport EMPTY_LEXEME
+from ...vocab cimport EMPTY_LEXEME
-from ..structs cimport TokenC, SpanC
+from ...structs cimport TokenC, SpanC
-from ..lexeme cimport Lexeme
+from ...lexeme cimport Lexeme
-from ..symbols cimport punct
+from ...attrs cimport IS_SPACE
-from ..attrs cimport IS_SPACE
+from ...typedefs cimport attr_t
-from ..typedefs cimport attr_t
 
 
 cdef inline bint is_space_token(const TokenC* token) nogil:

spacy/pipeline/_parser_internals/arc_eager.pxd

@@ -1,8 +1,6 @@
-from cymem.cymem cimport Pool
-
 from .stateclass cimport StateClass
-from ..typedefs cimport weight_t, attr_t
+from ...typedefs cimport weight_t, attr_t
-from .transition_system cimport TransitionSystem, Transition
+from .transition_system cimport Transition, TransitionSystem
 
 
 cdef class ArcEager(TransitionSystem):

spacy/pipeline/_parser_internals/arc_eager.pyx

@@ -1,24 +1,17 @@
 # cython: profile=True, cdivision=True, infer_types=True
-from cpython.ref cimport Py_INCREF
 from cymem.cymem cimport Pool, Address
 from libc.stdint cimport int32_t
 
 from collections import defaultdict, Counter
-import json
 
-from ..typedefs cimport hash_t, attr_t
+from ...typedefs cimport hash_t, attr_t
-from ..strings cimport hash_string
+from ...strings cimport hash_string
-from ..structs cimport TokenC
+from ...structs cimport TokenC
-from ..tokens.doc cimport Doc, set_children_from_heads
+from ...tokens.doc cimport Doc, set_children_from_heads
+from ...gold.example cimport Example
+from ...errors import Errors
 from .stateclass cimport StateClass
 from ._state cimport StateC
-from .transition_system cimport move_cost_func_t, label_cost_func_t
-from ..gold.example cimport Example
-
-from ..errors import Errors
-from .nonproj import is_nonproj_tree
-from . import nonproj
-
 
 # Calculate cost as gold/not gold. We don't use scalar value anyway.
 cdef int BINARY_COSTS = 1

spacy/pipeline/_parser_internals/ner.pxd

@@ -1,6 +1,4 @@
 from .transition_system cimport TransitionSystem
-from .transition_system cimport Transition
-from ..typedefs cimport attr_t
 
 
 cdef class BiluoPushDown(TransitionSystem):

spacy/pipeline/_parser_internals/ner.pyx

@@ -2,17 +2,14 @@ from collections import Counter
 from libc.stdint cimport int32_t
 from cymem.cymem cimport Pool
 
-from ..typedefs cimport weight_t
+from ...typedefs cimport weight_t, attr_t
+from ...lexeme cimport Lexeme
+from ...attrs cimport IS_SPACE
+from ...gold.example cimport Example
+from ...errors import Errors
 from .stateclass cimport StateClass
 from ._state cimport StateC
-from .transition_system cimport Transition
+from .transition_system cimport Transition, do_func_t
-from .transition_system cimport do_func_t
-from ..lexeme cimport Lexeme
-from ..attrs cimport IS_SPACE
-from ..gold.iob_utils import biluo_tags_from_offsets
-from ..gold.example cimport Example
-
-from ..errors import Errors
 
 
 cdef enum:

spacy/pipeline/_parser_internals/nonproj.pyx

@@ -5,9 +5,9 @@ scheme.
 """
 from copy import copy
 
-from ..tokens.doc cimport Doc, set_children_from_heads
+from ...tokens.doc cimport Doc, set_children_from_heads
 
-from ..errors import Errors
+from ...errors import Errors
 
 
 DELIMITER = '||'

spacy/pipeline/_parser_internals/stateclass.pxd

@@ -1,12 +1,8 @@
-from libc.string cimport memcpy, memset
-
 from cymem.cymem cimport Pool
-cimport cython
 
-from ..structs cimport TokenC, SpanC
+from ...structs cimport TokenC, SpanC
-from ..typedefs cimport attr_t
+from ...typedefs cimport attr_t
 
-from ..vocab cimport EMPTY_LEXEME
 from ._state cimport StateC
 
 

spacy/pipeline/_parser_internals/stateclass.pyx

@@ -1,7 +1,7 @@
 # cython: infer_types=True
 import numpy
 
-from ..tokens.doc cimport Doc
+from ...tokens.doc cimport Doc
 
 
 cdef class StateClass:

spacy/pipeline/_parser_internals/transition_system.pxd

@@ -1,11 +1,11 @@
 from cymem.cymem cimport Pool
 
-from ..typedefs cimport attr_t, weight_t
+from ...typedefs cimport attr_t, weight_t
-from ..structs cimport TokenC
+from ...structs cimport TokenC
-from ..strings cimport StringStore
+from ...strings cimport StringStore
+from ...gold.example cimport Example
 from .stateclass cimport StateClass
 from ._state cimport StateC
-from ..gold.example cimport Example
 
 
 cdef struct Transition:

spacy/pipeline/_parser_internals/transition_system.pyx

@@ -1,19 +1,17 @@
 # cython: infer_types=True
 from __future__ import print_function
-from cpython.ref cimport Py_INCREF
 from cymem.cymem cimport Pool
 
 from collections import Counter
 import srsly
 
-from ..typedefs cimport weight_t
+from ...typedefs cimport weight_t, attr_t
-from ..tokens.doc cimport Doc
+from ...tokens.doc cimport Doc
-from ..structs cimport TokenC
+from ...structs cimport TokenC
 from .stateclass cimport StateClass
-from ..typedefs cimport attr_t
 
-from ..errors import Errors
+from ...errors import Errors
-from .. import util
+from ... import util
 
 
 cdef weight_t MIN_SCORE = -90000

spacy/pipeline/dep_parser.pyx

@@ -1,13 +1,13 @@
 # cython: infer_types=True, profile=True, binding=True
 from typing import Optional, Iterable
-from thinc.api import CosineDistance, to_categorical, get_array_module, Model, Config
+from thinc.api import Model, Config
 
-from ..syntax.nn_parser cimport Parser
+from .transition_parser cimport Parser
-from ..syntax.arc_eager cimport ArcEager
+from ._parser_internals.arc_eager cimport ArcEager
 
 from .functions import merge_subtokens
 from ..language import Language
-from ..syntax import nonproj
+from ._parser_internals import nonproj
 from ..scorer import Scorer
 
 
@@ -34,7 +34,7 @@ DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
 
 @Language.factory(
     "parser",
-    assigns=["token.dep", "token.is_sent_start", "doc.sents"],
+    assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
     default_config={
         "moves": None,
         "update_with_oracle_cut_size": 100,
@@ -120,7 +120,8 @@ cdef class DependencyParser(Parser):
             return dep
         results = {}
         results.update(Scorer.score_spans(examples, "sents", **kwargs))
-        results.update(Scorer.score_deps(examples, "dep", getter=dep_getter,
+        kwargs.setdefault("getter", dep_getter)
-            ignore_labels=("p", "punct"), **kwargs))
+        kwargs.setdefault("ignore_label", ("p", "punct"))
+        results.update(Scorer.score_deps(examples, "dep", **kwargs))
         del results["sents_per_type"]
         return results

spacy/pipeline/entity_linker.py

@@ -222,9 +222,9 @@ class EntityLinker(Pipe):
         set_dropout_rate(self.model, drop)
         if not sentence_docs:
             warnings.warn(Warnings.W093.format(name="Entity Linker"))
-            return 0.0
+            return losses
         sentence_encodings, bp_context = self.model.begin_update(sentence_docs)
-        loss, d_scores = self.get_similarity_loss(
+        loss, d_scores = self.get_loss(
             sentence_encodings=sentence_encodings, examples=examples
         )
         bp_context(d_scores)
@@ -235,7 +235,7 @@ class EntityLinker(Pipe):
             self.set_annotations(docs, predictions)
         return losses
 
-    def get_similarity_loss(self, examples: Iterable[Example], sentence_encodings):
+    def get_loss(self, examples: Iterable[Example], sentence_encodings):
         entity_encodings = []
         for eg in examples:
             kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
@@ -247,7 +247,7 @@ class EntityLinker(Pipe):
         entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
         if sentence_encodings.shape != entity_encodings.shape:
             err = Errors.E147.format(
-                method="get_similarity_loss", msg="gold entities do not match up"
+                method="get_loss", msg="gold entities do not match up"
             )
             raise RuntimeError(err)
         gradients = self.distance.get_grad(sentence_encodings, entity_encodings)
@@ -337,13 +337,13 @@ class EntityLinker(Pipe):
                                     final_kb_ids.append(candidates[0].entity_)
                                 else:
                                     random.shuffle(candidates)
-                                    # this will set all prior probabilities to 0 if they should be excluded from the model
+                                    # set all prior probabilities to 0 if incl_prior=False
                                     prior_probs = xp.asarray(
                                         [c.prior_prob for c in candidates]
                                     )
                                     if not self.cfg.get("incl_prior"):
                                         prior_probs = xp.asarray(
-                                            [0.0 for c in candidates]
+                                            [0.0 for _ in candidates]
                                         )
                                     scores = prior_probs
                                     # add in similarity from the context

spacy/pipeline/multitask.pyx

@@ -1,7 +1,7 @@
 # cython: infer_types=True, profile=True, binding=True
 from typing import Optional
 import numpy
-from thinc.api import CosineDistance, to_categorical, to_categorical, Model, Config
+from thinc.api import CosineDistance, to_categorical, Model, Config
 from thinc.api import set_dropout_rate
 
 from ..tokens.doc cimport Doc
@@ -9,7 +9,7 @@ from ..tokens.doc cimport Doc
 from .pipe import Pipe
 from .tagger import Tagger
 from ..language import Language
-from ..syntax import nonproj
+from ._parser_internals import nonproj
 from ..attrs import POS, ID
 from ..errors import Errors
 
@@ -219,3 +219,6 @@ class ClozeMultitask(Pipe):
 
         if losses is not None:
             losses[self.name] += loss
+
+    def add_label(self, label):
+        raise NotImplementedError

spacy/pipeline/ner.pyx

@@ -1,9 +1,9 @@
 # cython: infer_types=True, profile=True, binding=True
 from typing import Optional, Iterable
-from thinc.api import CosineDistance, to_categorical, get_array_module, Model, Config
+from thinc.api import Model, Config
 
-from ..syntax.nn_parser cimport Parser
+from .transition_parser cimport Parser
-from ..syntax.ner cimport BiluoPushDown
+from ._parser_internals.ner cimport BiluoPushDown
 
 from ..language import Language
 from ..scorer import Scorer

spacy/pipeline/pipe.pxd

@@ -0,0 +1,2 @@
+cdef class Pipe:
+    cdef public str name

spacy/pipeline/pipe.pyx

@@ -8,7 +8,7 @@ from ..errors import Errors
 from .. import util
 
 
-class Pipe:
+cdef class Pipe:
     """This class is a base class and not instantiated directly. Trainable
     pipeline components like the EntityRecognizer or TextCategorizer inherit
     from it and it defines the interface that components should follow to
@@ -17,8 +17,6 @@ class Pipe:
     DOCS: https://spacy.io/api/pipe
     """
 
-    name = None
-
     def __init__(self, vocab, model, name, **cfg):
         """Initialize a pipeline component.
 

spacy/pipeline/sentencizer.pyx

@@ -203,3 +203,9 @@ class Sentencizer(Pipe):
         cfg = srsly.read_json(path)
         self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
         return self
+
+    def get_loss(self, examples, scores):
+        raise NotImplementedError
+
+    def add_label(self, label):
+        raise NotImplementedError

spacy/pipeline/senter.pyx

@@ -109,7 +109,7 @@ class SentenceRecognizer(Tagger):
         for eg in examples:
             eg_truth = []
             for x in eg.get_aligned("sent_start"):
-                if x == None:
+                if x is None:
                     eg_truth.append(None)
                 elif x == 1:
                     eg_truth.append(labels[1])

spacy/pipeline/simple_ner.py

@@ -131,8 +131,6 @@ class SimpleNER(Pipe):
         return losses
 
     def get_loss(self, examples: List[Example], scores) -> Tuple[List[Floats2d], float]:
-        loss = 0
-        d_scores = []
         truths = []
         for eg in examples:
             tags = eg.get_aligned("TAG", as_string=True)
@@ -159,7 +157,6 @@ class SimpleNER(Pipe):
         if not hasattr(get_examples, "__call__"):
             gold_tuples = get_examples
             get_examples = lambda: gold_tuples
-        labels = _get_labels(get_examples())
         for label in _get_labels(get_examples()):
             self.add_label(label)
         labels = self.labels

spacy/pipeline/textcat.py

@@ -238,8 +238,11 @@ class TextCategorizer(Pipe):
 
         DOCS: https://spacy.io/api/textcategorizer#rehearse
         """
+
+        if losses is not None:
+            losses.setdefault(self.name, 0.0)
         if self._rehearsal_model is None:
-            return
+            return losses
         try:
             docs = [eg.predicted for eg in examples]
         except AttributeError:
@@ -250,7 +253,7 @@ class TextCategorizer(Pipe):
             raise TypeError(err)
         if not any(len(doc) for doc in docs):
             # Handle cases where there are no tokens in any docs.
-            return
+            return losses
         set_dropout_rate(self.model, drop)
         scores, bp_scores = self.model.begin_update(docs)
         target = self._rehearsal_model(examples)
@@ -259,7 +262,6 @@ class TextCategorizer(Pipe):
         if sgd is not None:
             self.model.finish_update(sgd)
         if losses is not None:
-            losses.setdefault(self.name, 0.0)
             losses[self.name] += (gradient ** 2).sum()
         return losses
 

spacy/pipeline/tok2vec.py

@@ -199,6 +199,9 @@ class Tok2Vec(Pipe):
         docs = [Doc(self.vocab, words=["hello"])]
         self.model.initialize(X=docs)
 
