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106
.github/contributors/hertelm.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI GmbH](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
non-exclusive, worldwide, no-charge, royalty-free license to:
* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
included in any product, work or materials arising out of the project to
which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Matthias Hertel |
| Company name (if applicable) | |
| Title or role (if applicable) | |
| Date | June 29, 2020 |
| GitHub username | hertelm |
| Website (optional) | |

2
netlify.toml
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@ -1,6 +1,8 @@
redirects = [
# Netlify
{from = "https://spacy.netlify.com/*", to="https://spacy.io/:splat", force = true },
# Subdomain for branches
{from = "https://nightly.spacy.io/*", to="https://spacy-io-develop.spacy.io/:splat", force = true, status = 200},
# Old subdomains
{from = "https://survey.spacy.io/*", to = "https://spacy.io", force = true},
{from = "http://survey.spacy.io/*", to = "https://spacy.io", force = true},

4
setup.py
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@ -81,7 +81,7 @@ def is_new_osx():
return False
mac_ver = platform.mac_ver()[0]
if mac_ver.startswith("10"):
minor_version = int(mac_ver.split('.')[1])
minor_version = int(mac_ver.split(".")[1])
if minor_version >= 7:
return True
else:
@ -158,7 +158,7 @@ def setup_package():
ext_modules = cythonize(ext_modules, compiler_directives=COMPILER_DIRECTIVES)
setup(
name="spacy",
name="spacy-nightly",
packages=PACKAGES,
version=about["__version__"],
ext_modules=ext_modules,

4
spacy/about.py
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@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy"
__version__ = "3.0.0.dev12"
__title__ = "spacy-nightly"
__version__ = "3.0.0a0"
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

14
spacy/cli/project.py
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@ -242,12 +242,16 @@ def project_clone(
try:
run_command(cmd)
except SystemExit:
err = f"Could not clone the repo '{repo}' into the temp dir '{tmp_dir}'"
err = f"Could not clone the repo '{repo}' into the temp dir '{tmp_dir}'."
msg.fail(err)
with (tmp_dir / ".git" / "info" / "sparse-checkout").open("w") as f:
f.write(name)
run_command(["git", "-C", str(tmp_dir), "fetch"])
run_command(["git", "-C", str(tmp_dir), "checkout"])
try:
run_command(["git", "-C", str(tmp_dir), "fetch"])
run_command(["git", "-C", str(tmp_dir), "checkout"])
except SystemExit:
err = f"Could not clone '{name}' in the repo '{repo}'."
msg.fail(err)
shutil.move(str(tmp_dir / Path(name).name), str(project_dir))
msg.good(f"Cloned project '{name}' from {repo} into {project_dir}")
for sub_dir in DIRS:
@ -525,9 +529,9 @@ def update_dvc_config(
outputs_no_cache = command.get("outputs_no_cache", [])
if not deps and not outputs and not outputs_no_cache:
continue
# Default to "." as the project path since dvc.yaml is auto-generated
# Default to the working dir as the project path since dvc.yaml is auto-generated
# and we don't want arbitrary paths in there
project_cmd = ["python", "-m", NAME, "project", ".", "exec", name]
project_cmd = ["python", "-m", NAME, "project", "exec", name]
deps_cmd = [c for cl in [["-d", p] for p in deps] for c in cl]
outputs_cmd = [c for cl in [["-o", p] for p in outputs] for c in cl]
outputs_nc_cmd = [c for cl in [["-O", p] for p in outputs_no_cache] for c in cl]

8
spacy/cli/train.py
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@ -339,6 +339,7 @@ def create_train_batches(nlp, corpus, cfg, randomization_index):
yield epoch, batch
if max_epochs >= 1 and epoch >= max_epochs:
break
random.shuffle(train_examples)
def create_evaluation_callback(nlp, optimizer, corpus, cfg):
@ -350,13 +351,14 @@ def create_evaluation_callback(nlp, optimizer, corpus, cfg):
)
n_words = sum(len(ex.predicted) for ex in dev_examples)
batch_size = cfg.get("evaluation_batch_size", 128)
start_time = timer()
if optimizer.averages:
with nlp.use_params(optimizer.averages):
scorer = nlp.evaluate(dev_examples, batch_size=32)
scorer = nlp.evaluate(dev_examples, batch_size=batch_size)
else:
scorer = nlp.evaluate(dev_examples, batch_size=32)
scorer = nlp.evaluate(dev_examples, batch_size=batch_size)
end_time = timer()
wps = n_words / (end_time - start_time)
scores = scorer.scores
@ -479,7 +481,7 @@ def train_while_improving(
if patience and (step - best_step) >= patience:
break
# Stop if we've exhausted our max steps (if specified)
if max_steps and (step * accumulate_gradient) >= max_steps:
if max_steps and step >= max_steps:
break

24
spacy/gold/corpus.py
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@ -45,18 +45,22 @@ class Corpus:
def make_examples(self, nlp, reference_docs, max_length=0):
for reference in reference_docs:
if len(reference) >= max_length >= 1:
if reference.is_sentenced:
for ref_sent in reference.sents:
yield Example(
nlp.make_doc(ref_sent.text),
ref_sent.as_doc()
)
else:
if len(reference) == 0:
continue
elif max_length == 0 or len(reference) < max_length:
yield Example(
nlp.make_doc(reference.text),
reference
)
elif reference.is_sentenced:
for ref_sent in reference.sents:
if len(ref_sent) == 0:
continue
elif max_length == 0 or len(ref_sent) < max_length:
yield Example(
nlp.make_doc(ref_sent.text),
ref_sent.as_doc()
)
def make_examples_gold_preproc(self, nlp, reference_docs):
for reference in reference_docs:
@ -65,7 +69,7 @@ class Corpus:
else:
ref_sents = [reference]
for ref_sent in ref_sents:
yield Example(
eg = Example(
Doc(
nlp.vocab,
words=[w.text for w in ref_sent],
@ -73,6 +77,8 @@ class Corpus:
),
ref_sent
)
if len(eg.x):
yield eg
def read_docbin(self, vocab, locs):
""" Yield training examples as example dicts """

