More formatting changes

fabfile.py

@@ -1,6 +1,6 @@
 import contextlib
 from pathlib import Path
-from fabric.api import local, lcd, env, settings, prefix
+from fabric.api import local, lcd
 from os import path, environ
 import shutil
 import sys
@@ -79,9 +79,7 @@ def pex():
     with virtualenv(VENV_DIR) as venv_local:
         with lcd(path.dirname(__file__)):
             sha = local("git rev-parse --short HEAD", capture=True)
-            venv_local(
-                "pex dist/*.whl -e spacy -o dist/spacy-%s.pex" % sha, direct=True
-            )
+            venv_local(f"pex dist/*.whl -e spacy -o dist/spacy-{sha}.pex", direct=True)
 
 
 def clean():

spacy/__main__.py

@@ -22,7 +22,7 @@ if __name__ == "__main__":
     if len(sys.argv) == 1:
         msg.info("Available commands", ", ".join(commands), exits=1)
     command = sys.argv.pop(1)
-    sys.argv[0] = "spacy %s" % command
+    sys.argv[0] = f"spacy {command}"
     if command in commands:
         plac.call(commands[command], sys.argv[1:])
     else:

spacy/_ml.py

@@ -296,7 +296,7 @@ def link_vectors_to_models(vocab):
             # This is a hack to avoid the problem in #3853. Maybe we should
             # print a warning as well?
             old_name = vectors.name
-            new_name = vectors.name + "_%d" % data.shape[0]
+            new_name = f"{vectors.name}_{data.shape[0]}"
             user_warning(Warnings.W019.format(old=old_name, new=new_name))
             vectors.name = new_name
             key = (ops.device, vectors.name)

spacy/cli/evaluate.py

@@ -47,20 +47,20 @@ def evaluate(
     end = timer()
     nwords = sum(len(ex.doc) for ex in dev_dataset)
     results = {
-        "Time": "%.2f s" % (end - begin),
+        "Time": f"{end - begin:.2f} s",
         "Words": nwords,
-        "Words/s": "%.0f" % (nwords / (end - begin)),
-        "TOK": "%.2f" % scorer.token_acc,
-        "POS": "%.2f" % scorer.tags_acc,
-        "UAS": "%.2f" % scorer.uas,
-        "LAS": "%.2f" % scorer.las,
-        "NER P": "%.2f" % scorer.ents_p,
-        "NER R": "%.2f" % scorer.ents_r,
-        "NER F": "%.2f" % scorer.ents_f,
-        "Textcat": "%.2f" % scorer.textcat_score,
-        "Sent P": "%.2f" % scorer.sent_p,
-        "Sent R": "%.2f" % scorer.sent_r,
-        "Sent F": "%.2f" % scorer.sent_f,
+        "Words/s": f"{nwords / (end - begin):.0f}",
+        "TOK": f"{scorer.token_acc:.2f}",
+        "POS": f"{scorer.tags_acc:.2f}",
+        "UAS": f"{scorer.uas:.2f}",
+        "LAS": f"{scorer.las:.2f}",
+        "NER P": f"{scorer.ents_p:.2f}",
+        "NER R": f"{scorer.ents_r:.2f}",
+        "NER F": f"{scorer.ents_f:.2f}",
+        "Textcat": f"{scorer.textcat_score:.2f}",
+        "Sent P": f"{scorer.sent_p:.2f}",
+        "Sent R": f"{scorer.sent_r:.2f}",
+        "Sent F": f"{scorer.sent_f:.2f}",
     }
     msg.table(results, title="Results")
 

spacy/cli/init_model.py

@@ -186,7 +186,7 @@ def add_vectors(nlp, vectors_loc, prune_vectors, name=None):
         if vectors_data is not None:
             nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
     if name is None:
-        nlp.vocab.vectors.name = "%s_model.vectors" % nlp.meta["lang"]
+        nlp.vocab.vectors.name = f"{nlp.meta['lang']}_model.vectors"
     else:
         nlp.vocab.vectors.name = name
     nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
@@ -232,7 +232,7 @@ def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
                     word = literal_eval(key)
                 except SyntaxError:
                     # Take odd strings literally.
-                    word = literal_eval("'%s'" % key)
+                    word = literal_eval(f"'{key}'")
                 smooth_count = counts.smoother(int(freq))
                 probs[word] = math.log(smooth_count) - log_total
     oov_prob = math.log(counts.smoother(0)) - log_total