+    def add_label(self, label):
+        raise NotImplementedError
+
 
 class Tok2VecListener(Model):
     """A layer that gets fed its answers from an upstream connection,

spacy/pipeline/transition_parser.pxd

@@ -1,16 +1,15 @@
-from .stateclass cimport StateClass
+from cymem.cymem cimport Pool
-from .arc_eager cimport TransitionSystem
+
 from ..vocab cimport Vocab
-from ..tokens.doc cimport Doc
+from .pipe cimport Pipe
-from ..structs cimport TokenC
+from ._parser_internals.transition_system cimport Transition, TransitionSystem
-from ._state cimport StateC
+from ._parser_internals._state cimport StateC
-from ._parser_model cimport WeightsC, ActivationsC, SizesC
+from ..ml.parser_model cimport WeightsC, ActivationsC, SizesC
 
 
-cdef class Parser:
+cdef class Parser(Pipe):
     cdef readonly Vocab vocab
     cdef public object model
-    cdef public str name
     cdef public object _rehearsal_model
     cdef readonly TransitionSystem moves
     cdef readonly object cfg

spacy/pipeline/transition_parser.pyx

@@ -1,42 +1,32 @@
 # cython: infer_types=True, cdivision=True, boundscheck=False
-cimport cython.parallel
+from __future__ import print_function
+from cymem.cymem cimport Pool
 cimport numpy as np
 from itertools import islice
-from cpython.ref cimport PyObject, Py_XDECREF
-from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
-from libc.math cimport exp
 from libcpp.vector cimport vector
-from libc.string cimport memset, memcpy
+from libc.string cimport memset
 from libc.stdlib cimport calloc, free
-from cymem.cymem cimport Pool
-from thinc.backends.linalg cimport Vec, VecVec
 
-from thinc.api import chain, clone, Linear, list2array, NumpyOps, CupyOps, use_ops
-from thinc.api import get_array_module, zero_init, set_dropout_rate
-from itertools import islice
 import srsly
+
+from ._parser_internals.stateclass cimport StateClass
+from ..ml.parser_model cimport alloc_activations, free_activations
+from ..ml.parser_model cimport predict_states, arg_max_if_valid
+from ..ml.parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
+from ..ml.parser_model cimport get_c_weights, get_c_sizes
+
+from ..tokens.doc cimport Doc
+from ..errors import Errors, Warnings
+from .. import util
+from ..util import create_default_optimizer
+
+from thinc.api import set_dropout_rate
 import numpy.random
 import numpy
 import warnings
 
-from ..tokens.doc cimport Doc
-from ..typedefs cimport weight_t, class_t, hash_t
-from ._parser_model cimport alloc_activations, free_activations
-from ._parser_model cimport predict_states, arg_max_if_valid
-from ._parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
-from ._parser_model cimport get_c_weights, get_c_sizes
-from .stateclass cimport StateClass
-from ._state cimport StateC
-from .transition_system cimport Transition
 
-from ..util import create_default_optimizer, registry
+cdef class Parser(Pipe):
-from ..compat import copy_array
-from ..errors import Errors, Warnings
-from .. import util
-from . import nonproj
-
-
-cdef class Parser:
     """
     Base class of the DependencyParser and EntityRecognizer.
     """
@@ -107,7 +97,7 @@ cdef class Parser:
 
     @property
     def tok2vec(self):
-        '''Return the embedding and convolutional layer of the model.'''
+        """Return the embedding and convolutional layer of the model."""
         return self.model.get_ref("tok2vec")
 
     @property
@@ -138,13 +128,13 @@ cdef class Parser:
         raise NotImplementedError
 
     def init_multitask_objectives(self, get_examples, pipeline, **cfg):
-        '''Setup models for secondary objectives, to benefit from multi-task
+        """Setup models for secondary objectives, to benefit from multi-task
         learning. This method is intended to be overridden by subclasses.
 
         For instance, the dependency parser can benefit from sharing
         an input representation with a label prediction model. These auxiliary
         models are discarded after training.
-        '''
+        """
         pass
 
     def use_params(self, params):

spacy/scorer.py

@@ -1,55 +1,61 @@
+from typing import Optional, Iterable, Dict, Any, Callable, Tuple, TYPE_CHECKING
 import numpy as np
 
+from .gold import Example
+from .tokens import Token, Doc
 from .errors import Errors
 from .util import get_lang_class
 from .morphology import Morphology
 
+if TYPE_CHECKING:
+    # This lets us add type hints for mypy etc. without causing circular imports
+    from .language import Language  # noqa: F401
+
+
+DEFAULT_PIPELINE = ["senter", "tagger", "morphologizer", "parser", "ner", "textcat"]
+
 
 class PRFScore:
-    """
+    """A precision / recall / F score."""
-    A precision / recall / F score
-    """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.tp = 0
         self.fp = 0
         self.fn = 0
 
-    def score_set(self, cand, gold):
+    def score_set(self, cand: set, gold: set) -> None:
         self.tp += len(cand.intersection(gold))
         self.fp += len(cand - gold)
         self.fn += len(gold - cand)
 
     @property
-    def precision(self):
+    def precision(self) -> float:
         return self.tp / (self.tp + self.fp + 1e-100)
 
     @property
-    def recall(self):
+    def recall(self) -> float:
         return self.tp / (self.tp + self.fn + 1e-100)
 
     @property
-    def fscore(self):
+    def fscore(self) -> float:
         p = self.precision
         r = self.recall
         return 2 * ((p * r) / (p + r + 1e-100))
 
-    def to_dict(self):
+    def to_dict(self) -> Dict[str, float]:
         return {"p": self.precision, "r": self.recall, "f": self.fscore}
 
 
 class ROCAUCScore:
-    """
+    """An AUC ROC score."""
-    An AUC ROC score.
-    """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.golds = []
         self.cands = []
         self.saved_score = 0.0
         self.saved_score_at_len = 0
 
-    def score_set(self, cand, gold):
+    def score_set(self, cand, gold) -> None:
         self.cands.append(cand)
         self.golds.append(gold)
 
@@ -70,51 +76,52 @@ class ROCAUCScore:
 class Scorer:
     """Compute evaluation scores."""
 
-    def __init__(self, nlp=None, **cfg):
+    def __init__(
+        self,
+        nlp: Optional["Language"] = None,
+        default_lang: str = "xx",
+        default_pipeline=DEFAULT_PIPELINE,
+        **cfg,
+    ) -> None:
         """Initialize the Scorer.
 
         DOCS: https://spacy.io/api/scorer#init
         """
         self.nlp = nlp
         self.cfg = cfg
-
         if not nlp:
-            # create a default pipeline
+            nlp = get_lang_class(default_lang)()
-            nlp = get_lang_class("xx")()
+            for pipe in default_pipeline:
-            nlp.add_pipe("senter")
+                nlp.add_pipe(pipe)
-            nlp.add_pipe("tagger")
-            nlp.add_pipe("morphologizer")
-            nlp.add_pipe("parser")
-            nlp.add_pipe("ner")
-            nlp.add_pipe("textcat")
             self.nlp = nlp
 
-    def score(self, examples):
+    def score(self, examples: Iterable[Example]) -> Dict[str, Any]:
         """Evaluate a list of Examples.
 
         examples (Iterable[Example]): The predicted annotations + correct annotations.
         RETURNS (Dict): A dictionary of scores.
+
         DOCS: https://spacy.io/api/scorer#score
         """
         scores = {}
-
         if hasattr(self.nlp.tokenizer, "score"):
             scores.update(self.nlp.tokenizer.score(examples, **self.cfg))
         for name, component in self.nlp.pipeline:
             if hasattr(component, "score"):
                 scores.update(component.score(examples, **self.cfg))
-
         return scores
 
     @staticmethod
-    def score_tokenization(examples, **cfg):
+    def score_tokenization(examples: Iterable[Example], **cfg) -> Dict[str, float]:
         """Returns accuracy and PRF scores for tokenization.
-
         * token_acc: # correct tokens / # gold tokens
         * token_p/r/f: PRF for token character spans
 
         examples (Iterable[Example]): Examples to score
-        RETURNS (dict): A dictionary containing the scores token_acc/p/r/f.
+        RETURNS (Dict[str, float]): A dictionary containing the scores
+            token_acc/p/r/f.
+
+        DOCS: https://spacy.io/api/scorer#score_tokenization
         """
         acc_score = PRFScore()
         prf_score = PRFScore()
@@ -145,16 +152,24 @@ class Scorer:
         }
 
     @staticmethod
-    def score_token_attr(examples, attr, getter=getattr, **cfg):
+    def score_token_attr(
+        examples: Iterable[Example],
+        attr: str,
+        *,
+        getter: Callable[[Token, str], Any] = getattr,
+        **cfg,
+    ) -> Dict[str, float]:
         """Returns an accuracy score for a token-level attribute.
 
         examples (Iterable[Example]): Examples to score
         attr (str): The attribute to score.
-        getter (callable): Defaults to getattr. If provided,
+        getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
             getter(token, attr) should return the value of the attribute for an
             individual token.
-        RETURNS (dict): A dictionary containing the accuracy score under the
+        RETURNS (Dict[str, float]): A dictionary containing the accuracy score
-            key attr_acc.
+            under the key attr_acc.
+
+        DOCS: https://spacy.io/api/scorer#score_token_attr
         """
         tag_score = PRFScore()
         for example in examples:
@@ -172,17 +187,21 @@ class Scorer:
                     gold_i = align.x2y[token.i].dataXd[0, 0]
                     pred_tags.add((gold_i, getter(token, attr)))
             tag_score.score_set(pred_tags, gold_tags)
-        return {
+        return {f"{attr}_acc": tag_score.fscore}
-            attr + "_acc": tag_score.fscore,
-        }
 
     @staticmethod
-    def score_token_attr_per_feat(examples, attr, getter=getattr, **cfg):
+    def score_token_attr_per_feat(
+        examples: Iterable[Example],
+        attr: str,
+        *,
+        getter: Callable[[Token, str], Any] = getattr,
+        **cfg,
+    ):
         """Return PRF scores per feat for a token attribute in UFEATS format.
 
         examples (Iterable[Example]): Examples to score
         attr (str): The attribute to score.
-        getter (callable): Defaults to getattr. If provided,
+        getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
             getter(token, attr) should return the value of the attribute for an
             individual token.
         RETURNS (dict): A dictionary containing the per-feat PRF scores unders
@@ -223,20 +242,26 @@ class Scorer:
                 per_feat[field].score_set(
                     pred_per_feat.get(field, set()), gold_per_feat.get(field, set()),
                 )
-        return {
+        return {f"{attr}_per_feat": per_feat}
-            attr + "_per_feat": per_feat,
-        }
 
     @staticmethod
-    def score_spans(examples, attr, getter=getattr, **cfg):
+    def score_spans(
+        examples: Iterable[Example],
+        attr: str,
+        *,
+        getter: Callable[[Doc, str], Any] = getattr,
+        **cfg,
+    ) -> Dict[str, Any]:
         """Returns PRF scores for labeled spans.
 
         examples (Iterable[Example]): Examples to score
         attr (str): The attribute to score.
-        getter (callable): Defaults to getattr. If provided,
+        getter (Callable[[Doc, str], Any]): Defaults to getattr. If provided,
             getter(doc, attr) should return the spans for the individual doc.
-        RETURNS (dict): A dictionary containing the PRF scores under the
+        RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
-            keys attr_p/r/f and the per-type PRF scores under attr_per_type.
+            the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
+
+        DOCS: https://spacy.io/api/scorer#score_spans
         """
         score = PRFScore()
         score_per_type = dict()
@@ -256,14 +281,12 @@ class Scorer:
             # Find all predidate labels, for all and per type
             gold_spans = set()
             pred_spans = set()
-
             # Special case for ents:
             # If we have missing values in the gold, we can't easily tell
             # whether our NER predictions are true.
             # It seems bad but it's what we've always done.
             if attr == "ents" and not all(token.ent_iob != 0 for token in gold_doc):
                 continue
-
             for span in getter(gold_doc, attr):
                 gold_span = (span.label_, span.start, span.end - 1)
                 gold_spans.add(gold_span)
@@ -279,38 +302,39 @@ class Scorer:
             # Score for all labels
             score.score_set(pred_spans, gold_spans)
         results = {
-            attr + "_p": score.precision,
+            f"{attr}_p": score.precision,
-            attr + "_r": score.recall,
+            f"{attr}_r": score.recall,
-            attr + "_f": score.fscore,
+            f"{attr}_f": score.fscore,
-            attr + "_per_type": {k: v.to_dict() for k, v in score_per_type.items()},
+            f"{attr}_per_type": {k: v.to_dict() for k, v in score_per_type.items()},
         }
         return results
 
     @staticmethod
     def score_cats(
-        examples,
+        examples: Iterable[Example],
-        attr,
+        attr: str,
-        getter=getattr,
+        *,
-        labels=[],
+        getter: Callable[[Doc, str], Any] = getattr,
-        multi_label=True,
+        labels: Iterable[str] = tuple(),
-        positive_label=None,
+        multi_label: bool = True,
-        **cfg
+        positive_label: Optional[str] = None,
-    ):
+        **cfg,
+    ) -> Dict[str, Any]:
         """Returns PRF and ROC AUC scores for a doc-level attribute with a
         dict with scores for each label like Doc.cats. The reported overall
         score depends on the scorer settings.
 