1
spacy/ml/_precomputable_affine.py
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@ -110,6 +110,7 @@ def init(model, X=None, Y=None):
ops = model.ops
W = normal_init(ops, W.shape, mean=float(ops.xp.sqrt(1.0 / nF * nI)))
pad = normal_init(ops, pad.shape, mean=1.0)
model.set_param("W", W)
model.set_param("b", b)
model.set_param("pad", pad)

66
spacy/syntax/_parser_model.pyx
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@ -339,6 +339,7 @@ cdef class precompute_hiddens:
cdef readonly int nF, nO, nP
cdef bint _is_synchronized
cdef public object ops
cdef public object numpy_ops
cdef np.ndarray _features
cdef np.ndarray _cached
cdef np.ndarray bias
@ -368,6 +369,7 @@ cdef class precompute_hiddens:
self.nP = 1
self.nO = cached.shape[2]
self.ops = lower_model.ops
self.numpy_ops = NumpyOps()
assert activation in (None, "relu", "maxout")
self.activation = activation
self._is_synchronized = False
@ -446,44 +448,32 @@ cdef class precompute_hiddens:
return state_vector, backward
def _nonlinearity(self, state_vector):
if isinstance(state_vector, numpy.ndarray):
ops = NumpyOps()
else:
ops = CupyOps()
if self.activation == "maxout":
state_vector, mask = ops.maxout(state_vector)
return self._maxout_nonlinearity(state_vector)
else:
state_vector = state_vector.reshape(state_vector.shape[:-1])
if self.activation == "relu":
mask = state_vector >= 0.
state_vector *= mask
else:
mask = None
return self._relu_nonlinearity(state_vector)
def backprop_nonlinearity(d_best):
if isinstance(d_best, numpy.ndarray):
ops = NumpyOps()
else:
ops = CupyOps()
if mask is not None:
mask_ = ops.asarray(mask)
# This will usually be on GPU
d_best = ops.asarray(d_best)
# Fix nans (which can occur from unseen classes.)
try:
d_best[ops.xp.isnan(d_best)] = 0.
except:
print(ops.xp.isnan(d_best))
raise
if self.activation == "maxout":
mask_ = ops.asarray(mask)
return ops.backprop_maxout(d_best, mask_, self.nP)
elif self.activation == "relu":
mask_ = ops.asarray(mask)
d_best *= mask_
d_best = d_best.reshape((d_best.shape + (1,)))
return d_best
else:
return d_best.reshape((d_best.shape + (1,)))
return state_vector, backprop_nonlinearity
def _maxout_nonlinearity(self, state_vector):
state_vector, mask = self.numpy_ops.maxout(state_vector)
# We're outputting to CPU, but we need this variable on GPU for the
# backward pass.
mask = self.ops.asarray(mask)
def backprop_maxout(d_best):
return self.ops.backprop_maxout(d_best, mask, self.nP)
return state_vector, backprop_maxout
def _relu_nonlinearity(self, state_vector):
state_vector = state_vector.reshape((state_vector.shape[0], -1))
mask = state_vector >= 0.
state_vector *= mask
# We're outputting to CPU, but we need this variable on GPU for the
# backward pass.
mask = self.ops.asarray(mask)
def backprop_relu(d_best):
d_best *= mask
return d_best.reshape((d_best.shape + (1,)))
return state_vector, backprop_relu

26
spacy/syntax/arc_eager.pyx
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@ -742,21 +742,14 @@ cdef class ArcEager(TransitionSystem):
if n_gold < 1:
raise ValueError
def get_oracle_sequence(self, Example example):
cdef StateClass state
cdef ArcEagerGold gold
states, golds, n_steps = self.init_gold_batch([example])
if not golds:
return []
def get_oracle_sequence_from_state(self, StateClass state, ArcEagerGold gold, _debug=None):
cdef int i
cdef Pool mem = Pool()
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
assert self.n_moves > 0
costs = <float*>mem.alloc(self.n_moves, sizeof(float))
is_valid = <int*>mem.alloc(self.n_moves, sizeof(int))
state = states[0]
gold = golds[0]
history = []
debug_log = []
failed = False
@ -772,18 +765,21 @@ cdef class ArcEager(TransitionSystem):
history.append(i)
s0 = state.S(0)
b0 = state.B(0)
debug_log.append(" ".join((
self.get_class_name(i),
"S0=", (example.x[s0].text if s0 >= 0 else "__"),
"B0=", (example.x[b0].text if b0 >= 0 else "__"),
"S0 head?", str(state.has_head(state.S(0))),
)))
if _debug:
example = _debug
debug_log.append(" ".join((
self.get_class_name(i),
"S0=", (example.x[s0].text if s0 >= 0 else "__"),
"B0=", (example.x[b0].text if b0 >= 0 else "__"),
"S0 head?", str(state.has_head(state.S(0))),
)))
action.do(state.c, action.label)
break
else:
failed = False
break
if failed:
example = _debug
print("Actions")
for i in range(self.n_moves):
print(self.get_class_name(i))