spacy/cli/package.py

@@ -83,7 +83,7 @@ def generate_meta(model_path, existing_meta, msg):
         ("lang", "Model language", meta.get("lang", "en")),
         ("name", "Model name", meta.get("name", "model")),
         ("version", "Model version", meta.get("version", "0.0.0")),
-        ("spacy_version", "Required spaCy version", ">=%s,<3.0.0" % about.__version__),
+        ("spacy_version", "Required spaCy version", f">={about.__version__},<3.0.0"),
         ("description", "Model description", meta.get("description", False)),
         ("author", "Author", meta.get("author", False)),
         ("email", "Author email", meta.get("email", False)),

spacy/cli/pretrain.py

@@ -179,14 +179,12 @@ def pretrain(
         else:
             if not epoch_start:
                 msg.fail(
-                    "You have to use the '--epoch-start' argument when using a renamed weight file for "
-                    "'--init-tok2vec'",
+                    "You have to use the --epoch-start argument when using a renamed weight file for --init-tok2vec",
                     exits=True,
                 )
             elif epoch_start < 0:
                 msg.fail(
-                    "The argument '--epoch-start' has to be greater or equal to 0. '%d' is invalid"
-                    % epoch_start,
+                    f"The argument --epoch-start has to be greater or equal to 0. {epoch_start} is invalid",
                     exits=True,
                 )
     else:
@@ -195,16 +193,14 @@ def pretrain(
 
     optimizer = create_default_optimizer(model.ops)
     tracker = ProgressTracker(frequency=10000)
-    msg.divider("Pre-training tok2vec layer - starting at epoch %d" % epoch_start)
+    msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_start}")
     row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
     msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
 
     def _save_model(epoch, is_temp=False):
         is_temp_str = ".temp" if is_temp else ""
         with model.use_params(optimizer.averages):
-            with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
-                "wb"
-            ) as file_:
+            with (output_dir / f"model{epoch}{is_temp_str}.bin").open("wb") as file_:
                 file_.write(model.tok2vec.to_bytes())
             log = {
                 "nr_word": tracker.nr_word,

spacy/cli/train.py

@@ -375,7 +375,7 @@ def train(
                     words_seen += sum(len(doc) for doc in docs)
             with nlp.use_params(optimizer.averages):
                 util.set_env_log(False)
-                epoch_model_path = output_path / ("model%d" % i)
+                epoch_model_path = output_path / f"model{i}"
                 nlp.to_disk(epoch_model_path)
                 nlp_loaded = util.load_model_from_path(epoch_model_path)
                 for beam_width in eval_beam_widths:
@@ -414,13 +414,13 @@ def train(
                             scorer = nlp_loaded.evaluate(dev_dataset, verbose=verbose)
                             end_time = timer()
                             cpu_wps = nwords / (end_time - start_time)
-                    acc_loc = output_path / ("model%d" % i) / "accuracy.json"
+                    acc_loc = output_path / f"model{i}" / "accuracy.json"
                     srsly.write_json(acc_loc, scorer.scores)
 
                     # Update model meta.json
                     meta["lang"] = nlp.lang
                     meta["pipeline"] = nlp.pipe_names
-                    meta["spacy_version"] = ">=%s" % about.__version__
+                    meta["spacy_version"] = f">={about.__version__}"
                     if beam_width == 1:
                         meta["speed"] = {
                             "nwords": nwords,
@@ -443,10 +443,10 @@ def train(
                         "keys": nlp.vocab.vectors.n_keys,
                         "name": nlp.vocab.vectors.name,
                     }
-                    meta.setdefault("name", "model%d" % i)
+                    meta.setdefault("name", f"model{i}")
                     meta.setdefault("version", version)
                     meta["labels"] = nlp.meta["labels"]
-                    meta_loc = output_path / ("model%d" % i) / "meta.json"
+                    meta_loc = output_path / f"model{i}" / "meta.json"
                     srsly.write_json(meta_loc, meta)
                     util.set_env_log(verbose)
 

spacy/gold.pyx

@@ -615,7 +615,7 @@ def _consume_ent(tags):
     else:
         start = "B-" + label
         end = "L-" + label
-        middle = ["I-%s" % label for _ in range(1, length - 1)]
+        middle = [f"I-{label}" for _ in range(1, length - 1)]
         return [start] + middle + [end]
 