         examples (Iterable[Example]): Examples to score
         attr (str): The attribute to score.
-        getter (callable): Defaults to getattr. If provided,
+        getter (Callable[[Doc, str], Any]): Defaults to getattr. If provided,
             getter(doc, attr) should return the values for the individual doc.
         labels (Iterable[str]): The set of possible labels. Defaults to [].
         multi_label (bool): Whether the attribute allows multiple labels.
             Defaults to True.
         positive_label (str): The positive label for a binary task with
             exclusive classes. Defaults to None.
-        RETURNS (dict): A dictionary containing the scores, with inapplicable
+        RETURNS (Dict[str, Any]): A dictionary containing the scores, with
-                scores as None:
+            inapplicable scores as None:
             for all:
                 attr_score (one of attr_f / attr_macro_f / attr_macro_auc),
                 attr_score_desc (text description of the overall score),
@@ -319,6 +343,8 @@ class Scorer:
             for binary exclusive with positive label: attr_p/r/f
             for 3+ exclusive classes, macro-averaged fscore: attr_macro_f
             for multilabel, macro-averaged AUC: attr_macro_auc
+
+        DOCS: https://spacy.io/api/scorer#score_cats
         """
         score = PRFScore()
         f_per_type = dict()
@@ -367,64 +393,67 @@ class Scorer:
                     )
                 )
         results = {
-            attr + "_score": None,
+            f"{attr}_score": None,
-            attr + "_score_desc": None,
+            f"{attr}_score_desc": None,
-            attr + "_p": None,
+            f"{attr}_p": None,
-            attr + "_r": None,
+            f"{attr}_r": None,
-            attr + "_f": None,
+            f"{attr}_f": None,
-            attr + "_macro_f": None,
+            f"{attr}_macro_f": None,
-            attr + "_macro_auc": None,
+            f"{attr}_macro_auc": None,
-            attr + "_f_per_type": {k: v.to_dict() for k, v in f_per_type.items()},
+            f"{attr}_f_per_type": {k: v.to_dict() for k, v in f_per_type.items()},
-            attr + "_auc_per_type": {k: v.score for k, v in auc_per_type.items()},
+            f"{attr}_auc_per_type": {k: v.score for k, v in auc_per_type.items()},
         }
         if len(labels) == 2 and not multi_label and positive_label:
-            results[attr + "_p"] = score.precision
+            results[f"{attr}_p"] = score.precision
-            results[attr + "_r"] = score.recall
+            results[f"{attr}_r"] = score.recall
-            results[attr + "_f"] = score.fscore
+            results[f"{attr}_f"] = score.fscore
-            results[attr + "_score"] = results[attr + "_f"]
+            results[f"{attr}_score"] = results[f"{attr}_f"]
-            results[attr + "_score_desc"] = "F (" + positive_label + ")"
+            results[f"{attr}_score_desc"] = f"F ({positive_label})"
         elif not multi_label:
-            results[attr + "_macro_f"] = sum(
+            results[f"{attr}_macro_f"] = sum(
                 [score.fscore for label, score in f_per_type.items()]
             ) / (len(f_per_type) + 1e-100)
-            results[attr + "_score"] = results[attr + "_macro_f"]
+            results[f"{attr}_score"] = results[f"{attr}_macro_f"]
-            results[attr + "_score_desc"] = "macro F"
+            results[f"{attr}_score_desc"] = "macro F"
         else:
-            results[attr + "_macro_auc"] = max(
+            results[f"{attr}_macro_auc"] = max(
                 sum([score.score for label, score in auc_per_type.items()])
                 / (len(auc_per_type) + 1e-100),
                 -1,
             )
-            results[attr + "_score"] = results[attr + "_macro_auc"]
+            results[f"{attr}_score"] = results[f"{attr}_macro_auc"]
-            results[attr + "_score_desc"] = "macro AUC"
+            results[f"{attr}_score_desc"] = "macro AUC"
         return results
 
     @staticmethod
     def score_deps(
-        examples,
+        examples: Iterable[Example],
-        attr,
+        attr: str,
-        getter=getattr,
+        *,
-        head_attr="head",
+        getter: Callable[[Token, str], Any] = getattr,
-        head_getter=getattr,
+        head_attr: str = "head",
-        ignore_labels=tuple(),
+        head_getter: Callable[[Token, str], Any] = getattr,
-        **cfg
+        ignore_labels: Tuple[str] = tuple(),
-    ):
+        **cfg,
+    ) -> Dict[str, Any]:
         """Returns the UAS, LAS, and LAS per type scores for dependency
         parses.
 
         examples (Iterable[Example]): Examples to score
         attr (str): The attribute containing the dependency label.
-        getter (callable): Defaults to getattr. If provided,
+        getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
             getter(token, attr) should return the value of the attribute for an
             individual token.
         head_attr (str): The attribute containing the head token. Defaults to
             'head'.
-        head_getter (callable): Defaults to getattr. If provided,
+        head_getter (Callable[[Token, str], Any]): Defaults to getattr. If provided,
             head_getter(token, attr) should return the value of the head for an
             individual token.
         ignore_labels (Tuple): Labels to ignore while scoring (e.g., punct).
-        RETURNS (dict): A dictionary containing the scores:
+        RETURNS (Dict[str, Any]): A dictionary containing the scores:
             attr_uas, attr_las, and attr_las_per_type.
+
+        DOCS: https://spacy.io/api/scorer#score_deps
         """
         unlabelled = PRFScore()
         labelled = PRFScore()
@@ -482,10 +511,11 @@ class Scorer:
                 set(item[:2] for item in pred_deps), set(item[:2] for item in gold_deps)
             )
         return {
-            attr + "_uas": unlabelled.fscore,
+            f"{attr}_uas": unlabelled.fscore,
-            attr + "_las": labelled.fscore,
+            f"{attr}_las": labelled.fscore,
-            attr
+            f"{attr}_las_per_type": {
-            + "_las_per_type": {k: v.to_dict() for k, v in labelled_per_dep.items()},
+                k: v.to_dict() for k, v in labelled_per_dep.items()
+            },
         }
 
 

spacy/tests/parser/test_arc_eager_oracle.py

@@ -4,8 +4,8 @@ from spacy import registry
 from spacy.gold import Example
 from spacy.pipeline import DependencyParser
 from spacy.tokens import Doc
-from spacy.syntax.nonproj import projectivize
+from spacy.pipeline._parser_internals.nonproj import projectivize
-from spacy.syntax.arc_eager import ArcEager
+from spacy.pipeline._parser_internals.arc_eager import ArcEager
 from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
 
 

spacy/tests/parser/test_ner.py

@@ -5,7 +5,7 @@ from spacy.lang.en import English
 
 from spacy.language import Language
 from spacy.lookups import Lookups
-from spacy.syntax.ner import BiluoPushDown
+from spacy.pipeline._parser_internals.ner import BiluoPushDown
 from spacy.gold import Example
 from spacy.tokens import Doc
 from spacy.vocab import Vocab

spacy/tests/parser/test_neural_parser.py

@@ -3,8 +3,8 @@ import pytest
 from spacy import registry
 from spacy.gold import Example
 from spacy.vocab import Vocab
-from spacy.syntax.arc_eager import ArcEager
+from spacy.pipeline._parser_internals.arc_eager import ArcEager
-from spacy.syntax.nn_parser import Parser
+from spacy.pipeline.transition_parser import Parser
 from spacy.tokens.doc import Doc
 from thinc.api import Model
 from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL

spacy/tests/parser/test_nonproj.py

@@ -1,7 +1,7 @@
 import pytest
-from spacy.syntax.nonproj import ancestors, contains_cycle, is_nonproj_arc
+from spacy.pipeline._parser_internals.nonproj import ancestors, contains_cycle, is_nonproj_arc
-from spacy.syntax.nonproj import is_nonproj_tree
+from spacy.pipeline._parser_internals.nonproj import is_nonproj_tree
-from spacy.syntax import nonproj
+from spacy.pipeline._parser_internals import nonproj
 
 from ..util import get_doc
 

spacy/tests/pipeline/test_analysis.py

@@ -1,15 +1,10 @@
-import spacy.language
 from spacy.language import Language
-from spacy.pipe_analysis import print_summary, validate_attrs
+from spacy.pipe_analysis import get_attr_info, validate_attrs
-from spacy.pipe_analysis import get_assigns_for_attr, get_requires_for_attr
-from spacy.pipe_analysis import count_pipeline_interdependencies
 from mock import Mock
 import pytest
 
 
 def test_component_decorator_assigns():
-    spacy.language.ENABLE_PIPELINE_ANALYSIS = True
-
     @Language.component("c1", assigns=["token.tag", "doc.tensor"])
     def test_component1(doc):
         return doc
@@ -32,10 +27,11 @@ def test_component_decorator_assigns():
 
     nlp = Language()
     nlp.add_pipe("c1")
-    with pytest.warns(UserWarning):
+    nlp.add_pipe("c2")
-        nlp.add_pipe("c2")
+    problems = nlp.analyze_pipes()["problems"]
+    assert problems["c2"] == ["token.pos"]
     nlp.add_pipe("c3")
-    assert get_assigns_for_attr(nlp, "doc.tensor") == ["c1", "c2"]
+    assert get_attr_info(nlp, "doc.tensor")["assigns"] == ["c1", "c2"]
     nlp.add_pipe("c1", name="c4")
     test_component4_meta = nlp.get_pipe_meta("c1")
     assert test_component4_meta.factory == "c1"
@@ -43,9 +39,8 @@ def test_component_decorator_assigns():
     assert not Language.has_factory("c4")
     assert nlp.pipe_factories["c1"] == "c1"
     assert nlp.pipe_factories["c4"] == "c1"
-    assert get_assigns_for_attr(nlp, "doc.tensor") == ["c1", "c2", "c4"]
+    assert get_attr_info(nlp, "doc.tensor")["assigns"] == ["c1", "c2", "c4"]
-    assert get_requires_for_attr(nlp, "token.pos") == ["c2"]
+    assert get_attr_info(nlp, "token.pos")["requires"] == ["c2"]
-    assert print_summary(nlp, no_print=True)
     assert nlp("hello world")
 
 
@@ -100,7 +95,6 @@ def test_analysis_validate_attrs_invalid(attr):
 
 def test_analysis_validate_attrs_remove_pipe():
     """Test that attributes are validated correctly on remove."""
-    spacy.language.ENABLE_PIPELINE_ANALYSIS = True
 
     @Language.component("pipe_analysis_c6", assigns=["token.tag"])
     def c1(doc):
@@ -112,26 +106,9 @@ def test_analysis_validate_attrs_remove_pipe():
 
     nlp = Language()
     nlp.add_pipe("pipe_analysis_c6")
-    with pytest.warns(UserWarning):
+    nlp.add_pipe("pipe_analysis_c7")
-        nlp.add_pipe("pipe_analysis_c7")
+    problems = nlp.analyze_pipes()["problems"]
-    with pytest.warns(None) as record:
+    assert problems["pipe_analysis_c7"] == ["token.pos"]
-        nlp.remove_pipe("pipe_analysis_c7")
+    nlp.remove_pipe("pipe_analysis_c7")
-    assert not record.list
+    problems = nlp.analyze_pipes()["problems"]
-
+    assert all(p == [] for p in problems.values())
-
-def test_pipe_interdependencies():
-    prefix = "test_pipe_interdependencies"
-
-    @Language.component(f"{prefix}.fancifier", assigns=("doc._.fancy",))
-    def fancifier(doc):
-        return doc
-
-    @Language.component(f"{prefix}.needer", requires=("doc._.fancy",))
-    def needer(doc):
-        return doc
-
-    nlp = Language()
-    nlp.add_pipe(f"{prefix}.fancifier")
-    nlp.add_pipe(f"{prefix}.needer")
-    counts = count_pipeline_interdependencies(nlp)
-    assert counts == [1, 0]

spacy/tests/pipeline/test_textcat.py

@@ -118,7 +118,7 @@ def test_overfitting_IO():
 
     # Test scoring
     scores = nlp.evaluate(
-        train_examples, component_cfg={"scorer": {"positive_label": "POSITIVE"}}
+        train_examples, scorer_cfg={"positive_label": "POSITIVE"}
     )
     assert scores["cats_f"] == 1.0
     assert scores["cats_score"] == 1.0

spacy/util.py

@@ -7,7 +7,7 @@ import importlib.util
 import re
 from pathlib import Path
 import thinc
-from thinc.api import NumpyOps, get_current_ops, Adam, Config, Optimizer, Model
+from thinc.api import NumpyOps, get_current_ops, Adam, Config, Optimizer
 import functools
 import itertools
 import numpy.random
@@ -24,8 +24,6 @@ import tempfile
 import shutil
 import shlex
 import inspect
-from thinc.types import Unserializable
-
 
 try:
     import cupy.random

website/docs/api/architectures.md

@@ -6,6 +6,7 @@ menu:
   - ['Tok2Vec', 'tok2vec']
   - ['Transformers', 'transformers']
   - ['Parser & NER', 'parser']
+  - ['Tagging', 'tagger']
   - ['Text Classification', 'textcat']
   - ['Entity Linking', 'entitylinker']
 ---
@@ -18,6 +19,30 @@ TODO: intro and how architectures work, link to
 
 ### spacy.HashEmbedCNN.v1 {#HashEmbedCNN}
 
+<!-- TODO: intro -->
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.HashEmbedCNN.v1"
+> # TODO: ...
+>
+> [model.tok2vec]
+> # ...
+> ```
+
+| Name                 | Type  | Description |
+| -------------------- | ----- | ----------- |
+| `width`              | int   |             |
+| `depth`              | int   |             |
+| `embed_size`         | int   |             |
+| `window_size`        | int   |             |
+| `maxout_pieces`      | int   |             |
+| `subword_features`   | bool  |             |
+| `dropout`            | float |             |
+| `pretrained_vectors` | bool  |             |
+
 ### spacy.HashCharEmbedCNN.v1 {#HashCharEmbedCNN}
 
