diff --git a/.github/contributors/cedar101.md b/.github/contributors/cedar101.md
new file mode 100644
index 000000000..4d04ebacf
--- /dev/null
+++ b/.github/contributors/cedar101.md
@@ -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 UG (haftungsbeschränkt)](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 | Kim, Baeg-il |
+| Company name (if applicable) | |
+| Title or role (if applicable) | |
+| Date | 2019-07-03 |
+| GitHub username | cedar101 |
+| Website (optional) | |
diff --git a/.github/contributors/yashpatadia.md b/.github/contributors/yashpatadia.md
new file mode 100644
index 000000000..2dcf9211d
--- /dev/null
+++ b/.github/contributors/yashpatadia.md
@@ -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 | Yash Patadia |
+| Company name (if applicable) | |
+| Title or role (if applicable) | |
+| Date | 11/07/2019 |
+| GitHub username | yash1994 |
+| Website (optional) | |
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index ef586ac8d..35d431d48 100644
--- a/.gitignore
+++ b/.gitignore
@@ -56,6 +56,8 @@ parts/
sdist/
var/
*.egg-info/
+pip-wheel-metadata/
+Pipfile.lock
.installed.cfg
*.egg
.eggs
diff --git a/bin/__init__.py b/bin/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/bin/train_word_vectors.py b/bin/train_word_vectors.py
index 624e339a0..663ce060d 100644
--- a/bin/train_word_vectors.py
+++ b/bin/train_word_vectors.py
@@ -5,7 +5,6 @@ import logging
from pathlib import Path
from collections import defaultdict
from gensim.models import Word2Vec
-from preshed.counter import PreshCounter
import plac
import spacy
diff --git a/bin/ud/conll17_ud_eval.py b/bin/ud/conll17_ud_eval.py
index 78a976a6d..88acfabac 100644
--- a/bin/ud/conll17_ud_eval.py
+++ b/bin/ud/conll17_ud_eval.py
@@ -292,8 +292,8 @@ def evaluate(gold_ud, system_ud, deprel_weights=None, check_parse=True):
def spans_score(gold_spans, system_spans):
correct, gi, si = 0, 0, 0
- undersegmented = list()
- oversegmented = list()
+ undersegmented = []
+ oversegmented = []
combo = 0
previous_end_si_earlier = False
previous_end_gi_earlier = False
diff --git a/bin/wiki_entity_linking/__init__.py b/bin/wiki_entity_linking/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/bin/wiki_entity_linking/kb_creator.py b/bin/wiki_entity_linking/kb_creator.py
new file mode 100644
index 000000000..e8e081cef
--- /dev/null
+++ b/bin/wiki_entity_linking/kb_creator.py
@@ -0,0 +1,171 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+from .train_descriptions import EntityEncoder
+from . import wikidata_processor as wd, wikipedia_processor as wp
+from spacy.kb import KnowledgeBase
+
+import csv
+import datetime
+
+
+INPUT_DIM = 300 # dimension of pre-trained input vectors
+DESC_WIDTH = 64 # dimension of output entity vectors
+
+
+def create_kb(nlp, max_entities_per_alias, min_entity_freq, min_occ,
+ entity_def_output, entity_descr_output,
+ count_input, prior_prob_input, wikidata_input):
+ # Create the knowledge base from Wikidata entries
+ kb = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=DESC_WIDTH)
+
+ # disable this part of the pipeline when rerunning the KB generation from preprocessed files
+ read_raw_data = True
+
+ if read_raw_data:
+ print()
+ print(" * _read_wikidata_entities", datetime.datetime.now())
+ title_to_id, id_to_descr = wd.read_wikidata_entities_json(wikidata_input)
+
+ # write the title-ID and ID-description mappings to file
+ _write_entity_files(entity_def_output, entity_descr_output, title_to_id, id_to_descr)
+
+ else:
+ # read the mappings from file
+ title_to_id = get_entity_to_id(entity_def_output)
+ id_to_descr = get_id_to_description(entity_descr_output)
+
+ print()
+ print(" * _get_entity_frequencies", datetime.datetime.now())
+ print()
+ entity_frequencies = wp.get_all_frequencies(count_input=count_input)
+
+ # filter the entities for in the KB by frequency, because there's just too much data (8M entities) otherwise
+ filtered_title_to_id = dict()
+ entity_list = []
+ description_list = []
+ frequency_list = []
+ for title, entity in title_to_id.items():
+ freq = entity_frequencies.get(title, 0)
+ desc = id_to_descr.get(entity, None)
+ if desc and freq > min_entity_freq:
+ entity_list.append(entity)
+ description_list.append(desc)
+ frequency_list.append(freq)
+ filtered_title_to_id[title] = entity
+
+ print("Kept", len(filtered_title_to_id.keys()), "out of", len(title_to_id.keys()),
+ "titles with filter frequency", min_entity_freq)
+
+ print()
+ print(" * train entity encoder", datetime.datetime.now())
+ print()
+ encoder = EntityEncoder(nlp, INPUT_DIM, DESC_WIDTH)
+ encoder.train(description_list=description_list, to_print=True)
+
+ print()
+ print(" * get entity embeddings", datetime.datetime.now())
+ print()
+ embeddings = encoder.apply_encoder(description_list)
+
+ print()
+ print(" * adding", len(entity_list), "entities", datetime.datetime.now())
+ kb.set_entities(entity_list=entity_list, prob_list=frequency_list, vector_list=embeddings)
+
+ print()
+ print(" * adding aliases", datetime.datetime.now())
+ print()
+ _add_aliases(kb, title_to_id=filtered_title_to_id,
+ max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,
+ prior_prob_input=prior_prob_input)
+
+ print()
+ print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
+
+ print("done with kb", datetime.datetime.now())
+ return kb
+
+
+def _write_entity_files(entity_def_output, entity_descr_output, title_to_id, id_to_descr):
+ with open(entity_def_output, mode='w', encoding='utf8') as id_file:
+ id_file.write("WP_title" + "|" + "WD_id" + "\n")
+ for title, qid in title_to_id.items():
+ id_file.write(title + "|" + str(qid) + "\n")
+
+ with open(entity_descr_output, mode='w', encoding='utf8') as descr_file:
+ descr_file.write("WD_id" + "|" + "description" + "\n")
+ for qid, descr in id_to_descr.items():
+ descr_file.write(str(qid) + "|" + descr + "\n")
+
+
+def get_entity_to_id(entity_def_output):
+ entity_to_id = dict()
+ with open(entity_def_output, 'r', encoding='utf8') as csvfile:
+ csvreader = csv.reader(csvfile, delimiter='|')
+ # skip header
+ next(csvreader)
+ for row in csvreader:
+ entity_to_id[row[0]] = row[1]
+ return entity_to_id
+
+
+def get_id_to_description(entity_descr_output):
+ id_to_desc = dict()
+ with open(entity_descr_output, 'r', encoding='utf8') as csvfile:
+ csvreader = csv.reader(csvfile, delimiter='|')
+ # skip header
+ next(csvreader)
+ for row in csvreader:
+ id_to_desc[row[0]] = row[1]
+ return id_to_desc
+
+
+def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, prior_prob_input):
+ wp_titles = title_to_id.keys()
+
+ # adding aliases with prior probabilities
+ # we can read this file sequentially, it's sorted by alias, and then by count
+ with open(prior_prob_input, mode='r', encoding='utf8') as prior_file:
+ # skip header
+ prior_file.readline()
+ line = prior_file.readline()
+ previous_alias = None
+ total_count = 0
+ counts = []
+ entities = []
+ while line:
+ splits = line.replace('\n', "").split(sep='|')
+ new_alias = splits[0]
+ count = int(splits[1])
+ entity = splits[2]
+
+ if new_alias != previous_alias and previous_alias:
+ # done reading the previous alias --> output
+ if len(entities) > 0:
+ selected_entities = []
+ prior_probs = []
+ for ent_count, ent_string in zip(counts, entities):
+ if ent_string in wp_titles:
+ wd_id = title_to_id[ent_string]
+ p_entity_givenalias = ent_count / total_count
+ selected_entities.append(wd_id)
+ prior_probs.append(p_entity_givenalias)
+
+ if selected_entities:
+ try:
+ kb.add_alias(alias=previous_alias, entities=selected_entities, probabilities=prior_probs)
+ except ValueError as e:
+ print(e)
+ total_count = 0
+ counts = []
+ entities = []
+
+ total_count += count
+
+ if len(entities) < max_entities_per_alias and count >= min_occ:
+ counts.append(count)
+ entities.append(entity)
+ previous_alias = new_alias
+
+ line = prior_file.readline()
+
diff --git a/bin/wiki_entity_linking/train_descriptions.py b/bin/wiki_entity_linking/train_descriptions.py
new file mode 100644
index 000000000..6a4d046e5
--- /dev/null
+++ b/bin/wiki_entity_linking/train_descriptions.py
@@ -0,0 +1,121 @@
+# coding: utf-8
+from random import shuffle
+
+import numpy as np
+
+from spacy._ml import zero_init, create_default_optimizer
+from spacy.cli.pretrain import get_cossim_loss
+
+from thinc.v2v import Model
+from thinc.api import chain
+from thinc.neural._classes.affine import Affine
+
+
+class EntityEncoder:
+ """
+ Train the embeddings of entity descriptions to fit a fixed-size entity vector (e.g. 64D).