 
@@ -1204,12 +1204,12 @@ def biluo_tags_from_offsets(doc, entities, missing="O"):
         # Only interested if the tokenization is correct
         if start_token is not None and end_token is not None:
             if start_token == end_token:
-                biluo[start_token] = "U-%s" % label
+                biluo[start_token] = f"U-{label}"
             else:
-                biluo[start_token] = "B-%s" % label
+                biluo[start_token] = f"B-{label}"
                 for i in range(start_token+1, end_token):
-                    biluo[i] = "I-%s" % label
-                biluo[end_token] = "L-%s" % label
+                    biluo[i] = f"I-{label}"
+                biluo[end_token] = f"L-{label}"
     # Now distinguish the O cases from ones where we miss the tokenization
     entity_chars = set()
     for start_char, end_char, label in entities:

spacy/kb.pyx

@@ -442,7 +442,7 @@ cdef class KnowledgeBase:
 cdef class Writer:
     def __init__(self, object loc):
         if path.exists(loc):
-            assert not path.isdir(loc), "%s is directory." % loc
+            assert not path.isdir(loc), f"{loc} is directory"
         if isinstance(loc, Path):
             loc = bytes(loc)
         cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc

spacy/lang/ca/tokenizer_exceptions.py

@@ -30,9 +30,9 @@ _exc["12m."] = [{ORTH: "12"}, {ORTH: "m.", LEMMA: "p.m."}]
 
 for h in range(1, 12 + 1):
     for period in ["a.m.", "am"]:
-        _exc["%d%s" % (h, period)] = [{ORTH: "%d" % h}, {ORTH: period, LEMMA: "a.m."}]
+        _exc[f"{h}{period}"] = [{ORTH: f"{h}"}, {ORTH: period, LEMMA: "a.m."}]
     for period in ["p.m.", "pm"]:
-        _exc["%d%s" % (h, period)] = [{ORTH: "%d" % h}, {ORTH: period, LEMMA: "p.m."}]
+        _exc[f"{h}{period}"] = [{ORTH: f"{h}"}, {ORTH: period, LEMMA: "p.m."}]
 
 
 TOKENIZER_EXCEPTIONS = _exc

spacy/lang/da/tokenizer_exceptions.py

@@ -559,7 +559,7 @@ for exc_data in [
 # Dates
 for h in range(1, 31 + 1):
     for period in ["."]:
-        _exc["%d%s" % (h, period)] = [{ORTH: "%d." % h}]
+        _exc[f"{h}{period}"] = [{ORTH: f"{h}."}]
 
 _custom_base_exc = {"i.": [{ORTH: "i", LEMMA: "i", NORM: "i"}, {ORTH: ".", TAG: PUNCT}]}
 _exc.update(_custom_base_exc)

spacy/lang/el/tokenizer_exceptions.py

@@ -131,14 +131,14 @@ _exc.update(_other_exc)
 for h in range(1, 12 + 1):
 
     for period in ["π.μ.", "πμ"]:
-        _exc["%d%s" % (h, period)] = [
-            {ORTH: "%d" % h},
+        _exc[f"{h}{period}"] = [
+            {ORTH: f"{h}"},
             {ORTH: period, LEMMA: "π.μ.", NORM: "π.μ."},
         ]
 
     for period in ["μ.μ.", "μμ"]:
-        _exc["%d%s" % (h, period)] = [
-            {ORTH: "%d" % h},
+        _exc[f"{h}{period}"] = [
+            {ORTH: f"{h}"},
             {ORTH: period, LEMMA: "μ.μ.", NORM: "μ.μ."},
         ]
 