 ### spacy.HashCharEmbedBiLSTM.v1 {#HashCharEmbedBiLSTM}
@@ -99,6 +124,28 @@ architectures into your training config.
 | `use_upper`         | bool                                       |             |
 | `nO`                | int                                        |             |
 
+## Tagging architectures {#tagger source="spacy/ml/models/tagger.py"}
+
+### spacy.Tagger.v1 {#Tagger}
+
+<!-- TODO: intro -->
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.Tagger.v1"
+> nO = null
+>
+> [model.tok2vec]
+> # ...
+> ```
+
+| Name      | Type                                       | Description |
+| --------- | ------------------------------------------ | ----------- |
+| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) |             |
+| `nO`      | int                                        |             |
+
 ## Text classification architectures {#textcat source="spacy/ml/models/textcat.py"}
 
 ### spacy.TextCatEnsemble.v1 {#TextCatEnsemble}
@@ -112,3 +159,21 @@ architectures into your training config.
 ## Entity linking architectures {#entitylinker source="spacy/ml/models/entity_linker.py"}
 
 ### spacy.EntityLinker.v1 {#EntityLinker}
+
+<!-- TODO: intro -->
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.EntityLinker.v1"
+> nO = null
+>
+> [model.tok2vec]
+> # ...
+> ```
+
+| Name      | Type                                       | Description |
+| --------- | ------------------------------------------ | ----------- |
+| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) |             |
+| `nO`      | int                                        |             |

website/docs/api/dependencyparser.md

@@ -29,9 +29,11 @@ architectures and their arguments and hyperparameters.
 > nlp.add_pipe("parser", config=config)
 > ```
 
+<!-- TODO: finish API docs -->
+
 | Setting | Type                                       | Description       | Default                                                           |
 | ------- | ------------------------------------------ | ----------------- | ----------------------------------------------------------------- |
-| `moves` | list                                       | <!-- TODO: -->    | `None`                                                            |
+| `moves` | list                                       |                   | `None`                                                            |
 | `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransitionBasedParser](/api/architectures#TransitionBasedParser) |
 
 ```python
@@ -59,17 +61,19 @@ Create a new pipeline instance. In your application, you would normally use a
 shortcut for this and instantiate the component using its string name and
 [`nlp.add_pipe`](/api/language#add_pipe).
 
+<!-- TODO: finish API docs -->
+
 | Name                          | Type                                       | Description                                                                                 |
 | ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
 | `vocab`                       | `Vocab`                                    | The shared vocabulary.                                                                      |
 | `model`                       | [`Model`](https://thinc.ai/docs/api-model) | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component.             |
 | `name`                        | str                                        | String name of the component instance. Used to add entries to the `losses` during training. |
-| `moves`                       | list                                       | <!-- TODO: -->                                                                              |
+| `moves`                       | list                                       |                                                                                             |
 | _keyword-only_                |                                            |                                                                                             |
-| `update_with_oracle_cut_size` | int                                        | <!-- TODO: -->                                                                              |
+| `update_with_oracle_cut_size` | int                                        |                                                                                             |
-| `multitasks`                  | `Iterable`                                 | <!-- TODO: -->                                                                              |
+| `multitasks`                  | `Iterable`                                 |                                                                                             |
-| `learn_tokens`                | bool                                       | <!-- TODO: -->                                                                              |
+| `learn_tokens`                | bool                                       |                                                                                             |
-| `min_action_freq`             | int                                        | <!-- TODO: -->                                                                              |
+| `min_action_freq`             | int                                        |                                                                                             |
 
 ## DependencyParser.\_\_call\_\_ {#call tag="method"}
 

website/docs/api/entitylinker.md

@@ -32,12 +32,14 @@ architectures and their arguments and hyperparameters.
 > nlp.add_pipe("entity_linker", config=config)
 > ```
 
+<!-- TODO: finish API docs -->
+
 | Setting          | Type                                       | Description       | Default                                         |
 | ---------------- | ------------------------------------------ | ----------------- | ----------------------------------------------- |
-| `kb`             | `KnowledgeBase`                            | <!-- TODO: -->    | `None`                                          |
+| `kb`             | `KnowledgeBase`                            |                   | `None`                                          |
-| `labels_discard` | `Iterable[str]`                            | <!-- TODO: -->    | `[]`                                            |
+| `labels_discard` | `Iterable[str]`                            |                   | `[]`                                            |
-| `incl_prior`     | bool                                       | <!-- TODO: -->    |  `True`                                         |
+| `incl_prior`     | bool                                       |                   |  `True`                                         |
-| `incl_context`   | bool                                       | <!-- TODO: -->    | `True`                                          |
+| `incl_context`   | bool                                       |                   | `True`                                          |
 | `model`          | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [EntityLinker](/api/architectures#EntityLinker) |
 
 ```python
@@ -65,16 +67,18 @@ Create a new pipeline instance. In your application, you would normally use a
 shortcut for this and instantiate the component using its string name and
 [`nlp.add_pipe`](/api/language#add_pipe).
 
+<!-- TODO: finish API docs -->
+
 | Name             | Type            | Description                                                                                 |
 | ---------------- | --------------- | ------------------------------------------------------------------------------------------- |
 | `vocab`          | `Vocab`         | The shared vocabulary.                                                                      |
 | `model`          | `Model`         | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component.             |
 | `name`           | str             | String name of the component instance. Used to add entries to the `losses` during training. |
 | _keyword-only_   |                 |                                                                                             |
-| `kb`             | `KnowlegeBase`  | <!-- TODO: -->                                                                              |
+| `kb`             | `KnowlegeBase`  |                                                                                             |
-| `labels_discard` | `Iterable[str]` | <!-- TODO: -->                                                                              |
+| `labels_discard` | `Iterable[str]` |                                                                                             |
-| `incl_prior`     | bool            | <!-- TODO: -->                                                                              |
+| `incl_prior`     | bool            |                                                                                             |
-| `incl_context`   | bool            | <!-- TODO: -->                                                                              |
+| `incl_context`   | bool            |                                                                                             |
 
 ## EntityLinker.\_\_call\_\_ {#call tag="method"}
 

website/docs/api/entityrecognizer.md

@@ -29,9 +29,11 @@ architectures and their arguments and hyperparameters.
 > nlp.add_pipe("ner", config=config)
 > ```
 