86
spacy/syntax/nn_parser.pyx
View File

@ -63,7 +63,9 @@ cdef class Parser:
self.model = model
if self.moves.n_moves != 0:
self.set_output(self.moves.n_moves)
self.cfg = cfg
self.cfg = dict(cfg)
self.cfg.setdefault("update_with_oracle_cut_size", 100)
self.cfg.setdefault("normalize_gradients_with_batch_size", True)
self._multitasks = []
for multitask in cfg.get("multitasks", []):
self.add_multitask_objective(multitask)
@ -263,22 +265,32 @@ cdef class Parser:
free(is_valid)
def update(self, examples, drop=0., set_annotations=False, sgd=None, losses=None):
cdef StateClass state
if losses is None:
losses = {}
losses.setdefault(self.name, 0.)
for multitask in self._multitasks:
multitask.update(examples, drop=drop, sgd=sgd)
n_examples = len([eg for eg in examples if self.moves.has_gold(eg)])
if n_examples == 0:
return losses
set_dropout_rate(self.model, drop)
# Prepare the stepwise model, and get the callback for finishing the batch
model, backprop_tok2vec = self.model.begin_update(
[eg.predicted for eg in examples])
# Chop sequences into lengths of this many transitions, to make the
# batch uniform length. We randomize this to overfit less.
cut_gold = numpy.random.choice(range(20, 100))
states, golds, max_steps = self._init_gold_batch(
examples,
max_length=cut_gold
)
if self.cfg["update_with_oracle_cut_size"] >= 1:
# Chop sequences into lengths of this many transitions, to make the
# batch uniform length. We randomize this to overfit less.
cut_size = self.cfg["update_with_oracle_cut_size"]
states, golds, max_steps = self._init_gold_batch(
examples,
max_length=numpy.random.choice(range(5, cut_size))
)
else:
states, golds, _ = self.moves.init_gold_batch(examples)
max_steps = max([len(eg.x) for eg in examples])
if not states:
return losses
all_states = list(states)
states_golds = zip(states, golds)
for _ in range(max_steps):
@ -287,6 +299,17 @@ cdef class Parser:
states, golds = zip(*states_golds)
scores, backprop = model.begin_update(states)
d_scores = self.get_batch_loss(states, golds, scores, losses)
if self.cfg["normalize_gradients_with_batch_size"]:
# We have to be very careful how we do this, because of the way we
# cut up the batch. We subdivide long sequences. If we normalize
# naively, we end up normalizing by sequence length, which
# is bad: that would mean that states in long sequences
# consistently get smaller gradients. Imagine if we have two
# sequences, one length 1000, one length 20. If we cut up
# the 1k sequence so that we have a "batch" of 50 subsequences,
# we don't want the gradients to get 50 times smaller!
d_scores /= n_examples
backprop(d_scores)
# Follow the predicted action
self.transition_states(states, scores)
@ -384,8 +407,6 @@ cdef class Parser:
cpu_log_loss(c_d_scores,
costs, is_valid, &scores[i, 0], d_scores.shape[1])
c_d_scores += d_scores.shape[1]
if len(states):
d_scores /= len(states)
if losses is not None:
losses.setdefault(self.name, 0.)
losses[self.name] += (d_scores**2).sum()
@ -428,7 +449,7 @@ cdef class Parser:
if component is self:
break
if hasattr(component, "pipe"):
doc_sample = list(component.pipe(doc_sample))
doc_sample = list(component.pipe(doc_sample, batch_size=8))
else:
doc_sample = [component(doc) for doc in doc_sample]
if doc_sample:
@ -498,40 +519,49 @@ cdef class Parser:
return self
def _init_gold_batch(self, examples, min_length=5, max_length=500):
"""Make a square batch, of length equal to the shortest doc. A long
"""Make a square batch, of length equal to the shortest transition
sequence or a cap. A long
doc will get multiple states. Let's say we have a doc of length 2*N,
where N is the shortest doc. We'll make two states, one representing
long_doc[:N], and another representing long_doc[N:]."""
cdef:
StateClass start_state
StateClass state
Transition action
all_states = self.moves.init_batch([eg.predicted for eg in examples])
kept = []
max_length_seen = 0
for state, eg in zip(all_states, examples):
if self.moves.has_gold(eg) and not state.is_final():
gold = self.moves.init_gold(state, eg)
kept.append((eg, state, gold))
max_length = max(min_length, min(max_length, min([len(eg.x) for eg in examples])))
max_moves = 0
oracle_actions = self.moves.get_oracle_sequence_from_state(
state.copy(), gold)
kept.append((eg, state, gold, oracle_actions))
min_length = min(min_length, len(oracle_actions))
max_length_seen = max(max_length, len(oracle_actions))
if not kept:
return [], [], 0
max_length = max(min_length, min(max_length, max_length_seen))
states = []
golds = []
for eg, state, gold in kept:
oracle_actions = self.moves.get_oracle_sequence(eg)
start = 0
while start < len(eg.predicted):
state = state.copy()
cdef int clas
max_moves = 0
for eg, state, gold, oracle_actions in kept:
for i in range(0, len(oracle_actions), max_length):
start_state = state.copy()
n_moves = 0
while state.B(0) < start and not state.is_final():
action = self.moves.c[oracle_actions.pop(0)]
for clas in oracle_actions[i:i+max_length]:
action = self.moves.c[clas]
action.do(state.c, action.label)
state.c.push_hist(action.clas)
n_moves += 1
has_gold = self.moves.has_gold(eg, start=start,
end=start+max_length)
if not state.is_final() and has_gold:
states.append(state)
if state.is_final():
break
max_moves = max(max_moves, n_moves)
if self.moves.has_gold(eg, start_state.B(0), state.B(0)):
states.append(start_state)
golds.append(gold)
max_moves = max(max_moves, n_moves)
start += min(max_length, len(eg.x)-start)
max_moves = max(max_moves, len(oracle_actions))
if state.is_final():
break
return states, golds, max_moves

25
spacy/syntax/transition_system.pyx
View File

@ -60,20 +60,25 @@ cdef class TransitionSystem:
states.append(state)
offset += len(doc)
return states
def get_oracle_sequence(self, Example example, _debug=False):
states, golds, _ = self.init_gold_batch([example])
if not states:
return []
state = states[0]
gold = golds[0]
if _debug:
return self.get_oracle_sequence_from_state(state, gold, _debug=example)
else:
return self.get_oracle_sequence_from_state(state, gold)
def get_oracle_sequence_from_state(self, StateClass state, gold, _debug=None):
cdef Pool mem = Pool()
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
assert self.n_moves > 0
costs = <float*>mem.alloc(self.n_moves, sizeof(float))
is_valid = <int*>mem.alloc(self.n_moves, sizeof(int))
cdef StateClass state
states, golds, n_steps = self.init_gold_batch([example])
if not states:
return []
state = states[0]
gold = golds[0]
history = []
debug_log = []
while not state.is_final():
@ -82,9 +87,10 @@ cdef class TransitionSystem:
if is_valid[i] and costs[i] <= 0:
action = self.c[i]
history.append(i)
s0 = state.S(0)
b0 = state.B(0)
if _debug:
s0 = state.S(0)
b0 = state.B(0)
example = _debug
debug_log.append(" ".join((
self.get_class_name(i),
"S0=", (example.x[s0].text if s0 >= 0 else "__"),
@ -95,6 +101,7 @@ cdef class TransitionSystem:
break
else:
if _debug:
example = _debug
print("Actions")
for i in range(self.n_moves):
print(self.get_class_name(i))