+ This entity vector will be stored in the KB, for further downstream use in the entity model.
+ """
+
+ DROP = 0
+ EPOCHS = 5
+ STOP_THRESHOLD = 0.04
+
+ BATCH_SIZE = 1000
+
+ def __init__(self, nlp, input_dim, desc_width):
+ self.nlp = nlp
+ self.input_dim = input_dim
+ self.desc_width = desc_width
+
+ def apply_encoder(self, description_list):
+ if self.encoder is None:
+ raise ValueError("Can not apply encoder before training it")
+
+ batch_size = 100000
+
+ start = 0
+ stop = min(batch_size, len(description_list))
+ encodings = []
+
+ while start < len(description_list):
+ docs = list(self.nlp.pipe(description_list[start:stop]))
+ doc_embeddings = [self._get_doc_embedding(doc) for doc in docs]
+ enc = self.encoder(np.asarray(doc_embeddings))
+ encodings.extend(enc.tolist())
+
+ start = start + batch_size
+ stop = min(stop + batch_size, len(description_list))
+
+ return encodings
+
+ def train(self, description_list, to_print=False):
+ processed, loss = self._train_model(description_list)
+ if to_print:
+ print("Trained on", processed, "entities across", self.EPOCHS, "epochs")
+ print("Final loss:", loss)
+
+ def _train_model(self, description_list):
+ # TODO: when loss gets too low, a 'mean of empty slice' warning is thrown by numpy
+
+ self._build_network(self.input_dim, self.desc_width)
+
+ processed = 0
+ loss = 1
+ descriptions = description_list.copy() # copy this list so that shuffling does not affect other functions
+
+ for i in range(self.EPOCHS):
+ shuffle(descriptions)
+
+ batch_nr = 0
+ start = 0
+ stop = min(self.BATCH_SIZE, len(descriptions))
+
+ while loss > self.STOP_THRESHOLD and start < len(descriptions):
+ batch = []
+ for descr in descriptions[start:stop]:
+ doc = self.nlp(descr)
+ doc_vector = self._get_doc_embedding(doc)
+ batch.append(doc_vector)
+
+ loss = self._update(batch)
+ print(i, batch_nr, loss)
+ processed += len(batch)
+
+ batch_nr += 1
+ start = start + self.BATCH_SIZE
+ stop = min(stop + self.BATCH_SIZE, len(descriptions))
+
+ return processed, loss
+
+ @staticmethod
+ def _get_doc_embedding(doc):
+ indices = np.zeros((len(doc),), dtype="i")
+ for i, word in enumerate(doc):
+ if word.orth in doc.vocab.vectors.key2row:
+ indices[i] = doc.vocab.vectors.key2row[word.orth]
+ else:
+ indices[i] = 0
+ word_vectors = doc.vocab.vectors.data[indices]
+ doc_vector = np.mean(word_vectors, axis=0)
+ return doc_vector
+
+ def _build_network(self, orig_width, hidden_with):
+ with Model.define_operators({">>": chain}):
+ # very simple encoder-decoder model
+ self.encoder = (
+ Affine(hidden_with, orig_width)
+ )
+ self.model = self.encoder >> zero_init(Affine(orig_width, hidden_with, drop_factor=0.0))
+ self.sgd = create_default_optimizer(self.model.ops)
+
+ def _update(self, vectors):
+ predictions, bp_model = self.model.begin_update(np.asarray(vectors), drop=self.DROP)
+ loss, d_scores = self._get_loss(scores=predictions, golds=np.asarray(vectors))
+ bp_model(d_scores, sgd=self.sgd)
+ return loss / len(vectors)
+
+ @staticmethod
+ def _get_loss(golds, scores):
+ loss, gradients = get_cossim_loss(scores, golds)
+ return loss, gradients
diff --git a/bin/wiki_entity_linking/training_set_creator.py b/bin/wiki_entity_linking/training_set_creator.py
new file mode 100644
index 000000000..5d401bb3f
--- /dev/null
+++ b/bin/wiki_entity_linking/training_set_creator.py
@@ -0,0 +1,353 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import os
+import re
+import bz2
+import datetime
+
+from spacy.gold import GoldParse
+from bin.wiki_entity_linking import kb_creator
+
+"""
+Process Wikipedia interlinks to generate a training dataset for the EL algorithm.
+Gold-standard entities are stored in one file in standoff format (by character offset).
+"""
+
+ENTITY_FILE = "gold_entities.csv"
+
+
+def create_training(wikipedia_input, entity_def_input, training_output):
+ wp_to_id = kb_creator.get_entity_to_id(entity_def_input)
+ _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=None)
+
+
+def _process_wikipedia_texts(wikipedia_input, wp_to_id, training_output, limit=None):
+ """
+ Read the XML wikipedia data to parse out training data:
+ raw text data + positive instances
+ """
+ title_regex = re.compile(r'(?<=<title>).*(?=</title>)')
+ id_regex = re.compile(r'(?<=<id>)\d*(?=</id>)')
+
+ read_ids = set()
+ entityfile_loc = training_output / ENTITY_FILE
+ with open(entityfile_loc, mode="w", encoding='utf8') as entityfile:
+ # write entity training header file
+ _write_training_entity(outputfile=entityfile,
+ article_id="article_id",
+ alias="alias",
+ entity="WD_id",
+ start="start",
+ end="end")
+
+ with bz2.open(wikipedia_input, mode='rb') as file:
+ line = file.readline()
+ cnt = 0
+ article_text = ""
+ article_title = None
+ article_id = None
+ reading_text = False
+ reading_revision = False
+ while line and (not limit or cnt < limit):
+ if cnt % 1000000 == 0:
+ print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
+ clean_line = line.strip().decode("utf-8")
+
+ if clean_line == "<revision>":
+ reading_revision = True
+ elif clean_line == "</revision>":
+ reading_revision = False
+
+ # Start reading new page
+ if clean_line == "<page>":
+ article_text = ""
+ article_title = None
+ article_id = None
+
+ # finished reading this page
+ elif clean_line == "</page>":
+ if article_id:
+ try:
+ _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_text.strip(),
+ training_output)
+ except Exception as e:
+ print("Error processing article", article_id, article_title, e)
+ else:
+ print("Done processing a page, but couldn't find an article_id ?", article_title)
+ article_text = ""
+ article_title = None
+ article_id = None
+ reading_text = False
+ reading_revision = False
+
+ # start reading text within a page
+ if "<text" in clean_line:
+ reading_text = True
+
+ if reading_text:
+ article_text += " " + clean_line
+
+ # stop reading text within a page (we assume a new page doesn't start on the same line)
+ if "</text" in clean_line:
+ reading_text = False
+
+ # read the ID of this article (outside the revision portion of the document)
+ if not reading_revision:
+ ids = id_regex.search(clean_line)
+ if ids:
+ article_id = ids[0]
+ if article_id in read_ids:
+ print("Found duplicate article ID", article_id, clean_line) # This should never happen ...