+<!-- TODO: finish API docs -->
+
 | Setting | Type                                       | Description       | Default                                                           |
 | ------- | ------------------------------------------ | ----------------- | ----------------------------------------------------------------- |
-| `moves` | list                                       | <!-- TODO: -->    | `None`                                                            |
+| `moves` | list                                       |                   | `None`                                                            |
 | `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TransitionBasedParser](/api/architectures#TransitionBasedParser) |
 
 ```python
@@ -59,17 +61,19 @@ Create a new pipeline instance. In your application, you would normally use a
 shortcut for this and instantiate the component using its string name and
 [`nlp.add_pipe`](/api/language#add_pipe).
 
+<!-- TODO: finish API docs -->
+
 | Name                          | Type                                       | Description                                                                                 |
 | ----------------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
 | `vocab`                       | `Vocab`                                    | The shared vocabulary.                                                                      |
 | `model`                       | [`Model`](https://thinc.ai/docs/api-model) | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component.             |
 | `name`                        | str                                        | String name of the component instance. Used to add entries to the `losses` during training. |
-| `moves`                       | list                                       | <!-- TODO: -->                                                                              |
+| `moves`                       | list                                       |                                                                                             |
 | _keyword-only_                |                                            |                                                                                             |
-| `update_with_oracle_cut_size` | int                                        | <!-- TODO: -->                                                                              |
+| `update_with_oracle_cut_size` | int                                        |                                                                                             |
-| `multitasks`                  | `Iterable`                                 | <!-- TODO: -->                                                                              |
+| `multitasks`                  | `Iterable`                                 |                                                                                             |
-| `learn_tokens`                | bool                                       | <!-- TODO: -->                                                                              |
+| `learn_tokens`                | bool                                       |                                                                                             |
-| `min_action_freq`             | int                                        | <!-- TODO: -->                                                                              |
+| `min_action_freq`             | int                                        |                                                                                             |
 
 ## EntityRecognizer.\_\_call\_\_ {#call tag="method"}
 

website/docs/api/example.md

@@ -8,9 +8,8 @@ new: 3.0
 
 An `Example` holds the information for one training instance. It stores two
 `Doc` objects: one for holding the gold-standard reference data, and one for
-holding the predictions of the pipeline. An `Alignment` <!-- TODO: link? -->
+holding the predictions of the pipeline. An `Alignment` object stores the
-object stores the alignment between these two documents, as they can differ in
+alignment between these two documents, as they can differ in tokenization.
-tokenization.
 
 ## Example.\_\_init\_\_ {#init tag="method"}
 

website/docs/api/language.md

@@ -98,10 +98,10 @@ decorator. For more details and examples, see the
 | ----------------------- | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `name`                  | str                  | The name of the component factory.                                                                                                                                                                                          |
 | _keyword-only_          |                      |                                                                                                                                                                                                                             |
-| `assigns`               | `Iterable[str]`      | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something -->                                                                               |
+| `assigns`               | `Iterable[str]`      | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis)..                                                                           |
-| `requires`              | `Iterable[str]`      | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something -->                                                                               |
+| `requires`              | `Iterable[str]`      | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                            |
-| `retokenizes`           | bool                 | Whether the component changes tokenization. Used for pipeline analysis. <!-- TODO: link to something -->                                                                                                                    |
+| `retokenizes`           | bool                 | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                                                                 |
-| `scores`                | `Iterable[str]`      | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`.                                                                                                                                  |
+| `scores`                | `Iterable[str]`      | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                  |
 | `default_score_weights` | `Dict[str, float]`   | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. |
 | `func`                  | `Optional[Callable]` | Optional function if not used a a decorator.                                                                                                                                                                                |
 
@@ -146,10 +146,10 @@ examples, see the
 | `name`                  | str                  | The name of the component factory.                                                                                                                                                                                          |
 | _keyword-only_          |                      |                                                                                                                                                                                                                             |
 | `default_config`        | `Dict[str, any]`     | The default config, describing the default values of the factory arguments.                                                                                                                                                 |
-| `assigns`               | `Iterable[str]`      | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something -->                                                                               |
+| `assigns`               | `Iterable[str]`      | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                            |
-| `requires`              | `Iterable[str]`      | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something -->                                                                               |
+| `requires`              | `Iterable[str]`      | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                            |
-| `retokenizes`           | bool                 | Whether the component changes tokenization. Used for pipeline analysis. <!-- TODO: link to something -->                                                                                                                    |
+| `retokenizes`           | bool                 | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                                                                 |
-| `scores`                | `Iterable[str]`      | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`.                                                                                                                                  |
+| `scores`                | `Iterable[str]`      | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                  |
 | `default_score_weights` | `Dict[str, float]`   | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. |
 | `func`                  | `Optional[Callable]` | Optional function if not used a a decorator.                                                                                                                                                                                |
 
@@ -302,6 +302,7 @@ Evaluate a model's pipeline components.
 | `batch_size`    | int                             | The batch size to use.                                                                                 |
 | `scorer`        | `Scorer`                        | Optional [`Scorer`](/api/scorer) to use. If not passed in, a new one will be created.                  |
 | `component_cfg` | `Dict[str, dict]`               | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. |
+| `scorer_cfg`    | `Dict[str, Any]`                | Optional dictionary of keyword arguments for the `Scorer`. Defaults to `None`.                         |
 | **RETURNS**     | `Dict[str, Union[float, dict]]` | A dictionary of evaluation scores.                                                                     |
 
 ## Language.use_params {#use_params tag="contextmanager, method"}
@@ -597,6 +598,97 @@ contains the information about the component and its default provided by the
 | `name`      | str                           | The pipeline component name. |
 | **RETURNS** | [`FactoryMeta`](#factorymeta) |  The factory meta.           |
 
+## Language.analyze_pipes {#analyze_pipes tag="method" new="3"}
+
+Analyze the current pipeline components and show a summary of the attributes
+they assign and require, and the scores they set. The data is based on the
+information provided in the [`@Language.component`](/api/language#component) and
+[`@Language.factory`](/api/language#factory) decorator. If requirements aren't
+met, e.g. if a component specifies a required property that is not set by a
+previous component, a warning is shown.
+
+<Infobox variant="warning" title="Important note">
+
+The pipeline analysis is static and does **not actually run the components**.
+This means that it relies on the information provided by the components
+themselves. If a custom component declares that it assigns an attribute but it
+doesn't, the pipeline analysis won't catch that.
+
+</Infobox>
+
+> #### Example
+>
+> ```python
+> nlp = spacy.blank("en")
+> nlp.add_pipe("tagger")
+> nlp.add_pipe("entity_linker")
+> analysis = nlp.analyze_pipes()
+> ```
+
+<Accordion title="Example output" spaced>
+
+```json
+### Structured
+{
+  "summary": {
+    "tagger": {
+      "assigns": ["token.tag"],
+      "requires": [],
+      "scores": ["tag_acc", "pos_acc", "lemma_acc"],
+      "retokenizes": false
+    },
+    "entity_linker": {
+      "assigns": ["token.ent_kb_id"],
+      "requires": ["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
+      "scores": [],
+      "retokenizes": false
+    }
+  },
+  "problems": {
+    "tagger": [],
+    "entity_linker": ["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"]
+  },
+  "attrs": {
+    "token.ent_iob": { "assigns": [], "requires": ["entity_linker"] },
+    "doc.ents": { "assigns": [], "requires": ["entity_linker"] },
+    "token.ent_kb_id": { "assigns": ["entity_linker"], "requires": [] },
+    "doc.sents": { "assigns": [], "requires": ["entity_linker"] },
+    "token.tag": { "assigns": ["tagger"], "requires": [] },
+    "token.ent_type": { "assigns": [], "requires": ["entity_linker"] }
+  }
+}
+```
+
+```
+### Pretty
+============================= Pipeline Overview =============================
+
+#   Component       Assigns           Requires         Scores      Retokenizes
+-   -------------   ---------------   --------------   ---------   -----------
+0   tagger          token.tag                          tag_acc     False
+                                                       pos_acc
+                                                       lemma_acc
+
+1   entity_linker   token.ent_kb_id   doc.ents                     False
+                                      doc.sents
+                                      token.ent_iob
+                                      token.ent_type
+
+
+================================ Problems (4) ================================
+⚠ 'entity_linker' requirements not met: doc.ents, doc.sents,
+token.ent_iob, token.ent_type
+```
+
+</Accordion>
+
+| Name           | Type        | Description                                                                                                                                                                                                    |
+| -------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| _keyword-only_ |             |                                                                                                                                                                                                                |
+| `keys`         | `List[str]` | The values to display in the table. Corresponds to attributes of the [`FactoryMeta`](/api/language#factorymeta). Defaults to `["assigns", "requires", "scores", "retokenizes"]`.                               |
+| `pretty`       | bool        | Pretty-print the results as a table. Defaults to `False`.                                                                                                                                                      |
+| **RETURNS**    | dict        | Dictionary containing the pipe analysis, keyed by `"summary"` (component meta by pipe), `"problems"` (attribute names by pipe) and `"attrs"` (pipes that assign and require an attribute, keyed by attribute). |
+
 ## Language.meta {#meta tag="property"}
 
 Custom meta data for the Language class. If a model is loaded, contains meta
@@ -832,8 +924,8 @@ instance and factory instance.
 | ----------------------- | ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `factory`               | str                | The name of the registered component factory.                                                                                                                                                                               |
 | `default_config`        | `Dict[str, Any]`   | The default config, describing the default values of the factory arguments.                                                                                                                                                 |
-| `assigns`               | `Iterable[str]`    | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something -->                                                                               |
+| `assigns`               | `Iterable[str]`    | `Doc` or `Token` attributes assigned by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                            |
-| `requires`              | `Iterable[str]`    | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for pipeline analysis. <!-- TODO: link to something -->                                                                               |
+| `requires`              | `Iterable[str]`    | `Doc` or `Token` attributes required by this component, e.g. `["token.ent_id"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                            |
-| `retokenizes`           | bool               | Whether the component changes tokenization. Used for pipeline analysis. <!-- TODO: link to something -->                                                                                                                    |
+| `retokenizes`           | bool               | Whether the component changes tokenization. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                                                                 |
-| `scores`                | `Iterable[str]`    | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`.                                                                                                                                  |
+| `scores`                | `Iterable[str]`    | All scores set by the components if it's trainable, e.g. `["ents_f", "ents_r", "ents_p"]`. Used for [pipe analysis](/usage/processing-pipelines#analysis).                                                                  |
 | `default_score_weights` | `Dict[str, float]` | The scores to report during training, and their default weight towards the final score used to select the best model. Weights should sum to `1.0` per component and will be combined and normalized for the whole pipeline. |

website/docs/api/morphologizer.md

@@ -63,14 +63,16 @@ Create a new pipeline instance. In your application, you would normally use a
 shortcut for this and instantiate the component using its string name and
 [`nlp.add_pipe`](/api/language#add_pipe).
 
+<!-- TODO: finish API docs -->
+
 | Name           | Type    | Description                                                                                 |
 | -------------- | ------- | ------------------------------------------------------------------------------------------- |
 | `vocab`        | `Vocab` | The shared vocabulary.                                                                      |
 | `model`        | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component.             |
 | `name`         | str     | String name of the component instance. Used to add entries to the `losses` during training. |
 | _keyword-only_ |         |                                                                                             |
-| `labels_morph` | dict    | <!-- TODO: -->                                                                              |
+| `labels_morph` | dict    |                                                                                             |
-| `labels_pos`   | dict    | <!-- TODO: -->                                                                              |
+| `labels_pos`   | dict    |                                                                                             |
 
 ## Morphologizer.\_\_call\_\_ {#call tag="method"}
 

website/docs/api/scorer.md

@@ -6,10 +6,9 @@ source: spacy/scorer.py
 ---
 
 The `Scorer` computes evaluation scores. It's typically created by
-[`Language.evaluate`](/api/language#evaluate).
+[`Language.evaluate`](/api/language#evaluate). In addition, the `Scorer`
-
+provides a number of evaluation methods for evaluating [`Token`](/api/token) and
-In addition, the `Scorer` provides a number of evaluation methods for evaluating
+[`Doc`](/api/doc) attributes.
-`Token` and `Doc` attributes.
 
 ## Scorer.\_\_init\_\_ {#init tag="method"}
 
@@ -20,10 +19,10 @@ Create a new `Scorer`.
 > ```python
 > from spacy.scorer import Scorer
 >
-> # default scoring pipeline
+> # Default scoring pipeline
 > scorer = Scorer()
 >
-> # provided scoring pipeline
+> # Provided scoring pipeline
 > nlp = spacy.load("en_core_web_sm")
 > scorer = Scorer(nlp)
 > ```
@@ -40,16 +39,20 @@ scoring methods provided by the components in the pipeline.
 The returned `Dict` contains the scores provided by the individual pipeline
 components. For the scoring methods provided by the `Scorer` and use by the core
 pipeline components, the individual score names start with the `Token` or `Doc`
-attribute being scored: `token_acc`, `token_p/r/f`, `sents_p/r/f`, `tag_acc`,
+attribute being scored:
-`pos_acc`, `morph_acc`, `morph_per_feat`, `lemma_acc`, `dep_uas`, `dep_las`,
+
-`dep_las_per_type`, `ents_p/r/f`, `ents_per_type`, `textcat_macro_auc`,
+- `token_acc`, `token_p`, `token_r`, `token_f`,
-`textcat_macro_f`.
+- `sents_p`, `sents_r`, `sents_f`
+- `tag_acc`, `pos_acc`, `morph_acc`, `morph_per_feat`, `lemma_acc`
+- `dep_uas`, `dep_las`, `dep_las_per_type`
+- `ents_p`, `ents_r` `ents_f`, `ents_per_type`
+- `textcat_macro_auc`, `textcat_macro_f`
 
 > #### Example
 >
 > ```python
 > scorer = Scorer()
-> scorer.score(examples)
+> scores = scorer.score(examples)
 > ```
 
 | Name        | Type                | Description                                                                                   |
@@ -57,78 +60,148 @@ attribute being scored: `token_acc`, `token_p/r/f`, `sents_p/r/f`, `tag_acc`,
 | `examples`  | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
 | **RETURNS** | `Dict`              | A dictionary of scores.                                                                       |
 
-## Scorer.score_tokenization {#score_tokenization tag="staticmethod"}
+## Scorer.score_tokenization {#score_tokenization tag="staticmethod" new="3"}
 
 Scores the tokenization:
 
-- `token_acc`: # correct tokens / # gold tokens
+- `token_acc`: number of correct tokens / number of gold tokens
-- `token_p/r/f`: PRF for token character spans
+- `token_p`, `token_r`, `token_f`: precision, recall and F-score for token
+  character spans
+
+> #### Example
+>
+> ```python
+> scores = Scorer.score_tokenization(examples)
+> ```
 
 | Name        | Type                | Description                                                                                   |
 | ----------- | ------------------- | --------------------------------------------------------------------------------------------- |
 | `examples`  | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations. |
-| **RETURNS** | `Dict`              | A dictionary containing the scores `token_acc/p/r/f`.                                         |
+| **RETURNS** | `Dict`              | A dictionary containing the scores `token_acc`, `token_p`, `token_r`, `token_f`.              |
 
-## Scorer.score_token_attr {#score_token_attr tag="staticmethod"}
+## Scorer.score_token_attr {#score_token_attr tag="staticmethod" new="3"}
 
 Scores a single token attribute.
 
-| Name        | Type                | Description                                                                                                                   |
+> #### Example
-| ----------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+>
-| `examples`  | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
+> ```python
-| `attr`      | `str`               | The attribute to score.                                                                                                       |
+> scores = Scorer.score_token_attr(examples, "pos")
-| `getter`    | `callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
+> print(scores["pos_acc"])
-| **RETURNS** | `Dict`              | A dictionary containing the score `attr_acc`.                                                                                 |