2
website/docs/api/phrasematcher.md
View File

@ -91,7 +91,7 @@ Match a stream of documents, yielding them in turn.
> ```python
> from spacy.matcher import PhraseMatcher
> matcher = PhraseMatcher(nlp.vocab)
> for doc in matcher.pipe(texts, batch_size=50):
> for doc in matcher.pipe(docs, batch_size=50):
> pass
> ```

30
website/docs/api/scorer.md
View File

@ -46,19 +46,19 @@ Update the evaluation scores from a single [`Doc`](/api/doc) /
## Properties
| Name | Type | Description |
| --------------------------------------------------- | ----- | ---------------------------------------------------------------------------------------------------------- |
| `token_acc` | float | Tokenization accuracy. |
| `tags_acc` | float | Part-of-speech tag accuracy (fine grained tags, i.e. `Token.tag`). |
| `uas` | float | Unlabelled dependency score. |
| `las` | float | Labelled dependency score. |
| `ents_p` | float | Named entity accuracy (precision). |
| `ents_r` | float | Named entity accuracy (recall). |
| `ents_f` | float | Named entity accuracy (F-score). |
| `ents_per_type` <Tag variant="new">2.1.5</Tag> | dict | Scores per entity label. Keyed by label, mapped to a dict of `p`, `r` and `f` scores. |
| Name | Type | Description |
| --------------------------------------------------- | ----- | -------------------------------------------------------------------------------------- |
| `token_acc` | float | Tokenization accuracy. |
| `tags_acc` | float | Part-of-speech tag accuracy (fine grained tags, i.e. `Token.tag`). |
| `uas` | float | Unlabelled dependency score. |
| `las` | float | Labelled dependency score. |
| `ents_p` | float | Named entity accuracy (precision). |
| `ents_r` | float | Named entity accuracy (recall). |
| `ents_f` | float | Named entity accuracy (F-score). |
| `ents_per_type` <Tag variant="new">2.1.5</Tag> | dict | Scores per entity label. Keyed by label, mapped to a dict of `p`, `r` and `f` scores. |
| `textcat_f` <Tag variant="new">3.0</Tag> | float | F-score on positive label for binary classification, macro-averaged F-score otherwise. |
| `textcat_auc` <Tag variant="new"3.0</Tag> | float | Macro-averaged AUC ROC score for multilabel classification (`-1` if undefined). |
| `textcats_f_per_cat` <Tag variant="new">3.0</Tag> | dict | F-scores per textcat label, keyed by label. |
| `textcats_auc_per_cat` <Tag variant="new">3.0</Tag> | dict | ROC AUC scores per textcat label, keyed by label. |
| `las_per_type` <Tag variant="new">2.2.3</Tag> | dict | Labelled dependency scores, keyed by label. |
| `scores` | dict | All scores, keyed by type. |
| `textcat_auc` <Tag variant="new">3.0</Tag> | float | Macro-averaged AUC ROC score for multilabel classification (`-1` if undefined). |
| `textcats_f_per_cat` <Tag variant="new">3.0</Tag> | dict | F-scores per textcat label, keyed by label. |
| `textcats_auc_per_cat` <Tag variant="new">3.0</Tag> | dict | ROC AUC scores per textcat label, keyed by label. |
| `las_per_type` <Tag variant="new">2.2.3</Tag> | dict | Labelled dependency scores, keyed by label. |
| `scores` | dict | All scores, keyed by type. |