+ read_ids.add(article_id)
+
+ # read the title of this article (outside the revision portion of the document)
+ if not reading_revision:
+ titles = title_regex.search(clean_line)
+ if titles:
+ article_title = titles[0].strip()
+
+ line = file.readline()
+ cnt += 1
+
+
+text_regex = re.compile(r'(?<=<text xml:space=\"preserve\">).*(?=</text)')
+
+
+def _process_wp_text(wp_to_id, entityfile, article_id, article_title, article_text, training_output):
+ found_entities = False
+
+ # ignore meta Wikipedia pages
+ if article_title.startswith("Wikipedia:"):
+ return
+
+ # remove the text tags
+ text = text_regex.search(article_text).group(0)
+
+ # stop processing if this is a redirect page
+ if text.startswith("#REDIRECT"):
+ return
+
+ # get the raw text without markup etc, keeping only interwiki links
+ clean_text = _get_clean_wp_text(text)
+
+ # read the text char by char to get the right offsets for the interwiki links
+ final_text = ""
+ open_read = 0
+ reading_text = True
+ reading_entity = False
+ reading_mention = False
+ reading_special_case = False
+ entity_buffer = ""
+ mention_buffer = ""
+ for index, letter in enumerate(clean_text):
+ if letter == '[':
+ open_read += 1
+ elif letter == ']':
+ open_read -= 1
+ elif letter == '|':
+ if reading_text:
+ final_text += letter
+ # switch from reading entity to mention in the [[entity|mention]] pattern
+ elif reading_entity:
+ reading_text = False
+ reading_entity = False
+ reading_mention = True
+ else:
+ reading_special_case = True
+ else:
+ if reading_entity:
+ entity_buffer += letter
+ elif reading_mention:
+ mention_buffer += letter
+ elif reading_text:
+ final_text += letter
+ else:
+ raise ValueError("Not sure at point", clean_text[index-2:index+2])
+
+ if open_read > 2:
+ reading_special_case = True
+
+ if open_read == 2 and reading_text:
+ reading_text = False
+ reading_entity = True
+ reading_mention = False
+
+ # we just finished reading an entity
+ if open_read == 0 and not reading_text:
+ if '#' in entity_buffer or entity_buffer.startswith(':'):
+ reading_special_case = True
+ # Ignore cases with nested structures like File: handles etc
+ if not reading_special_case:
+ if not mention_buffer:
+ mention_buffer = entity_buffer
+ start = len(final_text)
+ end = start + len(mention_buffer)
+ qid = wp_to_id.get(entity_buffer, None)
+ if qid:
+ _write_training_entity(outputfile=entityfile,
+ article_id=article_id,
+ alias=mention_buffer,
+ entity=qid,
+ start=start,
+ end=end)
+ found_entities = True
+ final_text += mention_buffer
+
+ entity_buffer = ""
+ mention_buffer = ""
+
+ reading_text = True
+ reading_entity = False
+ reading_mention = False
+ reading_special_case = False
+
+ if found_entities:
+ _write_training_article(article_id=article_id, clean_text=final_text, training_output=training_output)
+
+
+info_regex = re.compile(r'{[^{]*?}')
+htlm_regex = re.compile(r'<!--[^-]*-->')
+category_regex = re.compile(r'\[\[Category:[^\[]*]]')
+file_regex = re.compile(r'\[\[File:[^[\]]+]]')
+ref_regex = re.compile(r'<ref.*?>') # non-greedy
+ref_2_regex = re.compile(r'</ref.*?>') # non-greedy
+
+
+def _get_clean_wp_text(article_text):
+ clean_text = article_text.strip()
+
+ # remove bolding & italic markup
+ clean_text = clean_text.replace('\'\'\'', '')
+ clean_text = clean_text.replace('\'\'', '')
+
+ # remove nested {{info}} statements by removing the inner/smallest ones first and iterating
+ try_again = True
+ previous_length = len(clean_text)
+ while try_again:
+ clean_text = info_regex.sub('', clean_text) # non-greedy match excluding a nested {
+ if len(clean_text) < previous_length:
+ try_again = True
+ else:
+ try_again = False
+ previous_length = len(clean_text)
+
+ # remove HTML comments
+ clean_text = htlm_regex.sub('', clean_text)
+
+ # remove Category and File statements
+ clean_text = category_regex.sub('', clean_text)
+ clean_text = file_regex.sub('', clean_text)
+
+ # remove multiple =
+ while '==' in clean_text:
+ clean_text = clean_text.replace("==", "=")
+
+ clean_text = clean_text.replace(". =", ".")
+ clean_text = clean_text.replace(" = ", ". ")
+ clean_text = clean_text.replace("= ", ".")
+ clean_text = clean_text.replace(" =", "")
+
+ # remove refs (non-greedy match)
+ clean_text = ref_regex.sub('', clean_text)
+ clean_text = ref_2_regex.sub('', clean_text)
+
+ # remove additional wikiformatting
+ clean_text = re.sub(r'<blockquote>', '', clean_text)
+ clean_text = re.sub(r'</blockquote>', '', clean_text)
+
+ # change special characters back to normal ones
+ clean_text = clean_text.replace(r'<', '<')
+ clean_text = clean_text.replace(r'>', '>')
+ clean_text = clean_text.replace(r'"', '"')
+ clean_text = clean_text.replace(r'&nbsp;', ' ')
+ clean_text = clean_text.replace(r'&', '&')
+
+ # remove multiple spaces
+ while ' ' in clean_text:
+ clean_text = clean_text.replace(' ', ' ')
+
+ return clean_text.strip()
+
+
+def _write_training_article(article_id, clean_text, training_output):
+ file_loc = training_output / str(article_id) + ".txt"
+ with open(file_loc, mode='w', encoding='utf8') as outputfile:
+ outputfile.write(clean_text)
+
+
+def _write_training_entity(outputfile, article_id, alias, entity, start, end):
+ outputfile.write(article_id + "|" + alias + "|" + entity + "|" + str(start) + "|" + str(end) + "\n")
+
+
+def is_dev(article_id):
+ return article_id.endswith("3")
+
+
+def read_training(nlp, training_dir, dev, limit):
+ # This method provides training examples that correspond to the entity annotations found by the nlp object
+ entityfile_loc = training_dir / ENTITY_FILE
+ data = []
+
+ # assume the data is written sequentially, so we can reuse the article docs
+ current_article_id = None
+ current_doc = None
+ ents_by_offset = dict()
+ skip_articles = set()
+ total_entities = 0
+
+ with open(entityfile_loc, mode='r', encoding='utf8') as file:
+ for line in file:
+ if not limit or len(data) < limit:
+ fields = line.replace('\n', "").split(sep='|')
+ article_id = fields[0]
+ alias = fields[1]
+ wp_title = fields[2]
+ start = fields[3]
+ end = fields[4]
+
+ if dev == is_dev(article_id) and article_id != "article_id" and article_id not in skip_articles:
+ if not current_doc or (current_article_id != article_id):
+ # parse the new article text
+ file_name = article_id + ".txt"
+ try:
+ with open(os.path.join(training_dir, file_name), mode="r", encoding='utf8') as f:
+ text = f.read()
+ if len(text) < 30000: # threshold for convenience / speed of processing
+ current_doc = nlp(text)
+ current_article_id = article_id
+ ents_by_offset = dict()
+ for ent in current_doc.ents:
+ sent_length = len(ent.sent)
+ # custom filtering to avoid too long or too short sentences
+ if 5 < sent_length < 100:
+ ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent
+ else:
+ skip_articles.add(article_id)
+ current_doc = None
+ except Exception as e:
+ print("Problem parsing article", article_id, e)
+ skip_articles.add(article_id)
+ raise e
+
+ # repeat checking this condition in case an exception was thrown
+ if current_doc and (current_article_id == article_id):
+ found_ent = ents_by_offset.get(start + "_" + end, None)
+ if found_ent:
+ if found_ent.text != alias:
+ skip_articles.add(article_id)
+ current_doc = None
+ else:
+ sent = found_ent.sent.as_doc()
+ # currently feeding the gold data one entity per sentence at a time
+ gold_start = int(start) - found_ent.sent.start_char
+ gold_end = int(end) - found_ent.sent.start_char
+ gold_entities = [(gold_start, gold_end, wp_title)]
+ gold = GoldParse(doc=sent, links=gold_entities)
+ data.append((sent, gold))
+ total_entities += 1
+ if len(data) % 2500 == 0:
+ print(" -read", total_entities, "entities")
+
+ print(" -read", total_entities, "entities")
+ return data
diff --git a/bin/wiki_entity_linking/wikidata_processor.py b/bin/wiki_entity_linking/wikidata_processor.py
new file mode 100644
index 000000000..a32a0769a
--- /dev/null
+++ b/bin/wiki_entity_linking/wikidata_processor.py
@@ -0,0 +1,119 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import bz2
+import json
+import datetime
+
+
+def read_wikidata_entities_json(wikidata_file, limit=None, to_print=False):
+ # Read the JSON wiki data and parse out the entities. Takes about 7u30 to parse 55M lines.