+> ```
 
-## Scorer.score_token_attr_per_feat {#score_token_attr_per_feat tag="staticmethod"}
+| Name           | Type                | Description                                                                                                                   |
+| -------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| `examples`     | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
+| `attr`         | `str`               | The attribute to score.                                                                                                       |
+| _keyword-only_ |                     |                                                                                                                               |
+| `getter`       | `Callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
+| **RETURNS**    | `Dict[str, float]`  | A dictionary containing the score `{attr}_acc`.                                                                               |
 
-Scores a single token attribute per feature for a token attribute in UFEATS
+## Scorer.score_token_attr_per_feat {#score_token_attr_per_feat tag="staticmethod" new="3"}
+
+Scores a single token attribute per feature for a token attribute in
+[UFEATS](https://universaldependencies.org/format.html#morphological-annotation)
 format.
 
-| Name        | Type                | Description                                                                                                                   |
+> #### Example
-| ----------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+>
-| `examples`  | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
+> ```python
-| `attr`      | `str`               | The attribute to score.                                                                                                       |
+> scores = Scorer.score_token_attr_per_feat(examples, "morph")
-| `getter`    | `callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
+> print(scores["morph_per_feat"])
-| **RETURNS** | `Dict`              | A dictionary containing the per-feature PRF scores unders the key `attr_per_feat`.                                            |
+> ```
 
-## Scorer.score_spans {#score_spans tag="staticmethod"}
+| Name           | Type                | Description                                                                                                                   |
+| -------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| `examples`     | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
+| `attr`         | `str`               | The attribute to score.                                                                                                       |
+| _keyword-only_ |                     |                                                                                                                               |
+| `getter`       | `Callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
+| **RETURNS**    | `Dict`              | A dictionary containing the per-feature PRF scores under the key `{attr}_per_feat`.                                           |
+
+## Scorer.score_spans {#score_spans tag="staticmethod" new="3"}
 
 Returns PRF scores for labeled or unlabeled spans.
 
-| Name        | Type                | Description                                                                                                           |
+> #### Example
-| ----------- | ------------------- | --------------------------------------------------------------------------------------------------------------------- |
+>
-| `examples`  | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                         |
+> ```python
-| `attr`      | `str`               | The attribute to score.                                                                                               |
+> scores = Scorer.score_spans(examples, "ents")
-| `getter`    | `callable`          | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`.     |
+> print(scores["ents_f"])
-| **RETURNS** | `Dict`              | A dictionary containing the PRF scores under the keys `attr_p/r/f` and the per-type PRF scores under `attr_per_type`. |
+> ```
 
-## Scorer.score_deps {#score_deps tag="staticmethod"}
+| Name           | Type                | Description                                                                                                                                   |
+| -------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
+| `examples`     | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                                 |
+| `attr`         | `str`               | The attribute to score.                                                                                                                       |
+| _keyword-only_ |                     |                                                                                                                                               |
+| `getter`       | `Callable`          | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the `Span` objects for an individual `Doc`.                             |
+| **RETURNS**    | `Dict`              | A dictionary containing the PRF scores under the keys `{attr}_p`, `{attr}_r`, `{attr}_f` and the per-type PRF scores under `{attr}_per_type`. |
+
+## Scorer.score_deps {#score_deps tag="staticmethod" new="3"}
 
 Calculate the UAS, LAS, and LAS per type scores for dependency parses.
 
+> #### Example
+>
+> ```python
+> def dep_getter(token, attr):
+>     dep = getattr(token, attr)
+>     dep = token.vocab.strings.as_string(dep).lower()
+>     return dep
+>
+> scores = Scorer.score_deps(
+>     examples,
+>     "dep",
+>     getter=dep_getter,
+>     ignore_labels=("p", "punct")
+> )
+> print(scores["dep_uas"], scores["dep_las"])
+> ```
+
 | Name            | Type                | Description                                                                                                                   |
 | --------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
 | `examples`      | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                 |
 | `attr`          | `str`               | The attribute containing the dependency label.                                                                                |
-| `getter`        | `callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
+| _keyword-only_  |                     |                                                                                                                               |
+| `getter`        | `Callable`          | Defaults to `getattr`. If provided, `getter(token, attr)` should return the value of the attribute for an individual `Token`. |
 | `head_attr`     | `str`               | The attribute containing the head token.                                                                                      |
 | `head_getter`   | `callable`          | Defaults to `getattr`. If provided, `head_getter(token, attr)` should return the head for an individual `Token`.              |
 | `ignore_labels` | `Tuple`             | Labels to ignore while scoring (e.g., `punct`).                                                                               |
-| **RETURNS**     | `Dict`              | A dictionary containing the scores: `attr_uas`, `attr_las`, and `attr_las_per_type`.                                          |
+| **RETURNS**     | `Dict`              | A dictionary containing the scores: `{attr}_uas`, `{attr}_las`, and `{attr}_las_per_type`.                                    |
 
-## Scorer.score_cats {#score_cats tag="staticmethod"}
+## Scorer.score_cats {#score_cats tag="staticmethod" new="3"}
 
 Calculate PRF and ROC AUC scores for a doc-level attribute that is a dict
 containing scores for each label like `Doc.cats`. The reported overall score
-depends on the scorer settings.
+depends on the scorer settings:
 
-| Name             | Type                | Description                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+1. **all:** `{attr}_score` (one of `{attr}_f` / `{attr}_macro_f` /
-| ---------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+   `{attr}_macro_auc`), `{attr}_score_desc` (text description of the overall
-| `examples`       | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.                                                                                                                                                                                                                                                                                                                                                     |
+   score), `{attr}_f_per_type`, `{attr}_auc_per_type`
-| `attr`           | `str`               | The attribute to score.                                                                                                                                                                                                                                                                                                                                                                                                                           |
+2. **binary exclusive with positive label:** `{attr}_p`, `{attr}_r`, `{attr}_f`
-| `getter`         | `callable`          | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`.                                                                                                                                                                                                                                                                                                                                           |
+3. **3+ exclusive classes**, macro-averaged F-score: `{attr}_macro_f`;
-| labels           | `Iterable[str]`     | The set of possible labels. Defaults to `[]`.                                                                                                                                                                                                                                                                                                                                                                                                     |
+4. **multilabel**, macro-averaged AUC: `{attr}_macro_auc`
-| `multi_label`    | `bool`              | Whether the attribute allows multiple labels. Defaults to `True`.                                                                                                                                                                                                                                                                                                                                                                                 |
+
-| `positive_label` | `str`               | The positive label for a binary task with exclusive classes. Defaults to `None`.                                                                                                                                                                                                                                                                                                                                                                  |
+> #### Example
-| **RETURNS**      | `Dict`              | A dictionary containing the scores, with inapplicable scores as `None`: 1) for all: `attr_score` (one of `attr_f` / `attr_macro_f` / `attr_macro_auc`), `attr_score_desc` (text description of the overall score), `attr_f_per_type`, `attr_auc_per_type`; 2) for binary exclusive with positive label: `attr_p/r/f`; 3) for 3+ exclusive classes, macro-averaged fscore: `attr_macro_f`; 4) for multilabel, macro-averaged AUC: `attr_macro_auc` |
+>
+> ```python
+> labels = ["LABEL_A", "LABEL_B", "LABEL_C"]
+> scores = Scorer.score_cats(
+>     examples,
+>     "cats",
+>     labels=labels
+> )
+> print(scores["cats_macro_auc"])
+> ```
+
+| Name             | Type                | Description                                                                                             |
+| ---------------- | ------------------- | ------------------------------------------------------------------------------------------------------- |
+| `examples`       | `Iterable[Example]` | The `Example` objects holding both the predictions and the correct gold-standard annotations.           |
+| `attr`           | `str`               | The attribute to score.                                                                                 |
+| _keyword-only_   |                     |                                                                                                         |
+| `getter`         | `Callable`          | Defaults to `getattr`. If provided, `getter(doc, attr)` should return the cats for an individual `Doc`. |
+| labels           | `Iterable[str]`     | The set of possible labels. Defaults to `[]`.                                                           |
+| `multi_label`    | `bool`              | Whether the attribute allows multiple labels. Defaults to `True`.                                       |
+| `positive_label` | `str`               | The positive label for a binary task with exclusive classes. Defaults to `None`.                        |
+| **RETURNS**      | `Dict`              | A dictionary containing the scores, with inapplicable scores as `None`.                                 |

website/docs/api/top-level.md

@@ -290,6 +290,8 @@ factories.
 >     return Model("custom", forward, dims={"nO": nO})
 > ```
 
+<!-- TODO: finish table -->
+
 | Registry name     | Description                                                                                                                                                                                                                                       |
 | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `architectures`   | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`.                                                                          |
@@ -297,7 +299,7 @@ factories.
 | `languages`       | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points).                                                                                                                |
 | `lookups`         | Registry for large lookup tables available via `vocab.lookups`.                                                                                                                                                                                   |
 | `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points).                                                                            |
-| `assets`          | <!-- TODO: what is this used for again?-->                                                                                                                                                                                                        |
+| `assets`          |                                                                                                                                                                                                                                                   |
 | `optimizers`      | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers).                                                                                                                                                            |
 | `schedules`       | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules).                                                                                                                                                              |
 | `layers`          | Registry for functions that create [layers](https://thinc.ai/docs/api-layers).                                                                                                                                                                    |

website/docs/api/transformer.md

@@ -347,50 +347,52 @@ serialization by passing in the string names via the `exclude` argument.
 
 Transformer tokens and outputs for one `Doc` object.
 
-| Name      | Type                                               | Description                               |
+<!-- TODO: finish API docs, also mention "width" is property -->
-| --------- | -------------------------------------------------- | ----------------------------------------- |
+
-| `tokens`  | `Dict`                                             | <!-- TODO: -->                            |
+| Name      | Type                                               | Description |
-| `tensors` | `List[FloatsXd]`                                   | <!-- TODO: -->                            |
+| --------- | -------------------------------------------------- | ----------- |
-| `align`   | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | <!-- TODO: -->                            |
+| `tokens`  | `Dict`                                             |             |
-| `width`   | int                                                | <!-- TODO: also mention it's property --> |
+| `tensors` | `List[FloatsXd]`                                   |             |
+| `align`   | [`Ragged`](https://thinc.ai/docs/api-types#ragged) |             |
+| `width`   | int                                                |             |
 
 ### TransformerData.empty {#transformerdata-emoty tag="classmethod"}
 
-<!-- TODO: -->
+<!-- TODO: finish API docs -->
 
-| Name        | Type              | Description    |
+| Name        | Type              | Description |
-| ----------- | ----------------- | -------------- |
+| ----------- | ----------------- | ----------- |
-| **RETURNS** | `TransformerData` | <!-- TODO: --> |
+| **RETURNS** | `TransformerData` |             |
 
 ## FullTransformerBatch {#fulltransformerbatch tag="dataclass"}
 
-<!-- TODO: -->
+<!-- TODO: write, also mention doc_data is property -->
 
-| Name       | Type                                                                                                                       | Description                               |
+| Name       | Type                                                                                                                       | Description |
-| ---------- | -------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- |
+| ---------- | -------------------------------------------------------------------------------------------------------------------------- | ----------- |
-| `spans`    | `List[List[Span]]`                                                                                                         | <!-- TODO: -->                            |
+| `spans`    | `List[List[Span]]`                                                                                                         |             |
-| `tokens`   | [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) | <!-- TODO: -->                            |
+| `tokens`   | [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) |             |
-| `tensors`  | `List[torch.Tensor]`                                                                                                       | <!-- TODO: -->                            |
+| `tensors`  | `List[torch.Tensor]`                                                                                                       |             |
-| `align`    | [`Ragged`](https://thinc.ai/docs/api-types#ragged)                                                                         | <!-- TODO: -->                            |
+| `align`    | [`Ragged`](https://thinc.ai/docs/api-types#ragged)                                                                         |             |
-| `doc_data` | `List[TransformerData]`                                                                                                    | <!-- TODO: also mention it's property --> |
+| `doc_data` | `List[TransformerData]`                                                                                                    |             |
 
 ### FullTransformerBatch.unsplit_by_doc {#fulltransformerbatch-unsplit_by_doc tag="method"}
 
-<!-- TODO: -->
+<!-- TODO: write -->
 
-| Name        | Type                   | Description    |
+| Name        | Type                   | Description |
-| ----------- | ---------------------- | -------------- |
+| ----------- | ---------------------- | ----------- |
-| `arrays`    | `List[List[Floats3d]]` | <!-- TODO: --> |
+| `arrays`    | `List[List[Floats3d]]` |             |
-| **RETURNS** | `FullTransformerBatch` | <!-- TODO: --> |
+| **RETURNS** | `FullTransformerBatch` |             |
 
 ### FullTransformerBatch.split_by_doc {#fulltransformerbatch-split_by_doc tag="method"}
 
 Split a `TransformerData` object that represents a batch into a list with one
 `TransformerData` per `Doc`.
 
-| Name        | Type                    | Description    |
+| Name        | Type                    | Description |
-| ----------- | ----------------------- | -------------- |
+| ----------- | ----------------------- | ----------- |
-| **RETURNS** | `List[TransformerData]` | <!-- TODO: --> |
+| **RETURNS** | `List[TransformerData]` |             |
 
 ## Span getters {#span_getters tag="registered functions" source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/span_getters.py"}
 
@@ -421,11 +423,13 @@ getters using the `@registry.span_getters` decorator.
 
 The following built-in functions are available:
 
+<!-- TODO: finish API docs -->
+
 | Name               | Description                                                        |
 | ------------------ | ------------------------------------------------------------------ |
 | `doc_spans.v1`     | Create a span for each doc (no transformation, process each text). |
 | `sent_spans.v1`    | Create a span for each sentence if sentence boundaries are set.    |
-| `strided_spans.v1` | <!-- TODO: -->                                                     |
+| `strided_spans.v1` |                                                                    |
 
 ## Annotation setters {#annotation_setters tag="registered functions" source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/annotation_setters.py"}
 

website/docs/usage/processing-pipelines.md

@@ -231,10 +231,10 @@ available pipeline components and component functions.
 | `morphologizer` | [`Morphologizer`](/api/morphologizer)           | Assign morphological features and coarse-grained POS tags.                                |
 | `senter`        | [`SentenceRecognizer`](/api/sentencerecognizer) | Assign sentence boundaries.                                                               |
 | `sentencizer`   | [`Sentencizer`](/api/sentencizer)               | Add rule-based sentence segmentation without the dependency parse.                        |
-| `tok2vec`       | [`Tok2Vec`](/api/tok2vec)                       | <!-- TODO: -->                                                                            |
+| `tok2vec`       | [`Tok2Vec`](/api/tok2vec)                       |                                                                                           |
 | `transformer`   | [`Transformer`](/api/transformer)               | Assign the tokens and outputs of a transformer model.                                     |
 
-<!-- TODO: update with more components -->
+<!-- TODO: finish and update with more components -->
 
 <!-- TODO: explain default config and factories -->
 
@@ -311,6 +311,99 @@ nlp.rename_pipe("ner", "entityrecognizer")
 nlp.replace_pipe("tagger", my_custom_tagger)
 ```
 