2
website/docs/usage/rule-based-matching.md
View File

@ -122,7 +122,7 @@ for match_id, start, end in matches:
```
The matcher returns a list of `(match_id, start, end)` tuples – in this case,
`[('15578876784678163569', 0, 2)]`, which maps to the span `doc[0:2]` of our
`[('15578876784678163569', 0, 3)]`, which maps to the span `doc[0:3]` of our
original document. The `match_id` is the [hash value](/usage/spacy-101#vocab) of
the string ID "HelloWorld". To get the string value, you can look up the ID in
the [`StringStore`](/api/stringstore).

129
website/docs/usage/v2-3.md
View File

@ -161,10 +161,18 @@ debugging your tokenizer configuration.
spaCy's custom warnings have been replaced with native Python
[`warnings`](https://docs.python.org/3/library/warnings.html). Instead of
setting `SPACY_WARNING_IGNORE`, use the
[`warnings` filters](https://docs.python.org/3/library/warnings.html#the-warnings-filter)
setting `SPACY_WARNING_IGNORE`, use the [`warnings`
filters](https://docs.python.org/3/library/warnings.html#the-warnings-filter)
to manage warnings.
```diff
import spacy
+ import warnings
- spacy.errors.SPACY_WARNING_IGNORE.append('W007')
+ warnings.filterwarnings("ignore", message=r"\\[W007\\]", category=UserWarning)
```
#### Normalization tables
The normalization tables have moved from the language data in
@ -174,6 +182,65 @@ If you're adding data for a new language, the normalization table should be
added to `spacy-lookups-data`. See
[adding norm exceptions](/usage/adding-languages#norm-exceptions).
#### No preloaded vocab for models with vectors
To reduce the initial loading time, the lexemes in `nlp.vocab` are no longer
loaded on initialization for models with vectors. As you process texts, the
lexemes will be added to the vocab automatically, just as in small models
without vectors.
To see the number of unique vectors and number of words with vectors, see
`nlp.meta['vectors']`, for example for `en_core_web_md` there are `20000`
unique vectors and `684830` words with vectors:
```python
{
'width': 300,
'vectors': 20000,
'keys': 684830,
'name': 'en_core_web_md.vectors'
}
```
If required, for instance if you are working directly with word vectors rather
than processing texts, you can load all lexemes for words with vectors at once:
```python
for orth in nlp.vocab.vectors:
_ = nlp.vocab[orth]
```
If your workflow previously iterated over `nlp.vocab`, a similar alternative
is to iterate over words with vectors instead:
```diff
- lexemes = [w for w in nlp.vocab]
+ lexemes = [nlp.vocab[orth] for orth in nlp.vocab.vectors]
```
Be aware that the set of preloaded lexemes in a v2.2 model is not equivalent to
the set of words with vectors. For English, v2.2 `md/lg` models have 1.3M
provided lexemes but only 685K words with vectors. The vectors have been
updated for most languages in v2.2, but the English models contain the same
vectors for both v2.2 and v2.3.
#### Lexeme.is_oov and Token.is_oov
<Infobox title="Important note" variant="warning">
Due to a bug, the values for `is_oov` are reversed in v2.3.0, but this will be
fixed in the next patch release v2.3.1.
</Infobox>
In v2.3, `Lexeme.is_oov` and `Token.is_oov` are `True` if the lexeme does not
have a word vector. This is equivalent to `token.orth not in
nlp.vocab.vectors`.
Previously in v2.2, `is_oov` corresponded to whether a lexeme had stored
probability and cluster features. The probability and cluster features are no
longer included in the provided medium and large models (see the next section).
#### Probability and cluster features
> #### Load and save extra prob lookups table
@ -201,6 +268,28 @@ model vocab, which will take a few seconds on initial loading. When you save
this model after loading the `prob` table, the full `prob` table will be saved
as part of the model vocab.
To load the probability table into a provided model, first make sure you have
`spacy-lookups-data` installed. To load the table, remove the empty provided
`lexeme_prob` table and then access `Lexeme.prob` for any word to load the
table from `spacy-lookups-data`:
```diff
+ # prerequisite: pip install spacy-lookups-data
import spacy
nlp = spacy.load("en_core_web_md")
# remove the empty placeholder prob table
+ if nlp.vocab.lookups_extra.has_table("lexeme_prob"):
+ nlp.vocab.lookups_extra.remove_table("lexeme_prob")
# access any `.prob` to load the full table into the model
assert nlp.vocab["a"].prob == -3.9297883511
# if desired, save this model with the probability table included
nlp.to_disk("/path/to/model")
```
If you'd like to include custom `cluster`, `prob`, or `sentiment` tables as part
of a new model, add the data to
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) under
@ -218,3 +307,39 @@ When you initialize a new model with [`spacy init-model`](/api/cli#init-model),
the `prob` table from `spacy-lookups-data` may be loaded as part of the
initialization. If you'd like to omit this extra data as in spaCy's provided
v2.3 models, use the new flag `--omit-extra-lookups`.
#### Tag maps in provided models vs. blank models
The tag maps in the provided models may differ from the tag maps in the spaCy
library. You can access the tag map in a loaded model under
`nlp.vocab.morphology.tag_map`.
The tag map from `spacy.lang.lg.tag_map` is still used when a blank model is
initialized. If you want to provide an alternate tag map, update
`nlp.vocab.morphology.tag_map` after initializing the model or if you're using
the [train CLI](/api/cli#train), you can use the new `--tag-map-path` option to
provide in the tag map as a JSON dict.
If you want to export a tag map from a provided model for use with the train
CLI, you can save it as a JSON dict. To only use string keys as required by
JSON and to make it easier to read and edit, any internal integer IDs need to
be converted back to strings:
```python
import spacy
import srsly
nlp = spacy.load("en_core_web_sm")
tag_map = {}
# convert any integer IDs to strings for JSON
for tag, morph in nlp.vocab.morphology.tag_map.items():