spacy/lang/en/tokenizer_exceptions.py

@@ -328,13 +328,13 @@ for exc_data in [
 
 for h in range(1, 12 + 1):
     for period in ["a.m.", "am"]:
-        _exc["%d%s" % (h, period)] = [
-            {ORTH: "%d" % h},
+        _exc[f"{h}{period}"] = [
+            {ORTH: f"{h}"},
             {ORTH: period, LEMMA: "a.m.", NORM: "a.m."},
         ]
     for period in ["p.m.", "pm"]:
-        _exc["%d%s" % (h, period)] = [
-            {ORTH: "%d" % h},
+        _exc[f"{h}{period}"] = [
+            {ORTH: f"{h}"},
             {ORTH: period, LEMMA: "p.m.", NORM: "p.m."},
         ]
 

spacy/lang/es/tokenizer_exceptions.py

@@ -28,9 +28,9 @@ _exc["12m."] = [{ORTH: "12"}, {ORTH: "m.", LEMMA: "p.m."}]
 
 for h in range(1, 12 + 1):
     for period in ["a.m.", "am"]:
-        _exc["%d%s" % (h, period)] = [{ORTH: "%d" % h}, {ORTH: period, LEMMA: "a.m."}]
+        _exc[f"{h}{period}"] = [{ORTH: f"{h}"}, {ORTH: period, LEMMA: "a.m."}]
     for period in ["p.m.", "pm"]:
-        _exc["%d%s" % (h, period)] = [{ORTH: "%d" % h}, {ORTH: period, LEMMA: "p.m."}]
+        _exc[f"{h}{period}"] = [{ORTH: f"{h}"}, {ORTH: period, LEMMA: "p.m."}]
 
 
 for orth in [

spacy/lang/fr/tokenizer_exceptions.py

@@ -85,7 +85,7 @@ for verb, verb_lemma in [("est", "être")]:
 
 for pre, pre_lemma in [("qu'", "que"), ("n'", "ne")]:
     for orth in [pre, pre.title()]:
-        _exc["%sest-ce" % orth] = [
+        _exc[f"{orth}est-ce"] = [
             {LEMMA: pre_lemma, ORTH: orth, TAG: "ADV"},
             {LEMMA: "être", ORTH: "est", TAG: "VERB"},
             {LEMMA: "ce", ORTH: "-ce"},

spacy/language.py

@@ -1015,7 +1015,7 @@ def _fix_pretrained_vectors_name(nlp):
     elif not nlp.vocab.vectors.size:
         nlp.vocab.vectors.name = None
     elif "name" in nlp.meta and "lang" in nlp.meta:
-        vectors_name = "%s_%s.vectors" % (nlp.meta["lang"], nlp.meta["name"])
+        vectors_name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
         nlp.vocab.vectors.name = vectors_name
     else:
         raise ValueError(Errors.E092)

spacy/morphology.pyx

@@ -72,7 +72,7 @@ def _normalize_props(props):
             # just take the first one :(
             if "|" in value:
                 value = value.split("|")[0]
-            attr = '%s_%s' % (key, value)
+            attr = f"{key}_{value}"
             if attr in FEATURES:
                 props.pop(key)
                 props[attr] = True

spacy/pipeline/pipes.pyx

@@ -985,14 +985,14 @@ class MultitaskObjective(Tagger):
         offset = token_annotation.heads[i] - i
         offset = min(offset, 2)
         offset = max(offset, -2)
-        return "%s-%s:%d" % (token_annotation.deps[i], token_annotation.tags[i], offset)
+        return f"{token_annotation.deps[i]}-{token_annotation.tags[i]}:{offset}"
 
     @staticmethod
     def make_ent_tag(i, token_annotation):
         if token_annotation.entities is None or token_annotation.entities[i] is None:
             return None
         else:
-            return "%s-%s" % (token_annotation.tags[i], token_annotation.entities[i])
+            return f"{token_annotation.tags[i]}-{token_annotation.entities[i]}"
 