+ # get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
+
+ lang = 'en'
+ site_filter = 'enwiki'
+
+ # properties filter (currently disabled to get ALL data)
+ prop_filter = dict()
+ # prop_filter = {'P31': {'Q5', 'Q15632617'}} # currently defined as OR: one property suffices to be selected
+
+ title_to_id = dict()
+ id_to_descr = dict()
+
+ # parse appropriate fields - depending on what we need in the KB
+ parse_properties = False
+ parse_sitelinks = True
+ parse_labels = False
+ parse_descriptions = True
+ parse_aliases = False
+ parse_claims = False
+
+ with bz2.open(wikidata_file, mode='rb') as file:
+ line = file.readline()
+ cnt = 0
+ while line and (not limit or cnt < limit):
+ if cnt % 500000 == 0:
+ print(datetime.datetime.now(), "processed", cnt, "lines of WikiData dump")
+ clean_line = line.strip()
+ if clean_line.endswith(b","):
+ clean_line = clean_line[:-1]
+ if len(clean_line) > 1:
+ obj = json.loads(clean_line)
+ entry_type = obj["type"]
+
+ if entry_type == "item":
+ # filtering records on their properties (currently disabled to get ALL data)
+ # keep = False
+ keep = True
+
+ claims = obj["claims"]
+ if parse_claims:
+ for prop, value_set in prop_filter.items():
+ claim_property = claims.get(prop, None)
+ if claim_property:
+ for cp in claim_property:
+ cp_id = cp['mainsnak'].get('datavalue', {}).get('value', {}).get('id')
+ cp_rank = cp['rank']
+ if cp_rank != "deprecated" and cp_id in value_set:
+ keep = True
+
+ if keep:
+ unique_id = obj["id"]
+
+ if to_print:
+ print("ID:", unique_id)
+ print("type:", entry_type)
+
+ # parsing all properties that refer to other entities
+ if parse_properties:
+ for prop, claim_property in claims.items():
+ cp_dicts = [cp['mainsnak']['datavalue'].get('value') for cp in claim_property
+ if cp['mainsnak'].get('datavalue')]
+ cp_values = [cp_dict.get('id') for cp_dict in cp_dicts if isinstance(cp_dict, dict)
+ if cp_dict.get('id') is not None]
+ if cp_values:
+ if to_print:
+ print("prop:", prop, cp_values)
+
+ found_link = False
+ if parse_sitelinks:
+ site_value = obj["sitelinks"].get(site_filter, None)
+ if site_value:
+ site = site_value['title']
+ if to_print:
+ print(site_filter, ":", site)
+ title_to_id[site] = unique_id
+ found_link = True
+
+ if parse_labels:
+ labels = obj["labels"]
+ if labels:
+ lang_label = labels.get(lang, None)
+ if lang_label:
+ if to_print:
+ print("label (" + lang + "):", lang_label["value"])
+
+ if found_link and parse_descriptions:
+ descriptions = obj["descriptions"]
+ if descriptions:
+ lang_descr = descriptions.get(lang, None)
+ if lang_descr:
+ if to_print:
+ print("description (" + lang + "):", lang_descr["value"])
+ id_to_descr[unique_id] = lang_descr["value"]
+
+ if parse_aliases:
+ aliases = obj["aliases"]
+ if aliases:
+ lang_aliases = aliases.get(lang, None)
+ if lang_aliases:
+ for item in lang_aliases:
+ if to_print:
+ print("alias (" + lang + "):", item["value"])
+
+ if to_print:
+ print()
+ line = file.readline()
+ cnt += 1
+
+ return title_to_id, id_to_descr
diff --git a/bin/wiki_entity_linking/wikipedia_processor.py b/bin/wiki_entity_linking/wikipedia_processor.py
new file mode 100644
index 000000000..c02e472bc
--- /dev/null
+++ b/bin/wiki_entity_linking/wikipedia_processor.py
@@ -0,0 +1,182 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import re
+import bz2
+import csv
+import datetime
+
+"""
+Process a Wikipedia dump to calculate entity frequencies and prior probabilities in combination with certain mentions.
+Write these results to file for downstream KB and training data generation.
+"""
+
+map_alias_to_link = dict()
+
+# these will/should be matched ignoring case
+wiki_namespaces = ["b", "betawikiversity", "Book", "c", "Category", "Commons",
+ "d", "dbdump", "download", "Draft", "Education", "Foundation",
+ "Gadget", "Gadget definition", "gerrit", "File", "Help", "Image", "Incubator",
+ "m", "mail", "mailarchive", "media", "MediaWiki", "MediaWiki talk", "Mediawikiwiki",
+ "MediaZilla", "Meta", "Metawikipedia", "Module",
+ "mw", "n", "nost", "oldwikisource", "outreach", "outreachwiki", "otrs", "OTRSwiki",
+ "Portal", "phab", "Phabricator", "Project", "q", "quality", "rev",
+ "s", "spcom", "Special", "species", "Strategy", "sulutil", "svn",
+ "Talk", "Template", "Template talk", "Testwiki", "ticket", "TimedText", "Toollabs", "tools",
+ "tswiki", "User", "User talk", "v", "voy",
+ "w", "Wikibooks", "Wikidata", "wikiHow", "Wikinvest", "wikilivres", "Wikimedia", "Wikinews",
+ "Wikipedia", "Wikipedia talk", "Wikiquote", "Wikisource", "Wikispecies", "Wikitech",
+ "Wikiversity", "Wikivoyage", "wikt", "wiktionary", "wmf", "wmania", "WP"]
+
+# find the links
+link_regex = re.compile(r'\[\[[^\[\]]*\]\]')
+
+# match on interwiki links, e.g. `en:` or `:fr:`
+ns_regex = r":?" + "[a-z][a-z]" + ":"
+
+# match on Namespace: optionally preceded by a :
+for ns in wiki_namespaces:
+ ns_regex += "|" + ":?" + ns + ":"
+
+ns_regex = re.compile(ns_regex, re.IGNORECASE)
+
+
+def read_wikipedia_prior_probs(wikipedia_input, prior_prob_output):
+ """
+ Read the XML wikipedia data and parse out intra-wiki links to estimate prior probabilities.
+ The full file takes about 2h to parse 1100M lines.
+ It works relatively fast because it runs line by line, irrelevant of which article the intrawiki is from.