tag_map[tag] = {}
for feat, val in morph.items():
feat = nlp.vocab.strings.as_string(feat)
if not isinstance(val, bool):
val = nlp.vocab.strings.as_string(val)
tag_map[tag][feat] = val
srsly.write_json("tag_map.json", tag_map)
```

17
website/docs/usage/v3.md Normal file
View File

@ -0,0 +1,17 @@
---
title: What's New in v3.0
teaser: New features, backwards incompatibilities and migration guide
menu:
- ['Summary', 'summary']
- ['New Features', 'features']
- ['Backwards Incompatibilities', 'incompat']
- ['Migrating from v2.x', 'migrating']
---
## Summary {#summary}
## New Features {#features}
## Backwards Incompatibilities {#incompat}
## Migrating from v2.x {#migrating}

27
website/gatsby-config.js
View File

@ -15,6 +15,11 @@ const universe = require('./meta/universe.json')
const DEFAULT_TEMPLATE = path.resolve('./src/templates/index.js')
const isNightly = !!+process.env.SPACY_NIGHTLY || site.nightlyBranches.includes(process.env.BRANCH)
const favicon = isNightly ? `src/images/icon_nightly.png` : `src/images/icon.png`
const binderBranch = isNightly ? 'nightly' : site.binderBranch
const siteUrl = isNightly ? site.siteUrlNightly : site.siteUrl
module.exports = {
siteMetadata: {
...site,
@ -22,6 +27,9 @@ module.exports = {
sidebars,
...models,
universe,
nightly: isNightly,
binderBranch,
siteUrl,
},
plugins: [
@ -128,7 +136,7 @@ module.exports = {
background_color: site.theme,
theme_color: site.theme,
display: `minimal-ui`,
icon: `src/images/icon.png`,
icon: favicon,
},
},
{
@ -140,6 +148,23 @@ module.exports = {
respectDNT: true,
},
},
{
resolve: 'gatsby-plugin-robots-txt',
options: {
host: siteUrl,
sitemap: `${siteUrl}/sitemap.xml`,
// If we're in a special state (nightly, legacy) prevent indexing
resolveEnv: () => (isNightly ? 'development' : 'production'),
env: {
production: {
policy: [{ userAgent: '*', allow: '/' }],
},
development: {
policy: [{ userAgent: '*', disallow: ['/'] }],
},
},
},
},
`gatsby-plugin-offline`,
],
}

14
website/meta/languages.json
View File

@ -78,11 +78,14 @@
"name": "Japanese",
"models": ["ja_core_news_sm", "ja_core_news_md", "ja_core_news_lg"],
"dependencies": [
{ "name": "Unidic", "url": "http://unidic.ninjal.ac.jp/back_number#unidic_cwj" },
{ "name": "Mecab", "url": "https://github.com/taku910/mecab" },
{
"name": "SudachiPy",
"url": "https://github.com/WorksApplications/SudachiPy"
}
],
"example": "これは文章です。",
"has_examples": true
},
{
@ -191,17 +194,6 @@
"example": "นี่คือประโยค",
"has_examples": true
},
{
"code": "ja",
"name": "Japanese",
"dependencies": [
{ "name": "Unidic", "url": "http://unidic.ninjal.ac.jp/back_number#unidic_cwj" },
{ "name": "Mecab", "url": "https://github.com/taku910/mecab" },
{ "name": "fugashi", "url": "https://github.com/polm/fugashi" }
],
"example": "これは文章です。",
"has_examples": true
},
{
"code": "ko",
"name": "Korean",

6
website/meta/sidebars.json
View File

@ -8,11 +8,7 @@
{ "text": "Installation", "url": "/usage" },
{ "text": "Models & Languages", "url": "/usage/models" },
{ "text": "Facts & Figures", "url": "/usage/facts-figures" },
{ "text": "spaCy 101", "url": "/usage/spacy-101" },
{ "text": "New in v2.3", "url": "/usage/v2-3" },
{ "text": "New in v2.2", "url": "/usage/v2-2" },
{ "text": "New in v2.1", "url": "/usage/v2-1" },
{ "text": "New in v2.0", "url": "/usage/v2" }
{ "text": "New in v3.0", "url": "/usage/v3" }
]
},
{

2
website/meta/site.json
View File

@ -3,6 +3,8 @@
"description": "spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more.",
"slogan": "Industrial-strength Natural Language Processing in Python",
"siteUrl": "https://spacy.io",
"siteUrlNightly": "https://nightly.spacy.io",
"nightlyBranches": ["spacy.io-develop"],
"email": "contact@explosion.ai",
"company": "Explosion AI",
"companyUrl": "https://explosion.ai",

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4
website/package.json
View File

@ -16,7 +16,7 @@
"autoprefixer": "^9.4.7",
"classnames": "^2.2.6",
"codemirror": "^5.43.0",
"gatsby": "^2.1.18",
"gatsby": "^2.11.1",
"gatsby-image": "^2.0.29",
"gatsby-mdx": "^0.3.6",
"gatsby-plugin-catch-links": "^2.0.11",
@ -25,6 +25,7 @@
"gatsby-plugin-offline": "^2.0.24",
"gatsby-plugin-react-helmet": "^3.0.6",
"gatsby-plugin-react-svg": "^2.0.0",
"gatsby-plugin-robots-txt": "^1.5.1",
"gatsby-plugin-sass": "^2.0.10",
"gatsby-plugin-sharp": "^2.0.20",
"gatsby-plugin-sitemap": "^2.0.5",
@ -52,6 +53,7 @@
"scripts": {
"build": "gatsby build",
"dev": "gatsby develop",
"dev:nightly": "BRANCH=spacy.io-develop npm run dev",
"lint": "eslint **",
"clear": "rm -rf .cache",
"test": "echo \"Write tests! -> https://gatsby.app/unit-testing\""

2
website/src/components/button.js
View File

@ -27,7 +27,7 @@ Button.defaultProps = {
}
Button.propTypes = {
to: PropTypes.string.isRequired,
to: PropTypes.string,
variant: PropTypes.oneOf(['primary', 'secondary', 'tertiary']),
large: PropTypes.bool,
icon: PropTypes.string,

2
website/src/components/icon.js
View File

@ -19,6 +19,7 @@ import { ReactComponent as NoIcon } from '../images/icons/no.svg'
import { ReactComponent as NeutralIcon } from '../images/icons/neutral.svg'
import { ReactComponent as OfflineIcon } from '../images/icons/offline.svg'
import { ReactComponent as SearchIcon } from '../images/icons/search.svg'
import { ReactComponent as MoonIcon } from '../images/icons/moon.svg'
import classes from '../styles/icon.module.sass'
@ -41,6 +42,7 @@ const icons = {
neutral: NeutralIcon,
offline: OfflineIcon,
search: SearchIcon,
moon: MoonIcon,
}
const Icon = ({ name, width, height, inline, variant, className }) => {

9
website/src/components/landing.js
View File

@ -2,7 +2,9 @@ import React, { Fragment } from 'react'
import classNames from 'classnames'
import pattern from '../images/pattern_blue.jpg'
import patternNightly from '../images/pattern_nightly.jpg'
import patternOverlay from '../images/pattern_landing.jpg'
import patternOverlayNightly from '../images/pattern_landing_nightly.jpg'
import logoSvgs from '../images/logos'
import Grid from './grid'