+### Analyzing pipeline components {#analysis new="3"}
+
+The [`nlp.analyze_pipes`](/api/language#analyze_pipes) method analyzes the
+components in the current pipeline and outputs information about them, like the
+attributes they set on the [`Doc`](/api/doc) and [`Token`](/api/token), whether
+they retokenize the `Doc` and which scores they produce during training. It will
+also show warnings if components require values that aren't set by previous
+component – for instance, if the entity linker is used but no component that
+runs before it sets named entities. Setting `pretty=True` will pretty-print a
+table instead of only returning the structured data.
+
+> #### ✏️ Things to try
+>
+> 1. Add the components `"ner"` and `"sentencizer"` _before_ the entity linker.
+>    The analysis should now show no problems, because requirements are met.
+
+```python
+### {executable="true"}
+import spacy
+
+nlp = spacy.blank("en")
+nlp.add_pipe("tagger")
+# This is a problem because it needs entities and sentence boundaries
+nlp.add_pipe("entity_linker")
+analysis = nlp.analyze_pipes(pretty=True)
+```
+
+<Accordion title="Example output">
+
+```json
+### Structured
+{
+  "summary": {
+    "tagger": {
+      "assigns": ["token.tag"],
+      "requires": [],
+      "scores": ["tag_acc", "pos_acc", "lemma_acc"],
+      "retokenizes": false
+    },
+    "entity_linker": {
+      "assigns": ["token.ent_kb_id"],
+      "requires": ["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
+      "scores": [],
+      "retokenizes": false
+    }
+  },
+  "problems": {
+    "tagger": [],
+    "entity_linker": ["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"]
+  },
+  "attrs": {
+    "token.ent_iob": { "assigns": [], "requires": ["entity_linker"] },
+    "doc.ents": { "assigns": [], "requires": ["entity_linker"] },
+    "token.ent_kb_id": { "assigns": ["entity_linker"], "requires": [] },
+    "doc.sents": { "assigns": [], "requires": ["entity_linker"] },
+    "token.tag": { "assigns": ["tagger"], "requires": [] },
+    "token.ent_type": { "assigns": [], "requires": ["entity_linker"] }
+  }
+}
+```
+
+```
+### Pretty
+============================= Pipeline Overview =============================
+
+#   Component       Assigns           Requires         Scores      Retokenizes
+-   -------------   ---------------   --------------   ---------   -----------
+0   tagger          token.tag                          tag_acc     False
+                                                       pos_acc
+                                                       lemma_acc
+
+1   entity_linker   token.ent_kb_id   doc.ents                     False
+                                      doc.sents
+                                      token.ent_iob
+                                      token.ent_type
+
+
+================================ Problems (4) ================================
+⚠ 'entity_linker' requirements not met: doc.ents, doc.sents,
+token.ent_iob, token.ent_type
+```
+
+</Accordion>
+
+<Infobox variant="warning" title="Important note">
+
+The pipeline analysis is static and does **not actually run the components**.
+This means that it relies on the information provided by the components
+themselves. If a custom component declares that it assigns an attribute but it
+doesn't, the pipeline analysis won't catch that.
+
+</Infobox>
+
 ## Creating custom pipeline components {#custom-components}
 
 A pipeline component is a function that receives a `Doc` object, modifies it and
@@ -489,6 +582,8 @@ All other settings can be passed in by the user via the `config` argument on
 [`@Language.factory`](/api/language#factory) decorator also lets you define a
 `default_config` that's used as a fallback.
 