+ """
+ with bz2.open(wikipedia_input, mode='rb') as file:
+ line = file.readline()
+ cnt = 0
+ while line:
+ if cnt % 5000000 == 0:
+ print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
+ clean_line = line.strip().decode("utf-8")
+
+ aliases, entities, normalizations = get_wp_links(clean_line)
+ for alias, entity, norm in zip(aliases, entities, normalizations):
+ _store_alias(alias, entity, normalize_alias=norm, normalize_entity=True)
+ _store_alias(alias, entity, normalize_alias=norm, normalize_entity=True)
+
+ line = file.readline()
+ cnt += 1
+
+ # write all aliases and their entities and count occurrences to file
+ with open(prior_prob_output, mode='w', encoding='utf8') as outputfile:
+ outputfile.write("alias" + "|" + "count" + "|" + "entity" + "\n")
+ for alias, alias_dict in sorted(map_alias_to_link.items(), key=lambda x: x[0]):
+ for entity, count in sorted(alias_dict.items(), key=lambda x: x[1], reverse=True):
+ outputfile.write(alias + "|" + str(count) + "|" + entity + "\n")
+
+
+def _store_alias(alias, entity, normalize_alias=False, normalize_entity=True):
+ alias = alias.strip()
+ entity = entity.strip()
+
+ # remove everything after # as this is not part of the title but refers to a specific paragraph
+ if normalize_entity:
+ # wikipedia titles are always capitalized
+ entity = _capitalize_first(entity.split("#")[0])
+ if normalize_alias:
+ alias = alias.split("#")[0]
+
+ if alias and entity:
+ alias_dict = map_alias_to_link.get(alias, dict())
+ entity_count = alias_dict.get(entity, 0)
+ alias_dict[entity] = entity_count + 1
+ map_alias_to_link[alias] = alias_dict
+
+
+def get_wp_links(text):
+ aliases = []
+ entities = []
+ normalizations = []
+
+ matches = link_regex.findall(text)
+ for match in matches:
+ match = match[2:][:-2].replace("_", " ").strip()
+
+ if ns_regex.match(match):
+ pass # ignore namespaces at the beginning of the string
+
+ # this is a simple [[link]], with the alias the same as the mention
+ elif "|" not in match:
+ aliases.append(match)
+ entities.append(match)
+ normalizations.append(True)
+
+ # in wiki format, the link is written as [[entity|alias]]
+ else:
+ splits = match.split("|")
+ entity = splits[0].strip()
+ alias = splits[1].strip()
+ # specific wiki format [[alias (specification)|]]
+ if len(alias) == 0 and "(" in entity:
+ alias = entity.split("(")[0]
+ aliases.append(alias)
+ entities.append(entity)
+ normalizations.append(False)
+ else:
+ aliases.append(alias)
+ entities.append(entity)
+ normalizations.append(False)
+
+ return aliases, entities, normalizations
+
+
+def _capitalize_first(text):
+ if not text:
+ return None
+ result = text[0].capitalize()
+ if len(result) > 0:
+ result += text[1:]
+ return result
+
+
+def write_entity_counts(prior_prob_input, count_output, to_print=False):
+ # Write entity counts for quick access later
+ entity_to_count = dict()
+ total_count = 0
+
+ with open(prior_prob_input, mode='r', encoding='utf8') as prior_file:
+ # skip header
+ prior_file.readline()
+ line = prior_file.readline()
+
+ while line:
+ splits = line.replace('\n', "").split(sep='|')
+ # alias = splits[0]
+ count = int(splits[1])
+ entity = splits[2]
+
+ current_count = entity_to_count.get(entity, 0)
+ entity_to_count[entity] = current_count + count
+
+ total_count += count
+
+ line = prior_file.readline()
+
+ with open(count_output, mode='w', encoding='utf8') as entity_file:
+ entity_file.write("entity" + "|" + "count" + "\n")
+ for entity, count in entity_to_count.items():
+ entity_file.write(entity + "|" + str(count) + "\n")
+
+ if to_print:
+ for entity, count in entity_to_count.items():
+ print("Entity count:", entity, count)
+ print("Total count:", total_count)
+
+
+def get_all_frequencies(count_input):
+ entity_to_count = dict()
+ with open(count_input, 'r', encoding='utf8') as csvfile:
+ csvreader = csv.reader(csvfile, delimiter='|')
+ # skip header
+ next(csvreader)
+ for row in csvreader:
+ entity_to_count[row[0]] = int(row[1])
+
+ return entity_to_count
+
diff --git a/examples/pipeline/dummy_entity_linking.py b/examples/pipeline/dummy_entity_linking.py
index 88415d040..0e59db304 100644
--- a/examples/pipeline/dummy_entity_linking.py
+++ b/examples/pipeline/dummy_entity_linking.py
@@ -9,26 +9,26 @@ from spacy.kb import KnowledgeBase
def create_kb(vocab):
- kb = KnowledgeBase(vocab=vocab)
+ kb = KnowledgeBase(vocab=vocab, entity_vector_length=1)
# adding entities
entity_0 = "Q1004791_Douglas"
print("adding entity", entity_0)
- kb.add_entity(entity=entity_0, prob=0.5)
+ kb.add_entity(entity=entity_0, prob=0.5, entity_vector=[0])
entity_1 = "Q42_Douglas_Adams"
print("adding entity", entity_1)
- kb.add_entity(entity=entity_1, prob=0.5)
+ kb.add_entity(entity=entity_1, prob=0.5, entity_vector=[1])
entity_2 = "Q5301561_Douglas_Haig"
print("adding entity", entity_2)
- kb.add_entity(entity=entity_2, prob=0.5)
+ kb.add_entity(entity=entity_2, prob=0.5, entity_vector=[2])
# adding aliases
print()
alias_0 = "Douglas"
print("adding alias", alias_0)
- kb.add_alias(alias=alias_0, entities=[entity_0, entity_1, entity_2], probabilities=[0.1, 0.6, 0.2])
+ kb.add_alias(alias=alias_0, entities=[entity_0, entity_1, entity_2], probabilities=[0.6, 0.1, 0.2])
alias_1 = "Douglas Adams"
print("adding alias", alias_1)
@@ -41,8 +41,12 @@ def create_kb(vocab):
def add_el(kb, nlp):
- el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": kb})
+ el_pipe = nlp.create_pipe(name='entity_linker', config={"context_width": 64})
+ el_pipe.set_kb(kb)
nlp.add_pipe(el_pipe, last=True)
+ nlp.begin_training()
+ el_pipe.context_weight = 0
+ el_pipe.prior_weight = 1
for alias in ["Douglas Adams", "Douglas"]:
candidates = nlp.linker.kb.get_candidates(alias)
@@ -66,6 +70,6 @@ def add_el(kb, nlp):
if __name__ == "__main__":
- nlp = spacy.load('en_core_web_sm')
- my_kb = create_kb(nlp.vocab)
- add_el(my_kb, nlp)
+ my_nlp = spacy.load('en_core_web_sm')
+ my_kb = create_kb(my_nlp.vocab)
+ add_el(my_kb, my_nlp)
diff --git a/examples/pipeline/wikidata_entity_linking.py b/examples/pipeline/wikidata_entity_linking.py
new file mode 100644
index 000000000..17c2976dd
--- /dev/null
+++ b/examples/pipeline/wikidata_entity_linking.py
@@ -0,0 +1,442 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import random
+import datetime
+from pathlib import Path
+
+from bin.wiki_entity_linking import training_set_creator, kb_creator, wikipedia_processor as wp
+from bin.wiki_entity_linking.kb_creator import DESC_WIDTH
+
+import spacy
+from spacy.kb import KnowledgeBase
+from spacy.util import minibatch, compounding
+
+"""
+Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm.