@ -14,9 +16,10 @@ import Link from './link'
import { chunkArray } from './util'
import classes from '../styles/landing.module.sass'
export const LandingHeader = ({ style = {}, children }) => {
const wrapperStyle = { backgroundImage: `url(${pattern})` }
const contentStyle = { backgroundImage: `url(${patternOverlay})`, ...style }
export const LandingHeader = ({ nightly, style = {}, children }) => {
const overlay = nightly ? patternOverlayNightly : patternOverlay
const wrapperStyle = { backgroundImage: `url(${nightly ? patternNightly : pattern})` }
const contentStyle = { backgroundImage: `url(${overlay})`, ...style }
return (
<header className={classes.header}>
<div className={classes.headerWrapper} style={wrapperStyle}>

14
website/src/components/main.js
View File

@ -5,15 +5,22 @@ import classNames from 'classnames'
import patternBlue from '../images/pattern_blue.jpg'
import patternGreen from '../images/pattern_green.jpg'
import patternPurple from '../images/pattern_purple.jpg'
import patternNightly from '../images/pattern_nightly.jpg'
import classes from '../styles/main.module.sass'
const patterns = { blue: patternBlue, green: patternGreen, purple: patternPurple }
const patterns = {
blue: patternBlue,
green: patternGreen,
purple: patternPurple,
nightly: patternNightly,
}
export const Content = ({ Component = 'div', className, children }) => (
<Component className={classNames(classes.content, className)}>{children}</Component>
)
const Main = ({ sidebar, asides, wrapContent, theme, footer, children }) => {
const pattern = patterns[theme]
const mainClassNames = classNames(classes.root, {
[classes.withSidebar]: sidebar,
[classes.withAsides]: asides,
@ -23,10 +30,7 @@ const Main = ({ sidebar, asides, wrapContent, theme, footer, children }) => {
<main className={mainClassNames}>
{wrapContent ? <Content Component="article">{children}</Content> : children}
{asides && (
<div
className={classes.asides}
style={{ backgroundImage: `url(${patterns[theme]}` }}
/>
<div className={classes.asides} style={{ backgroundImage: `url(${pattern}` }} />
)}
{footer}
</main>

8
website/src/components/seo.js
View File

@ -6,6 +6,7 @@ import { StaticQuery, graphql } from 'gatsby'
import socialImageDefault from '../images/social_default.jpg'
import socialImageApi from '../images/social_api.jpg'
import socialImageUniverse from '../images/social_universe.jpg'
import socialImageNightly from '../images/social_nightly.jpg'
function getPageTitle(title, sitename, slogan, sectionTitle) {
if (sectionTitle && title) {
@ -17,13 +18,14 @@ function getPageTitle(title, sitename, slogan, sectionTitle) {
return `${sitename} · ${slogan}`
}
function getImage(section) {
function getImage(section, nightly) {
if (nightly) return socialImageNightly
if (section === 'api') return socialImageApi
if (section === 'universe') return socialImageUniverse
return socialImageDefault
}
const SEO = ({ description, lang, title, section, sectionTitle, bodyClass }) => (
const SEO = ({ description, lang, title, section, sectionTitle, bodyClass, nightly }) => (
<StaticQuery
query={query}
render={data => {
@ -35,7 +37,7 @@ const SEO = ({ description, lang, title, section, sectionTitle, bodyClass }) =>
siteMetadata.slogan,
sectionTitle
)
const socialImage = siteMetadata.siteUrl + getImage(section)
const socialImage = siteMetadata.siteUrl + getImage(section, nightly)
const meta = [
{
name: 'description',

3
website/src/components/tag.js
View File

@ -11,6 +11,9 @@ const Tag = ({ spaced, variant, tooltip, children }) => {
const isValid = isString(children) && !isNaN(children)
const version = isValid ? Number(children).toFixed(1) : children
const tooltipText = `This feature is new and was introduced in spaCy v${version}`
// TODO: we probably want to handle this more elegantly, but the idea is
// that we can hide tags referring to old versions
// const hideTag = version.startsWith('2')
return (
<TagTemplate spaced={spaced} tooltip={tooltipText}>
v{version}

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@ -0,0 +1,3 @@
<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 32 32">
<path d="M10.895 7.574c0 7.55 5.179 13.67 11.567 13.67 1.588 0 3.101-0.38 4.479-1.063-1.695 4.46-5.996 7.636-11.051 7.636-6.533 0-11.83-5.297-11.83-11.83 0-4.82 2.888-8.959 7.023-10.803-0.116 0.778-0.188 1.573-0.188 2.39z"></path>
</svg>
Side by Side

After

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47
website/src/pages/404.js Normal file
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@ -0,0 +1,47 @@
import React from 'react'
import { window } from 'browser-monads'
import { graphql } from 'gatsby'
import Template from '../templates/index'
import { LandingHeader, LandingTitle } from '../components/landing'
import Button from '../components/button'
export default ({ data, location }) => {
const { nightly } = data.site.siteMetadata
const pageContext = { title: '404 Error', searchExclude: true, isIndex: false }
return (
<Template data={data} pageContext={pageContext} location={location}>
<LandingHeader style={{ minHeight: 400 }} nightly={nightly}>
<LandingTitle>
Ooops, this page
<br />
does not exist!
</LandingTitle>
<br />
<Button onClick={() => window.history.go(-1)} variant="tertiary">
Click here to go back
</Button>
</LandingHeader>
</Template>
)
}
export const pageQuery = graphql`
query {
site {
siteMetadata {
nightly
title
description
navigation {
text
url
}
docSearch {
apiKey
indexName
}
}
}
}
`

7
website/src/pages/404.md
View File

@ -1,7 +0,0 @@
---
title: 404 Error
---
import Error from 'widgets/404.js'
<Error />

5
website/src/styles/alert.module.sass
View File

@ -3,11 +3,14 @@
bottom: 0
left: 0
width: 100%
background: var(--color-subtle-light)
background: var(--color-back)
z-index: 100
font: var(--font-size-sm)/var(--line-height-md) var(--font-primary)
text-align: center
padding: 1rem
box-shadow: var(--box-shadow)
border-top: 2px solid
color: var(--color-theme)
.warning
--alert-bg: var(--color-yellow-light)

11
website/src/styles/layout.sass
View File