     @staticmethod
     def make_sent_start(target, token_annotation, cache=True, _cache={}):

spacy/syntax/nonproj.pyx

@@ -154,8 +154,7 @@ def _decorate(heads, proj_heads, labels):
     deco_labels = []
     for tokenid, head in enumerate(heads):
         if head != proj_heads[tokenid]:
-            deco_labels.append(
-                '%s%s%s' % (labels[tokenid], DELIMITER, labels[head]))
+            deco_labels.append(f"{labels[tokenid]}{DELIMITER}{labels[head]}")
         else:
             deco_labels.append(labels[tokenid])
     return deco_labels

spacy/syntax/stateclass.pyx

@@ -46,9 +46,9 @@ cdef class StateClass:
 
     def print_state(self, words):
         words = list(words) + ['_']
-        top = words[self.S(0)] + '_%d' % self.S_(0).head
-        second = words[self.S(1)] + '_%d' % self.S_(1).head
-        third = words[self.S(2)] + '_%d' % self.S_(2).head
+        top = f"{words[self.S(0)]}_{self.S_(0).head}"
+        second = f"{words[self.S(1)]}_{self.S_(1).head}"
+        third = f"{words[self.S(2)]}_{self.S_(2).head}"
         n0 = words[self.B(0)]
         n1 = words[self.B(1)]
         return ' '.join((third, second, top, '|', n0, n1))

spacy/tests/conftest.py

@@ -14,11 +14,11 @@ def pytest_runtest_setup(item):
         # recognize the option we're asking about. To avoid this, we need to
         # pass a default value. We default to False, i.e., we act like all the
         # options weren't given.
-        return item.config.getoption("--%s" % opt, False)
+        return item.config.getoption(f"--{opt}", False)
 
     for opt in ["slow"]:
         if opt in item.keywords and not getopt(opt):
-            pytest.skip("need --%s option to run" % opt)
+            pytest.skip(f"need --{opt} option to run")
 
 
 # Fixtures for language tokenizers (languages sorted alphabetically)

spacy/tests/pipeline/test_pipe_methods.py

@@ -115,7 +115,7 @@ def test_disable_pipes_list_arg(nlp):
 @pytest.mark.parametrize("n_pipes", [100])
 def test_add_lots_of_pipes(nlp, n_pipes):
     for i in range(n_pipes):
-        nlp.add_pipe(lambda doc: doc, name="pipe_%d" % i)
+        nlp.add_pipe(lambda doc: doc, name=f"pipe_{i}")
     assert len(nlp.pipe_names) == n_pipes
 
 

spacy/tokens/doc.pyx

@@ -499,7 +499,7 @@ cdef class Doc:
                 token = &self.c[i]
                 if token.ent_iob == 1:
                     if start == -1:
-                        seq = ["%s|%s" % (t.text, t.ent_iob_) for t in self[i-5:i+5]]
+                        seq = [f"{t.text}|{t.ent_iob_}" for t in self[i-5:i+5]]
                         raise ValueError(Errors.E093.format(seq=" ".join(seq)))
                 elif token.ent_iob == 2 or token.ent_iob == 0:
                     if start != -1:

spacy/util.py

@@ -62,7 +62,7 @@ def get_lang_class(lang):
         return registry.languages.get(lang)
     else:
         try:
-            module = importlib.import_module(".lang.%s" % lang, "spacy")
+            module = importlib.import_module(f".lang.{lang}", "spacy")
         except ImportError as err:
             raise ImportError(Errors.E048.format(lang=lang, err=err))
         set_lang_class(lang, getattr(module, module.__all__[0]))
@@ -212,7 +212,7 @@ def load_model_from_init_py(init_file, **overrides):
     """
     model_path = Path(init_file).parent
     meta = get_model_meta(model_path)
-    data_dir = "%s_%s-%s" % (meta["lang"], meta["name"], meta["version"])
+    data_dir = f"{meta['lang']}_{meta['name']}-{meta['version']}"
     data_path = model_path / data_dir
     if not model_path.exists():
         raise IOError(Errors.E052.format(path=data_path))