+"""
+
+ROOT_DIR = Path("C:/Users/Sofie/Documents/data/")
+OUTPUT_DIR = ROOT_DIR / 'wikipedia'
+TRAINING_DIR = OUTPUT_DIR / 'training_data_nel'
+
+PRIOR_PROB = OUTPUT_DIR / 'prior_prob.csv'
+ENTITY_COUNTS = OUTPUT_DIR / 'entity_freq.csv'
+ENTITY_DEFS = OUTPUT_DIR / 'entity_defs.csv'
+ENTITY_DESCR = OUTPUT_DIR / 'entity_descriptions.csv'
+
+KB_FILE = OUTPUT_DIR / 'kb_1' / 'kb'
+NLP_1_DIR = OUTPUT_DIR / 'nlp_1'
+NLP_2_DIR = OUTPUT_DIR / 'nlp_2'
+
+# get latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/
+WIKIDATA_JSON = ROOT_DIR / 'wikidata' / 'wikidata-20190304-all.json.bz2'
+
+# get enwiki-latest-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/enwiki/latest/
+ENWIKI_DUMP = ROOT_DIR / 'wikipedia' / 'enwiki-20190320-pages-articles-multistream.xml.bz2'
+
+# KB construction parameters
+MAX_CANDIDATES = 10
+MIN_ENTITY_FREQ = 20
+MIN_PAIR_OCC = 5
+
+# model training parameters
+EPOCHS = 10
+DROPOUT = 0.5
+LEARN_RATE = 0.005
+L2 = 1e-6
+CONTEXT_WIDTH = 128
+
+
+def run_pipeline():
+ # set the appropriate booleans to define which parts of the pipeline should be re(run)
+ print("START", datetime.datetime.now())
+ print()
+ nlp_1 = spacy.load('en_core_web_lg')
+ nlp_2 = None
+ kb_2 = None
+
+ # one-time methods to create KB and write to file
+ to_create_prior_probs = False
+ to_create_entity_counts = False
+ to_create_kb = False
+
+ # read KB back in from file
+ to_read_kb = True
+ to_test_kb = False
+
+ # create training dataset
+ create_wp_training = False
+
+ # train the EL pipe
+ train_pipe = True
+ measure_performance = True
+
+ # test the EL pipe on a simple example
+ to_test_pipeline = True
+
+ # write the NLP object, read back in and test again
+ to_write_nlp = True
+ to_read_nlp = True
+ test_from_file = False
+
+ # STEP 1 : create prior probabilities from WP (run only once)
+ if to_create_prior_probs:
+ print("STEP 1: to_create_prior_probs", datetime.datetime.now())
+ wp.read_wikipedia_prior_probs(wikipedia_input=ENWIKI_DUMP, prior_prob_output=PRIOR_PROB)
+ print()
+
+ # STEP 2 : deduce entity frequencies from WP (run only once)
+ if to_create_entity_counts:
+ print("STEP 2: to_create_entity_counts", datetime.datetime.now())
+ wp.write_entity_counts(prior_prob_input=PRIOR_PROB, count_output=ENTITY_COUNTS, to_print=False)
+ print()
+
+ # STEP 3 : create KB and write to file (run only once)
+ if to_create_kb:
+ print("STEP 3a: to_create_kb", datetime.datetime.now())
+ kb_1 = kb_creator.create_kb(nlp_1,
+ max_entities_per_alias=MAX_CANDIDATES,
+ min_entity_freq=MIN_ENTITY_FREQ,
+ min_occ=MIN_PAIR_OCC,
+ entity_def_output=ENTITY_DEFS,
+ entity_descr_output=ENTITY_DESCR,
+ count_input=ENTITY_COUNTS,
+ prior_prob_input=PRIOR_PROB,
+ wikidata_input=WIKIDATA_JSON)
+ print("kb entities:", kb_1.get_size_entities())
+ print("kb aliases:", kb_1.get_size_aliases())
+ print()
+
+ print("STEP 3b: write KB and NLP", datetime.datetime.now())
+ kb_1.dump(KB_FILE)
+ nlp_1.to_disk(NLP_1_DIR)
+ print()
+
+ # STEP 4 : read KB back in from file
+ if to_read_kb:
+ print("STEP 4: to_read_kb", datetime.datetime.now())
+ nlp_2 = spacy.load(NLP_1_DIR)
+ kb_2 = KnowledgeBase(vocab=nlp_2.vocab, entity_vector_length=DESC_WIDTH)
+ kb_2.load_bulk(KB_FILE)
+ print("kb entities:", kb_2.get_size_entities())
+ print("kb aliases:", kb_2.get_size_aliases())
+ print()
+
+ # test KB
+ if to_test_kb:
+ check_kb(kb_2)
+ print()
+
+ # STEP 5: create a training dataset from WP
+ if create_wp_training:
+ print("STEP 5: create training dataset", datetime.datetime.now())
+ training_set_creator.create_training(wikipedia_input=ENWIKI_DUMP,
+ entity_def_input=ENTITY_DEFS,
+ training_output=TRAINING_DIR)
+
+ # STEP 6: create and train the entity linking pipe
+ if train_pipe:
+ print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
+ type_to_int = {label: i for i, label in enumerate(nlp_2.entity.labels)}
+ print(" -analysing", len(type_to_int), "different entity types")
+ el_pipe = nlp_2.create_pipe(name='entity_linker',
+ config={"context_width": CONTEXT_WIDTH,
+ "pretrained_vectors": nlp_2.vocab.vectors.name,
+ "type_to_int": type_to_int})
+ el_pipe.set_kb(kb_2)
+ nlp_2.add_pipe(el_pipe, last=True)
+
+ other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"]
+ with nlp_2.disable_pipes(*other_pipes): # only train Entity Linking
+ optimizer = nlp_2.begin_training()
+ optimizer.learn_rate = LEARN_RATE
+ optimizer.L2 = L2
+
+ # define the size (nr of entities) of training and dev set
+ train_limit = 5000
+ dev_limit = 5000
+
+ train_data = training_set_creator.read_training(nlp=nlp_2,
+ training_dir=TRAINING_DIR,
+ dev=False,
+ limit=train_limit)
+
+ print("Training on", len(train_data), "articles")
+ print()
+
+ dev_data = training_set_creator.read_training(nlp=nlp_2,
+ training_dir=TRAINING_DIR,
+ dev=True,
+ limit=dev_limit)
+
+ print("Dev testing on", len(dev_data), "articles")
+ print()
+
+ if not train_data:
+ print("Did not find any training data")
+ else:
+ for itn in range(EPOCHS):
+ random.shuffle(train_data)
+ losses = {}
+ batches = minibatch(train_data, size=compounding(4.0, 128.0, 1.001))
+ batchnr = 0
+
+ with nlp_2.disable_pipes(*other_pipes):
+ for batch in batches:
+ try:
+ docs, golds = zip(*batch)
+ nlp_2.update(
+ docs,
+ golds,
+ sgd=optimizer,
+ drop=DROPOUT,
+ losses=losses,
+ )
+ batchnr += 1
+ except Exception as e:
+ print("Error updating batch:", e)
+
+ if batchnr > 0:
+ el_pipe.cfg["context_weight"] = 1
+ el_pipe.cfg["prior_weight"] = 1
+ dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
+ losses['entity_linker'] = losses['entity_linker'] / batchnr
+ print("Epoch, train loss", itn, round(losses['entity_linker'], 2),
+ " / dev acc avg", round(dev_acc_context, 3))
+
+ # STEP 7: measure the performance of our trained pipe on an independent dev set
+ if len(dev_data) and measure_performance:
+ print()
+ print("STEP 7: performance measurement of Entity Linking pipe", datetime.datetime.now())
+ print()
+
+ counts, acc_r, acc_r_label, acc_p, acc_p_label, acc_o, acc_o_label = _measure_baselines(dev_data, kb_2)
+ print("dev counts:", sorted(counts.items(), key=lambda x: x[0]))
+ print("dev acc oracle:", round(acc_o, 3), [(x, round(y, 3)) for x, y in acc_o_label.items()])
+ print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_label.items()])
+ print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_label.items()])
+
+ # using only context
+ el_pipe.cfg["context_weight"] = 1
+ el_pipe.cfg["prior_weight"] = 0
+ dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
+ print("dev acc context avg:", round(dev_acc_context, 3),
+ [(x, round(y, 3)) for x, y in dev_acc_context_dict.items()])
+
+ # measuring combined accuracy (prior + context)
+ el_pipe.cfg["context_weight"] = 1
+ el_pipe.cfg["prior_weight"] = 1
+ dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe, error_analysis=False)
+ print("dev acc combo avg:", round(dev_acc_combo, 3),
+ [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
+
+ # STEP 8: apply the EL pipe on a toy example
+ if to_test_pipeline:
+ print()
+ print("STEP 8: applying Entity Linking to toy example", datetime.datetime.now())
+ print()
+ run_el_toy_example(nlp=nlp_2)
+
+ # STEP 9: write the NLP pipeline (including entity linker) to file
+ if to_write_nlp:
+ print()
+ print("STEP 9: testing NLP IO", datetime.datetime.now())
+ print()
+ print("writing to", NLP_2_DIR)
+ nlp_2.to_disk(NLP_2_DIR)
+ print()
+
+ # verify that the IO has gone correctly
+ if to_read_nlp:
+ print("reading from", NLP_2_DIR)
+ nlp_3 = spacy.load(NLP_2_DIR)
+
+ print("running toy example with NLP 3")
+ run_el_toy_example(nlp=nlp_3)
+
+ # testing performance with an NLP model from file
+ if test_from_file:
+ nlp_2 = spacy.load(NLP_1_DIR)
+ nlp_3 = spacy.load(NLP_2_DIR)
+ el_pipe = nlp_3.get_pipe("entity_linker")
+
+ dev_limit = 5000
+ dev_data = training_set_creator.read_training(nlp=nlp_2,
+ training_dir=TRAINING_DIR,
+ dev=True,
+ limit=dev_limit)
+
+ print("Dev testing from file on", len(dev_data), "articles")
+ print()
+
+ dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe=el_pipe, error_analysis=False)
+ print("dev acc combo avg:", round(dev_acc_combo, 3),
+ [(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
+
+ print()
+ print("STOP", datetime.datetime.now())
+
+
+def _measure_accuracy(data, el_pipe=None, error_analysis=False):
+ # If the docs in the data require further processing with an entity linker, set el_pipe
+ correct_by_label = dict()
+ incorrect_by_label = dict()
+
+ docs = [d for d, g in data if len(d) > 0]
+ if el_pipe is not None:
+ docs = list(el_pipe.pipe(docs))
+ golds = [g for d, g in data if len(d) > 0]
+
+ for doc, gold in zip(docs, golds):
+ try:
+ correct_entries_per_article = dict()
+ for entity in gold.links:
+ start, end, gold_kb = entity
+ correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
+
+ for ent in doc.ents:
+ ent_label = ent.label_
+ pred_entity = ent.kb_id_
+ start = ent.start_char
+ end = ent.end_char
+ gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
+ # the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
+ if gold_entity is not None:
+ if gold_entity == pred_entity:
+ correct = correct_by_label.get(ent_label, 0)
+ correct_by_label[ent_label] = correct + 1
+ else:
+ incorrect = incorrect_by_label.get(ent_label, 0)
+ incorrect_by_label[ent_label] = incorrect + 1
+ if error_analysis:
+ print(ent.text, "in", doc)
+ print("Predicted", pred_entity, "should have been", gold_entity)
+ print()
+
+ except Exception as e:
+ print("Error assessing accuracy", e)
+
+ acc, acc_by_label = calculate_acc(correct_by_label, incorrect_by_label)
+ return acc, acc_by_label
+
+
+def _measure_baselines(data, kb):
+ # Measure 3 performance baselines: random selection, prior probabilities, and 'oracle' prediction for upper bound
+ counts_by_label = dict()
+
+ random_correct_by_label = dict()
+ random_incorrect_by_label = dict()
+
+ oracle_correct_by_label = dict()
+ oracle_incorrect_by_label = dict()
+
+ prior_correct_by_label = dict()
+ prior_incorrect_by_label = dict()
+
+ docs = [d for d, g in data if len(d) > 0]
+ golds = [g for d, g in data if len(d) > 0]
+
+ for doc, gold in zip(docs, golds):
+ try:
+ correct_entries_per_article = dict()
+ for entity in gold.links:
+ start, end, gold_kb = entity
+ correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
+
+ for ent in doc.ents:
+ ent_label = ent.label_
+ start = ent.start_char
+ end = ent.end_char
+ gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
+
+ # the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
+ if gold_entity is not None:
+ counts_by_label[ent_label] = counts_by_label.get(ent_label, 0) + 1
+ candidates = kb.get_candidates(ent.text)
+ oracle_candidate = ""
+ best_candidate = ""
+ random_candidate = ""
+ if candidates:
+ scores = []
+
+ for c in candidates:
+ scores.append(c.prior_prob)
+ if c.entity_ == gold_entity:
+ oracle_candidate = c.entity_
+
+ best_index = scores.index(max(scores))
+ best_candidate = candidates[best_index].entity_
+ random_candidate = random.choice(candidates).entity_
+
+ if gold_entity == best_candidate:
+ prior_correct_by_label[ent_label] = prior_correct_by_label.get(ent_label, 0) + 1
+ else:
+ prior_incorrect_by_label[ent_label] = prior_incorrect_by_label.get(ent_label, 0) + 1
+
+ if gold_entity == random_candidate:
+ random_correct_by_label[ent_label] = random_correct_by_label.get(ent_label, 0) + 1
+ else:
+ random_incorrect_by_label[ent_label] = random_incorrect_by_label.get(ent_label, 0) + 1
+
+ if gold_entity == oracle_candidate:
+ oracle_correct_by_label[ent_label] = oracle_correct_by_label.get(ent_label, 0) + 1
+ else:
+ oracle_incorrect_by_label[ent_label] = oracle_incorrect_by_label.get(ent_label, 0) + 1
+
+ except Exception as e:
+ print("Error assessing accuracy", e)
+
+ acc_prior, acc_prior_by_label = calculate_acc(prior_correct_by_label, prior_incorrect_by_label)
+ acc_rand, acc_rand_by_label = calculate_acc(random_correct_by_label, random_incorrect_by_label)
+ acc_oracle, acc_oracle_by_label = calculate_acc(oracle_correct_by_label, oracle_incorrect_by_label)
+
+ return counts_by_label, acc_rand, acc_rand_by_label, acc_prior, acc_prior_by_label, acc_oracle, acc_oracle_by_label
+
+
+def calculate_acc(correct_by_label, incorrect_by_label):
+ acc_by_label = dict()
+ total_correct = 0
+ total_incorrect = 0
+ all_keys = set()
+ all_keys.update(correct_by_label.keys())
+ all_keys.update(incorrect_by_label.keys())
+ for label in sorted(all_keys):
+ correct = correct_by_label.get(label, 0)
+ incorrect = incorrect_by_label.get(label, 0)
+ total_correct += correct
+ total_incorrect += incorrect
+ if correct == incorrect == 0:
+ acc_by_label[label] = 0
+ else:
+ acc_by_label[label] = correct / (correct + incorrect)
+ acc = 0
+ if not (total_correct == total_incorrect == 0):
+ acc = total_correct / (total_correct + total_incorrect)
+ return acc, acc_by_label
+
+
+def check_kb(kb):
+ for mention in ("Bush", "Douglas Adams", "Homer", "Brazil", "China"):
+ candidates = kb.get_candidates(mention)
+
+ print("generating candidates for " + mention + " :")
+ for c in candidates:
+ print(" ", c.prior_prob, c.alias_, "-->", c.entity_ + " (freq=" + str(c.entity_freq) + ")")
+ print()
+
+
+def run_el_toy_example(nlp):
+ text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
+ "Douglas reminds us to always bring our towel, even in China or Brazil. " \
+ "The main character in Doug's novel is the man Arthur Dent, " \
+ "but Douglas doesn't write about George Washington or Homer Simpson."