@ -47,6 +47,11 @@
--color-theme-purple-light: hsla(255, 61%, 54%, 0.06)
--color-theme-purple-opaque: hsla(255, 61%, 54%, 0.11)
--color-theme-nightly: hsl(257, 99%, 67%)
--color-theme-nightly-dark: hsl(257, 99%, 57%)
--color-theme-nightly-light: hsla(257, 99%, 67%, 0.06)
--color-theme-nightly-opaque: hsla(257, 99%, 67%, 0.11)
// Regular colors
--color-back: hsl(0, 0%, 100%)
--color-front: hsl(213, 15%, 12%)
@ -106,6 +111,12 @@
--color-theme-light: var(--color-theme-purple-light)
--color-theme-opaque: var(--color-theme-purple-opaque)
.theme-nightly
--color-theme: var(--color-theme-nightly)
--color-theme-dark: var(--color-theme-nightly-dark)
--color-theme-light: var(--color-theme-nightly-light)
--color-theme-opaque: var(--color-theme-nightly-opaque)
/* Fonts */

3
website/src/styles/sidebar.module.sass
View File

@ -22,6 +22,9 @@ $crumb-bar: 2px
& > *
padding: 0 2rem 0.35rem
&:last-child
margin-bottom: 5rem
.label
color: var(--color-dark)
font: bold var(--font-size-lg)/var(--line-height-md) var(--font-secondary)

5
website/src/templates/docs.js
View File

@ -31,7 +31,7 @@ const Docs = ({ pageContext, children }) => (
theme,
version,
} = pageContext
const { sidebars = [], modelsRepo, languages } = site.siteMetadata
const { sidebars = [], modelsRepo, languages, nightly } = site.siteMetadata
const isModels = section === 'models'
const sidebar = pageContext.sidebar
? { items: pageContext.sidebar }
@ -83,7 +83,7 @@ const Docs = ({ pageContext, children }) => (
{sidebar && <Sidebar items={sidebar.items} pageMenu={pageMenu} slug={slug} />}
<Main
section={section}
theme={theme}
theme={nightly ? 'nightly' : theme}
sidebar
asides
wrapContent
@ -146,6 +146,7 @@ const query = graphql`
models
starters
}
nightly
sidebars {
section
items {

28
website/src/templates/index.js
View File

@ -75,10 +75,23 @@ const scopeComponents = {
InlineCode,
}
const AlertSpace = () => {
const AlertSpace = ({ nightly }) => {
const isOnline = useOnlineStatus()
return (
<>
{nightly && (
<Alert
title="You're viewing the pre-release docs."
icon="moon"
closeOnClick={false}
>
The page reflects{' '}
<Link to="https://pypi.org/project/spacy-nightly/">
<InlineCode>spacy-nightly</InlineCode>
</Link>
, not the latest <Link to="https://spacy.io">stable version</Link>.
</Alert>
)}
{!isOnline && (
<Alert title="Looks like you're offline." icon="offline" variant="warning">
But don't worry, your visited pages should be saved for you.
@ -130,9 +143,10 @@ class Layout extends React.Component {
const { data, pageContext, location, children } = this.props
const { file, site = {} } = data || {}
const mdx = file ? file.childMdx : null
const { title, section, sectionTitle, teaser, theme = 'blue', searchExclude } = pageContext
const bodyClass = classNames(`theme-${theme}`, { 'search-exclude': !!searchExclude })
const meta = site.siteMetadata || {}
const { title, section, sectionTitle, teaser, theme = 'blue', searchExclude } = pageContext
const uiTheme = meta.nightly ? 'nightly' : theme
const bodyClass = classNames(`theme-${uiTheme}`, { 'search-exclude': !!searchExclude })
const isDocs = ['usage', 'models', 'api', 'styleguide'].includes(section)
const content = !mdx ? null : (
<MDXProvider components={mdxComponents}>
@ -148,8 +162,9 @@ class Layout extends React.Component {
section={section}
sectionTitle={sectionTitle}
bodyClass={bodyClass}
nightly={meta.nightly}
/>
<AlertSpace />
<AlertSpace nightly={meta.nightly} />
<Navigation
title={meta.title}
items={meta.navigation}
@ -167,11 +182,11 @@ class Layout extends React.Component {
mdxComponents={mdxComponents}
/>
) : (
<>
<div>
{children}
{content}
<Footer wide />
</>
</div>
)}
</>
)
@ -184,6 +199,7 @@ export const pageQuery = graphql`
query($slug: String!) {
site {
siteMetadata {
nightly
title
description
navigation {

14
website/src/templates/universe.js
View File

@ -30,8 +30,8 @@ function filterResources(resources, data) {
return sorted.filter(res => (res.category || []).includes(data.id))
}
const UniverseContent = ({ content = [], categories, pageContext, location, mdxComponents }) => {
const { theme, data = {} } = pageContext
const UniverseContent = ({ content = [], categories, theme, pageContext, mdxComponents }) => {
const { data = {} } = pageContext
const filteredResources = filterResources(content, data)
const activeData = data ? content.find(({ id }) => id === data.id) : null
const markdownComponents = { ...mdxComponents, code: InlineCode }
@ -302,15 +302,16 @@ const Universe = ({ pageContext, location, mdxComponents }) => (
<StaticQuery
query={query}
render={data => {
const content = data.site.siteMetadata.universe.resources
const categories = data.site.siteMetadata.universe.categories
const { universe, nightly } = data.site.siteMetadata
const theme = nightly ? 'nightly' : pageContext.theme
return (
<UniverseContent
content={content}
categories={categories}
content={universe.resources}
categories={universe.categories}
pageContext={pageContext}
location={location}
mdxComponents={mdxComponents}
theme={theme}
/>
)
}}
@ -323,6 +324,7 @@ const query = graphql`
query UniverseQuery {
site {
siteMetadata {
nightly
universe {
resources {
type

19
website/src/widgets/404.js
View File

@ -1,19 +0,0 @@
import React from 'react'
import { window } from 'browser-monads'
import { LandingHeader, LandingTitle } from '../components/landing'
import Button from '../components/button'
export default () => (
<LandingHeader style={{ minHeight: 400 }}>
<LandingTitle>
Ooops, this page
<br />
does not exist!
</LandingTitle>
<br />
<Button onClick={() => window.history.go(-1)} variant="tertiary">
Click here to go back
</Button>
</LandingHeader>
)

3
website/src/widgets/landing.js
View File

@ -68,7 +68,7 @@ const Landing = ({ data }) => {
const counts = getCounts(data.languages)
return (
<>
<LandingHeader>
<LandingHeader nightly={data.nightly}>
<LandingTitle>
Industrial-Strength
<br />
@ -268,6 +268,7 @@ const landingQuery = graphql`
query LandingQuery {
site {
siteMetadata {
nightly
repo
languages {
models