+<!-- TODO: add example of passing in a custom Python object via the config based on a registered function -->
+
 ```python
 ### With config {highlight="4,9"}
 import spacy

website/docs/usage/saving-loading.md

@@ -15,8 +15,6 @@ import Serialization101 from 'usage/101/\_serialization.md'
 
 ### Serializing the pipeline {#pipeline}
 
-<!-- TODO: update this -->
-
 When serializing the pipeline, keep in mind that this will only save out the
 **binary data for the individual components** to allow spaCy to restore them –
 not the entire objects. This is a good thing, because it makes serialization

website/docs/usage/training.md

@@ -3,7 +3,8 @@ title: Training Models
 next: /usage/projects
 menu:
   - ['Introduction', 'basics']
-  - ['CLI & Config', 'cli-config']
+  - ['Quickstart', 'quickstart']
+  - ['Config System', 'config']
   - ['Transfer Learning', 'transfer-learning']
   - ['Custom Models', 'custom-models']
   - ['Parallel Training', 'parallel-training']
@@ -29,12 +30,13 @@ ready-to-use spaCy models.
 
 </Infobox>
 
-## Training CLI & config {#cli-config}
+### Training CLI & config {#cli-config}
 
 <!-- TODO: intro describing the new v3 training philosophy -->
 
 The recommended way to train your spaCy models is via the
-[`spacy train`](/api/cli#train) command on the command line.
+[`spacy train`](/api/cli#train) command on the command line. You can pass in the
+following data and information:
 
 1. The **training and evaluation data** in spaCy's
    [binary `.spacy` format](/api/data-formats#binary-training) created using
@@ -68,38 +70,22 @@ workflows, from data preprocessing to training and packaging your model.
 
 </Project>
 
-<Accordion title="Understanding the training output">
+## Quickstart {#quickstart}
 
-When you train a model using the [`spacy train`](/api/cli#train) command, you'll
+> #### Instructions
-see a table showing metrics after each pass over the data. Here's what those
+>
-metrics means:
+> 1. Select your requirements and settings. The quickstart widget will
+>    auto-generate a recommended starter config for you.
+> 2. Use the buttons at the bottom to save the result to your clipboard or a
+>    file `config.cfg`.
+> 3. TOOD: recommended approach for filling config
+> 4. Run [`spacy train`](/api/cli#train) with your config and data.
 
-<!-- TODO: update table below and include note about scores in config -->
+import QuickstartTraining from 'widgets/quickstart-training.js'
 
-| Name       | Description                                                                                       |
+<QuickstartTraining />
-| ---------- | ------------------------------------------------------------------------------------------------- |
-| `Dep Loss` | Training loss for dependency parser. Should decrease, but usually not to 0.                       |
-| `NER Loss` | Training loss for named entity recognizer. Should decrease, but usually not to 0.                 |
-| `UAS`      | Unlabeled attachment score for parser. The percentage of unlabeled correct arcs. Should increase. |
-| `NER P.`   | NER precision on development data. Should increase.                                               |
-| `NER R.`   | NER recall on development data. Should increase.                                                  |
-| `NER F.`   | NER F-score on development data. Should increase.                                                 |
-| `Tag %`    | Fine-grained part-of-speech tag accuracy on development data. Should increase.                    |
-| `Token %`  | Tokenization accuracy on development data.                                                        |
-| `CPU WPS`  | Prediction speed on CPU in words per second, if available. Should stay stable.                    |
-| `GPU WPS`  | Prediction speed on GPU in words per second, if available. Should stay stable.                    |
 
-Note that if the development data has raw text, some of the gold-standard
+## Training config {#config}
-entities might not align to the predicted tokenization. These tokenization
-errors are **excluded from the NER evaluation**. If your tokenization makes it
-impossible for the model to predict 50% of your entities, your NER F-score might
-still look good.
-
-</Accordion>
-
----
-
-### Training config files {#config}
 
 > #### Migration from spaCy v2.x
 >
@@ -237,7 +223,70 @@ compound = 1.001
 
 <!-- TODO: refer to architectures API: /api/architectures. This should document the architectures in spacy/ml/models -->
 
-<!-- TODO: how do we document the default configs? -->
+### Metrics, training output and weighted scores {#metrics}
+
+When you train a model using the [`spacy train`](/api/cli#train) command, you'll
+see a table showing the metrics after each pass over the data. The available
+metrics **depend on the pipeline components**. Pipeline components also define
+which scores are shown and how they should be **weighted in the final score**
+that decides about the best model.
+
+The `training.score_weights` setting in your `config.cfg` lets you customize the
+scores shown in the table and how they should be weighted. In this example, the
+labeled dependency accuracy and NER F-score count towards the final score with
+40% each and the tagging accuracy makes up the remaining 20%. The tokenization
+accuracy and speed are both shown in the table, but not counted towards the
+score.
+
+> #### Why do I need score weights?
+>
+> At the end of your training process, you typically want to select the **best
+> model** – but what "best" means depends on the available components and your
+> specific use case. For instance, you may prefer a model with higher NER and
+> lower POS tagging accuracy over a model with lower NER and higher POS
+> accuracy. You can express this preference in the score weights, e.g. by
+> assigning `ents_f` (NER F-score) a higher weight.
+
+```ini
+[training.score_weights]
+dep_las = 0.4
+ents_f = 0.4
+tag_acc = 0.2
+token_acc = 0.0
+speed = 0.0
+```
+
+The `score_weights` don't _have to_ sum to `1.0` – but it's recommended. When
+you generate a config for a given pipeline, the score weights are generated by
+combining and normalizing the default score weights of the pipeline components.
+The default score weights are defined by each pipeline component via the
+`default_score_weights` setting on the
+[`@Language.component`](/api/language#component) or
+[`@Language.factory`](/api/language#factory). By default, all pipeline
+components are weighted equally.
+
+<Accordion title="Understanding the training output and score types" spaced>
+
+<!-- TODO: come up with good short explanation of precision and recall -->
+
+| Name                       | Description                                                                                                             |
+| -------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+| **Loss**                   | The training loss representing the amount of work left for the optimizer. Should decrease, but usually not to `0`.      |
+| **Precision** (P)          | Should increase.                                                                                                        |
+| **Recall** (R)             | Should increase.                                                                                                        |
+| **F-Score** (F)            | The weighted average of precision and recall. Should increase.                                                          |
+| **UAS** / **LAS**          | Unlabeled and labeled attachment score for the dependency parser, i.e. the percentage of correct arcs. Should increase. |
+| **Words per second** (WPS) | Prediction speed in words per second. Should stay stable.                                                               |
+
+<!-- TODO: is this still relevant? -->
+
+Note that if the development data has raw text, some of the gold-standard
+entities might not align to the predicted tokenization. These tokenization
+errors are **excluded from the NER evaluation**. If your tokenization makes it
+impossible for the model to predict 50% of your entities, your NER F-score might
+still look good.
+
+</Accordion>
 
 ## Transfer learning {#transfer-learning}
 

website/docs/usage/transformers.md

@@ -88,7 +88,8 @@ The recommended workflow for training is to use spaCy's
 [`spacy train`](/api/cli#train) command. The training config defines all
 component settings and hyperparameters in one place and lets you describe a tree
 of objects by referring to creation functions, including functions you register
-yourself.
+yourself. For details on how to get started with training your own model, check
+out the [training quickstart](/usage/training#quickstart).
 
 <Project id="en_core_bert">
 

website/src/components/copy.js

@@ -3,21 +3,23 @@ import React, { useState, useRef } from 'react'
 import Icon from './icon'
 import classes from '../styles/copy.module.sass'
 
+export function copyToClipboard(ref, callback) {
+    const isClient = typeof window !== 'undefined'
+    if (ref.current && isClient) {
+        ref.current.select()
+        document.execCommand('copy')
+        callback(true)
+        ref.current.blur()
+        setTimeout(() => callback(false), 1000)
+    }
+}
+
 const CopyInput = ({ text, prefix }) => {
     const isClient = typeof window !== 'undefined'
     const supportsCopy = isClient && document.queryCommandSupported('copy')
     const textareaRef = useRef()
     const [copySuccess, setCopySuccess] = useState(false)
-
+    const onClick = () => copyToClipboard(textareaRef, setCopySuccess)
-    function copyToClipboard() {
-        if (textareaRef.current && isClient) {
-            textareaRef.current.select()
-            document.execCommand('copy')
-            setCopySuccess(true)
-            textareaRef.current.blur()
-            setTimeout(() => setCopySuccess(false), 1000)
-        }
-    }
 
     function selectText() {
         if (textareaRef.current && isClient) {
@@ -37,7 +39,7 @@ const CopyInput = ({ text, prefix }) => {
                 onClick={selectText}
             />
             {supportsCopy && (
-                <button title="Copy to clipboard" onClick={copyToClipboard}>
+                <button title="Copy to clipboard" onClick={onClick}>
                     <Icon width={16} name={copySuccess ? 'accept' : 'clipboard'} />
                 </button>
             )}

website/src/components/icon.js

@@ -22,6 +22,7 @@ import { ReactComponent as SearchIcon } from '../images/icons/search.svg'
 import { ReactComponent as MoonIcon } from '../images/icons/moon.svg'
 import { ReactComponent as ClipboardIcon } from '../images/icons/clipboard.svg'
 import { ReactComponent as NetworkIcon } from '../images/icons/network.svg'
+import { ReactComponent as DownloadIcon } from '../images/icons/download.svg'
 
 import classes from '../styles/icon.module.sass'
 
@@ -46,7 +47,8 @@ const icons = {
     search: SearchIcon,
     moon: MoonIcon,
     clipboard: ClipboardIcon,
-    network: NetworkIcon
+    network: NetworkIcon,
+    download: DownloadIcon,
 }
 
 const Icon = ({ name, width, height, inline, variant, className }) => {

website/src/components/quickstart.js

@@ -1,4 +1,4 @@
-import React, { Fragment, useState, useEffect } from 'react'
+import React, { Fragment, useState, useEffect, useRef } from 'react'
 import PropTypes from 'prop-types'
 import classNames from 'classnames'
 import { window } from 'browser-monads'
@@ -6,6 +6,7 @@ import { window } from 'browser-monads'
 import Section from './section'
 import Icon from './icon'
 import { H2 } from './typography'
+import { copyToClipboard } from './copy'
 import classes from '../styles/quickstart.module.sass'
 
 function getNewChecked(optionId, checkedForId, multiple) {
@@ -14,10 +15,41 @@ function getNewChecked(optionId, checkedForId, multiple) {
     return [...checkedForId, optionId]
 }
 
-const Quickstart = ({ data, title, description, id, children }) => {
+function getRawContent(ref) {
+    if (ref.current && ref.current.childNodes) {
+        // Select all currently visible nodes (spans and text nodes)
+        const result = [...ref.current.childNodes].filter(el => el.offsetParent !== null)
+        return result.map(el => el.textContent).join('\n')
+    }
+    return ''
+}
+
+const Quickstart = ({
+    data,
+    title,
+    description,
+    copy,
+    download,
+    id,
+    setters = {},
+    hidePrompts,
+    children,
+}) => {
+    const contentRef = useRef()
+    const copyAreaRef = useRef()
+    const isClient = typeof window !== 'undefined'
+    const supportsCopy = isClient && document.queryCommandSupported('copy')
+    const showCopy = supportsCopy && copy
     const [styles, setStyles] = useState({})
     const [checked, setChecked] = useState({})
     const [initialized, setInitialized] = useState(false)
+    const [copySuccess, setCopySuccess] = useState(false)
+    const [otherState, setOtherState] = useState({})
+    const setOther = (id, value) => setOtherState({ ...otherState, [id]: value })
+    const onClickCopy = () => {
+        copyAreaRef.current.value = getRawContent(contentRef)
+        copyToClipboard(copyAreaRef, setCopySuccess)
+    }
 
     const getCss = (id, checkedOptions) => {
         const checkedForId = checkedOptions[id] || []
@@ -32,7 +64,7 @@ const Quickstart = ({ data, title, description, id, children }) => {
         if (!initialized) {
             const initialChecked = Object.assign(
                 {},
-                ...data.map(({ id, options }) => ({
+                ...data.map(({ id, options = [] }) => ({
                     [id]: options.filter(option => option.checked).map(({ id }) => id),
                 }))
             )
@@ -48,7 +80,7 @@ const Quickstart = ({ data, title, description, id, children }) => {
 
     return !data.length ? null : (
         <Section id={id}>
-            <div className={classes.root}>
+            <div className={classNames(classes.root, { [classes.hidePrompts]: !!hidePrompts })}>
                 {title && (
                     <H2 className={classes.title} name={id}>
                         <a href={`#${id}`}>{title}</a>
@@ -57,82 +89,154 @@ const Quickstart = ({ data, title, description, id, children }) => {
 
                 {description && <p className={classes.description}>{description}</p>}
 
-                {data.map(({ id, title, options = [], multiple, help }) => (
+                {data.map(
-                    <div key={id} data-quickstart-group={id} className={classes.group}>
+                    ({
-                        <style data-quickstart-style={id}>
+                        id,
-                            {styles[id] ||
+                        title,
-                                `[data-quickstart-results]>[data-quickstart-${id}] { display: none }`}
+                        options = [],
-                        </style>
+                        dropdown = [],
-                        <div className={classes.legend}>
+                        defaultValue,
-                            {title}
+                        multiple,
-                            {help && (
+                        other,
-                                <span data-tooltip={help} className={classes.help}>
+                        help,
-                                    {' '}
+                    }) => {
-                                    <Icon name="help" width={16} spaced />
+                        // Optional function that's called with the value
-                                </span>
+                        const setterFunc = setters[id] || (() => {})
-                            )}
+                        return (
-                        </div>
+                            <div key={id} data-quickstart-group={id} className={classes.group}>
-                        <div className={classes.fields}>
+                                <style data-quickstart-style={id} scoped>
-                            {options.map(option => {
+                                    {styles[id] ||
-                                const optionType = multiple ? 'checkbox' : 'radio'
+                                        `[data-quickstart-results]>[data-quickstart-${id}] { display: none }`}
-                                const checkedForId = checked[id] || []
+                                </style>
-                                return (
+                                <div className={classes.legend}>
-                                    <Fragment key={option.id}>
+                                    {title}
-                                        <input
+                                    {help && (
-                                            onChange={() => {
+                                        <span data-tooltip={help} className={classes.help}>
-                                                const newChecked = {
+                                            {' '}
-                                                    ...checked,
+                                            <Icon name="help" width={16} spaced />
-                                                    [id]: getNewChecked(
+                                        </span>
-                                                        option.id,
+                                    )}
-                                                        checkedForId,
+                                </div>
-                                                        multiple
+                                <div className={classes.fields}>
-                                                    ),
+                                    {!!dropdown.length && (
+                                        <select
+                                            defaultValue={defaultValue}
+                                            className={classes.select}
+                                            onChange={({ target }) => {
+                                                const value = target.value
+                                                if (value != other) {
+                                                    setterFunc(value)
+                                                    setOther(id, false)
+                                                } else {
+                                                    setterFunc('')
+                                                    setOther(id, true)
                                                 }
-                                                setChecked(newChecked)
-                                                setStyles({
-                                                    ...styles,
-                                                    [id]: getCss(id, newChecked),
-                                                })
                                             }}
-                                            type={optionType}
-                                            className={classNames(
-                                                classes.input,
-                                                classes[optionType]
-                                            )}
-                                            name={id}
-                                            id={`quickstart-${option.id}`}
-                                            value={option.id}
-                                            checked={checkedForId.includes(option.id)}
-                                        />
-                                        <label
-                                            className={classes.label}
-                                            htmlFor={`quickstart-${option.id}`}
                                         >
-                                            {option.title}
+                                            {dropdown.map(({ id, title }) => (
-                                            {option.meta && (
+                                                <option key={id} value={id}>
-                                                <span className={classes.meta}>{option.meta}</span>
+                                                    {title}
-                                            )}
+                                                </option>
-                                            {option.help && (
+                                            ))}
-                                                <span
+                                            {other && <option value={other}>{other}</option>}
-                                                    data-tooltip={option.help}
+                                        </select>
-                                                    className={classes.help}
+                                    )}
+                                    {other && otherState[id] && (
+                                        <input
+                                            type="text"
+                                            className={classes.textInput}
+                                            placeholder="Type here..."
+                                            onChange={({ target }) => setterFunc(target.value)}
+                                        />
+                                    )}
+                                    {options.map(option => {
+                                        const optionType = multiple ? 'checkbox' : 'radio'
+                                        const checkedForId = checked[id] || []
+                                        return (
+                                            <Fragment key={option.id}>
+                                                <input
+                                                    onChange={() => {
+                                                        const newChecked = {
+                                                            ...checked,
+                                                            [id]: getNewChecked(
+                                                                option.id,
+                                                                checkedForId,
+                                                                multiple
+                                                            ),
+                                                        }
+                                                        setChecked(newChecked)
+                                                        setStyles({
+                                                            ...styles,
+                                                            [id]: getCss(id, newChecked),
+                                                        })
+                                                        setterFunc(newChecked[id])
+                                                    }}
+                                                    type={optionType}
+                                                    className={classNames(
+                                                        classes.input,
+                                                        classes[optionType]
+                                                    )}
+                                                    name={id}
+                                                    id={`quickstart-${option.id}`}
+                                                    value={option.id}
+                                                    checked={checkedForId.includes(option.id)}
+                                                />
+                                                <label
+                                                    className={classes.label}
+                                                    htmlFor={`quickstart-${option.id}`}
                                                 >
-                                                    {' '}
+                                                    {option.title}
-                                                    <Icon name="help" width={16} spaced />
+                                                    {option.meta && (
-                                                </span>
+                                                        <span className={classes.meta}>
-                                            )}
+                                                            {option.meta}
-                                        </label>
+                                                        </span>
-                                    </Fragment>
+                                                    )}
-                                )
+                                                    {option.help && (
-                            })}
+                                                        <span
-                        </div>
+                                                            data-tooltip={option.help}
-                    </div>
+                                                            className={classes.help}
-                ))}
+                                                        >
+                                                            {' '}
+                                                            <Icon name="help" width={16} spaced />
+                                                        </span>
+                                                    )}
+                                                </label>
+                                            </Fragment>
+                                        )
+                                    })}
+                                </div>
+                            </div>
+                        )
+                    }
+                )}
                 <pre className={classes.code}>
-                    <code className={classes.results} data-quickstart-results="">
+                    <code className={classes.results} data-quickstart-results="" ref={contentRef}>
                         {children}
                     </code>
+
+                    <menu className={classes.menu}>
+                        {showCopy && (
+                            <button
+                                title="Copy to clipboard"
+                                onClick={onClickCopy}
+                                className={classes.iconButton}
+                            >
+                                <Icon width={18} name={copySuccess ? 'accept' : 'clipboard'} />
+                            </button>
+                        )}
+                        {download && (
+                            <a
+                                href={`data:application/octet-stream,${getRawContent(contentRef)}`}
+                                title="Download file"
+                                download={download}
+                                className={classes.iconButton}
+                            >
+                                <Icon width={18} name="download" />
+                            </a>
+                        )}
+                    </menu>
                 </pre>
+                {showCopy && <textarea ref={copyAreaRef} className={classes.copyArea} rows={1} />}
             </div>
         </Section>
     )
@@ -141,6 +245,7 @@ const Quickstart = ({ data, title, description, id, children }) => {
 Quickstart.defaultProps = {
     data: [],
     id: 'quickstart',
+    copy: true,
 }
 
 Quickstart.propTypes = {
@@ -164,12 +269,13 @@ Quickstart.propTypes = {
     ),
 }
 
-const QS = ({ children, prompt = 'bash', divider = false, ...props }) => {
+const QS = ({ children, prompt = 'bash', divider = false, comment = false, ...props }) => {
     const qsClassNames = classNames({
         [classes.prompt]: !!prompt && !divider,
         [classes.bash]: prompt === 'bash' && !divider,
         [classes.python]: prompt === 'python' && !divider,
         [classes.divider]: !!divider,
+        [classes.comment]: !!comment,
     })
     const attrs = Object.assign(
         {},

website/src/images/icons/download.svg

@@ -0,0 +1,4 @@
+<svg  xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24">
+<path d="M16.707 7.404c-0.189-0.188-0.448-0.283-0.707-0.283s-0.518 0.095-0.707 0.283l-2.293 2.293v-6.697c0-0.552-0.448-1-1-1s-1 0.448-1 1v6.697l-2.293-2.293c-0.189-0.188-0.44-0.293-0.707-0.293s-0.518 0.105-0.707 0.293c-0.39 0.39-0.39 1.024 0 1.414l4.707 4.682 4.709-4.684c0.388-0.387 0.388-1.022-0.002-1.412z"></path>
+<path d="M20.987 16c0-0.105-0.004-0.211-0.039-0.316l-2-6c-0.136-0.409-0.517-0.684-0.948-0.684h-0.219c-0.094 0.188-0.21 0.368-0.367 0.525l-1.482 1.475h1.348l1.667 5h-13.893l1.667-5h1.348l-1.483-1.475c-0.157-0.157-0.274-0.337-0.367-0.525h-0.219c-0.431 0-0.812 0.275-0.948 0.684l-2 6c-0.035 0.105-0.039 0.211-0.039 0.316-0.013 0-0.013 5-0.013 5 0 0.553 0.447 1 1 1h16c0.553 0 1-0.447 1-1 0 0 0-5-0.013-5z"></path>
+</svg>

website/src/styles/code.module.sass

@@ -24,7 +24,7 @@
 .code,
 .juniper-input pre
     display: block
-    padding: 1.75em 2em
+    padding: 1.75em 1.5em
 
 .code
     &[data-prompt]:before,

website/src/styles/layout.sass

@@ -370,9 +370,9 @@ body [id]:target
     background-color: var(--color-dark-secondary)
     border-left: 0.35em solid var(--color-theme)
     display: block
-    margin-right: -2em
+    margin-right: -1.5em
-    margin-left: -2em
+    margin-left: -1.5em
-    padding-right: 2em
+    padding-right: 1.5em
     padding-left: 1.65em
 
     &:empty:before

website/src/styles/quickstart.module.sass

@@ -83,6 +83,24 @@
 .fields
     flex: 100%
 
+.select
+    cursor: pointer
+    border: 1px solid var(--color-subtle)
+    border-radius: var(--border-radius)
+    display: inline-block
+    padding: 0.35rem 1.25rem
+    margin: 0 1rem 0.75rem 0
+    font-size: var(--font-size-sm)
+    background: var(--color-back)
+
+.text-input
+    border: 1px solid var(--color-subtle)
+    border-radius: var(--border-radius)
+    display: inline-block
+    padding: 0.35rem 0.75rem
+    font-size: var(--font-size-sm)
+    background: var(--color-back)
+
 .code
     background: var(--color-front)
     color: var(--color-back)
@@ -95,6 +113,7 @@
     border-bottom-right-radius: var(--border-radius)
     -webkit-font-smoothing: subpixel-antialiased
     -moz-osx-font-smoothing: auto
+    position: relative
 
 .results
     display: block
@@ -105,6 +124,9 @@
     & > span
         display: block
 
+.hide-prompts .prompt:before
+    content: initial !important
+
 .prompt:before
     color: var(--color-theme)
     margin-right: 1em
@@ -115,6 +137,9 @@
 .python:before
     content: ">>>"
 
+.comment
+    color: var(--syntax-comment)
+
 .divider
     padding: 1.5rem 0
 
@@ -123,3 +148,29 @@
 
     .input:checked + .label &
         color: inherit
+
+.copy-area
+    width: 1px
+    height: 1px
+    opacity: 0
+    position: absolute
+
+.menu
+    color: var(--color-subtle)
+    padding-right: 1.5rem
+    display: inline-block
+    position: absolute
+    bottom: var(--spacing-xs)
+    right: 0
+
+.icon-button
+    display: inline-block
+    color: inherit
+    cursor: pointer
+    transition: transform 0.05s ease
+
+    &:not(:last-child)
+        margin-right: 1.5rem
+
+    &:hover
+        transform: scale(1.1)

website/src/widgets/quickstart-install.js

@@ -92,7 +92,7 @@ const QuickstartInstall = ({ id, title }) => (
                     </QS>
                     <QS package="source">pip install -r requirements.txt</QS>
                     <QS addition="transformers" package="pip">
-                        pip install -U spacy-lookups-transformers
+                        pip install -U spacy-transformers
                     </QS>
                     <QS addition="transformers" package="source">
                         pip install -U spacy-transformers

website/src/widgets/quickstart-training.js

@@ -0,0 +1,118 @@
+import React, { useState } from 'react'
+import { StaticQuery, graphql } from 'gatsby'
+
+import { Quickstart, QS } from '../components/quickstart'
+
+const DEFAULT_LANG = 'en'
+const MODELS_SMALL = { en: 'roberta-base-small' }
+const MODELS_LARGE = { en: 'roberta-base' }
+
+const COMPONENTS = ['tagger', 'parser', 'ner', 'textcat']
+const COMMENT = `# This is an auto-generated partial config for training a model.
+# TODO: intructions for how to fill and use it`
+const DATA = [
+    {
+        id: 'lang',
+        title: 'Language',
+        defaultValue: DEFAULT_LANG,
+    },
+    {
+        id: 'components',
+        title: 'Components',
+        help: 'Pipeline components to train. Requires training data for those annotations.',
+        options: COMPONENTS.map(id => ({ id, title: id })),
+        multiple: true,
+    },
+    {
+        id: 'hardware',
+        title: 'Hardware',
+        options: [
+            { id: 'cpu-only', title: 'CPU only' },
+            { id: 'cpu', title: 'CPU preferred' },
+            { id: 'gpu', title: 'GPU', checked: true },
+        ],
+    },
+    {
+        id: 'optimize',
+        title: 'Optimize for',
+        help: '...',
+        options: [
+            { id: 'efficiency', title: 'efficiency', checked: true },
+            { id: 'accuracy', title: 'accuracy' },
+        ],
+    },
+    {
+        id: 'config',
+        title: 'Configuration',
+        options: [
+            {
+                id: 'independent',
+                title: 'independent components',
+                help: "Make components independent and don't share weights",
+            },
+        ],
+        multiple: true,
+    },
+]
+
+const QuickstartTraining = ({ id, title, download = 'config.cfg' }) => {
+    const [lang, setLang] = useState(DEFAULT_LANG)
+    const [pipeline, setPipeline] = useState([])
+    const setters = { lang: setLang, components: setPipeline }
+    return (
+        <StaticQuery
+            query={query}
+            render={({ site }) => {
+                const langs = site.siteMetadata.languages
+                DATA[0].dropdown = langs.map(({ name, code }) => ({
+                    id: code,
+                    title: name,
+                }))
+                return (
+                    <Quickstart
+                        download={download}
+                        data={DATA}
+                        title={title}
+                        id={id}
+                        setters={setters}
+                        hidePrompts
+                    >
+                        <QS comment>{COMMENT}</QS>
+                        <span>[nlp]</span>
+                        <span>lang = "{lang}"</span>
+                        <span>pipeline = {JSON.stringify(pipeline).replace(/,/g, ', ')}</span>
+                        <br />
+                        <span>[components]</span>
+                        <br />
+                        <span>[components.transformer]</span>
+                        <QS optimize="efficiency">name = "{MODELS_SMALL[lang]}"</QS>
+                        <QS optimize="accuracy">name = "{MODELS_LARGE[lang]}"</QS>
+                        {!!pipeline.length && <br />}
+                        {pipeline.map((pipe, i) => (
+                            <>
+                                {i !== 0 && <br />}
+                                <span>[components.{pipe}]</span>
+                                <span>factory = "{pipe}"</span>
+                            </>
+                        ))}
+                    </Quickstart>
+                )
+            }}
+        />
+    )
+}
+
+const query = graphql`
+    query QuickstartTrainingQuery {
+        site {
+            siteMetadata {
+                languages {
+                    code
+                    name
+                }
+            }
+        }
+    }
+`
+
+export default QuickstartTraining