+ doc = nlp(text)
+ print(text)
+ for ent in doc.ents:
+ print(" ent", ent.text, ent.label_, ent.kb_id_)
+ print()
+
+
+if __name__ == "__main__":
+ run_pipeline()
diff --git a/pyproject.toml b/pyproject.toml
index 80bb5905a..35f3d9215 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,6 +5,6 @@ requires = ["setuptools",
"cymem>=2.0.2,<2.1.0",
"preshed>=2.0.1,<2.1.0",
"murmurhash>=0.28.0,<1.1.0",
- "thinc==7.0.0.dev6",
+ "thinc>=7.0.8,<7.1.0",
]
build-backend = "setuptools.build_meta"
diff --git a/requirements.txt b/requirements.txt
index 8cc52dfe4..5a6870cd3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=2.0.1,<2.1.0
-thinc>=7.0.2,<7.1.0
+thinc>=7.0.8,<7.1.0
blis>=0.2.2,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.2.0,<1.1.0
diff --git a/setup.py b/setup.py
index 33623588c..b36c48316 100755
--- a/setup.py
+++ b/setup.py
@@ -228,7 +228,7 @@ def setup_package():
"murmurhash>=0.28.0,<1.1.0",
"cymem>=2.0.2,<2.1.0",
"preshed>=2.0.1,<2.1.0",
- "thinc>=7.0.2,<7.1.0",
+ "thinc>=7.0.8,<7.1.0",
"blis>=0.2.2,<0.3.0",
"plac<1.0.0,>=0.9.6",
"requests>=2.13.0,<3.0.0",
@@ -246,6 +246,7 @@ def setup_package():
"cuda100": ["thinc_gpu_ops>=0.0.1,<0.1.0", "cupy-cuda100>=5.0.0b4"],
# Language tokenizers with external dependencies
"ja": ["mecab-python3==0.7"],
+ "ko": ["natto-py==0.9.0"],
},
python_requires=">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*",
classifiers=[
diff --git a/spacy/_ml.py b/spacy/_ml.py
index 349b88df9..4d9bb4c2b 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -24,7 +24,7 @@ from thinc.neural._classes.affine import _set_dimensions_if_needed
import thinc.extra.load_nlp
from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE
-from .errors import Errors
+from .errors import Errors, user_warning, Warnings
from . import util
try:
@@ -299,7 +299,17 @@ def link_vectors_to_models(vocab):
data = ops.asarray(vectors.data)
# Set an entry here, so that vectors are accessed by StaticVectors
# (unideal, I know)
- thinc.extra.load_nlp.VECTORS[(ops.device, vectors.name)] = data
+ key = (ops.device, vectors.name)
+ if key in thinc.extra.load_nlp.VECTORS:
+ if thinc.extra.load_nlp.VECTORS[key].shape != data.shape:
+ # 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]
+ user_warning(Warnings.W019.format(old=old_name, new=new_name))
+ vectors.name = new_name
+ key = (ops.device, vectors.name)
+ thinc.extra.load_nlp.VECTORS[key] = data
def PyTorchBiLSTM(nO, nI, depth, dropout=0.2):
@@ -652,6 +662,51 @@ def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=False,
return model
+def build_nel_encoder(embed_width, hidden_width, ner_types, **cfg):
+ # TODO proper error
+ if "entity_width" not in cfg:
+ raise ValueError("entity_width not found")
+ if "context_width" not in cfg:
+ raise ValueError("context_width not found")
+
+ conv_depth = cfg.get("conv_depth", 2)
+ cnn_maxout_pieces = cfg.get("cnn_maxout_pieces", 3)
+ pretrained_vectors = cfg.get("pretrained_vectors") # self.nlp.vocab.vectors.name
+ context_width = cfg.get("context_width")
+ entity_width = cfg.get("entity_width")
+
+ with Model.define_operators({">>": chain, "**": clone}):
+ model = (
+ Affine(entity_width, entity_width + context_width + 1 + ner_types)
+ >> Affine(1, entity_width, drop_factor=0.0)
+ >> logistic
+ )
+
+ # context encoder
+ tok2vec = (
+ Tok2Vec(
+ width=hidden_width,
+ embed_size=embed_width,
+ pretrained_vectors=pretrained_vectors,
+ cnn_maxout_pieces=cnn_maxout_pieces,
+ subword_features=True,
+ conv_depth=conv_depth,
+ bilstm_depth=0,
+ )
+ >> flatten_add_lengths
+ >> Pooling(mean_pool)
+ >> Residual(zero_init(Maxout(hidden_width, hidden_width)))
+ >> zero_init(Affine(context_width, hidden_width))
+ )
+
+ model.tok2vec = tok2vec
+
+ model.tok2vec = tok2vec
+ model.tok2vec.nO = context_width
+ model.nO = 1
+ return model
+
+
@layerize
def flatten(seqs, drop=0.0):
ops = Model.ops
diff --git a/spacy/about.py b/spacy/about.py
index 5e7093606..16e5e9522 100644
--- a/spacy/about.py
+++ b/spacy/about.py
@@ -4,13 +4,13 @@
# fmt: off
__title__ = "spacy"
-__version__ = "2.1.4"
+__version__ = "2.1.6"
__summary__ = "Industrial-strength Natural Language Processing (NLP) with Python and Cython"
__uri__ = "https://spacy.io"
__author__ = "Explosion AI"
__email__ = "contact@explosion.ai"
__license__ = "MIT"
-__release__ = False
+__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
diff --git a/spacy/attrs.pxd b/spacy/attrs.pxd
index 79a177ba9..d9aca078c 100644
--- a/spacy/attrs.pxd
+++ b/spacy/attrs.pxd
@@ -1,4 +1,6 @@
# Reserve 64 values for flag features
+from . cimport symbols
+
cdef enum attr_id_t:
NULL_ATTR
IS_ALPHA
@@ -88,3 +90,4 @@ cdef enum attr_id_t:
PROB
LANG
+ ENT_KB_ID = symbols.ENT_KB_ID
diff --git a/spacy/attrs.pyx b/spacy/attrs.pyx
index ed1f39a3f..8eeea363f 100644
--- a/spacy/attrs.pyx
+++ b/spacy/attrs.pyx
@@ -84,6 +84,7 @@ IDS = {
"DEP": DEP,
"ENT_IOB": ENT_IOB,
"ENT_TYPE": ENT_TYPE,
+ "ENT_KB_ID": ENT_KB_ID,
"HEAD": HEAD,
"SENT_START": SENT_START,
"SPACY": SPACY,
diff --git a/spacy/cli/pretrain.py b/spacy/cli/pretrain.py
index 2fe5b247a..57c26fcbd 100644
--- a/spacy/cli/pretrain.py
+++ b/spacy/cli/pretrain.py
@@ -5,6 +5,7 @@ import plac
import random
import numpy
import time
+import re
from collections import Counter
from pathlib import Path
from thinc.v2v import Affine, Maxout
@@ -65,6 +66,13 @@ from .train import _load_pretrained_tok2vec
"t2v",
Path,
),
+ epoch_start=(
+ "The epoch to start counting at. Only relevant when using '--init-tok2vec' and the given weight file has been "
+ "renamed. Prevents unintended overwriting of existing weight files.",
+ "option",
+ "es",
+ int
+ ),
)
def pretrain(
texts_loc,
@@ -83,6 +91,7 @@ def pretrain(
seed=0,
n_save_every=None,
init_tok2vec=None,
+ epoch_start=None,
):
"""
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
@@ -151,9 +160,29 @@ def pretrain(
if init_tok2vec is not None:
components = _load_pretrained_tok2vec(nlp, init_tok2vec)
msg.text("Loaded pretrained tok2vec for: {}".format(components))
+ # Parse the epoch number from the given weight file
+ model_name = re.search(r"model\d+\.bin", str(init_tok2vec))
+ if model_name:
+ # Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
+ epoch_start = int(model_name.group(0)[5:][:-4]) + 1
+ else:
+ if not epoch_start:
+ msg.fail(
+ "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,
+ exits=True
+ )
+ else:
+ # Without '--init-tok2vec' the '--epoch-start' argument is ignored
+ epoch_start = 0
+
optimizer = create_default_optimizer(model.ops)
tracker = ProgressTracker(frequency=10000)
- msg.divider("Pre-training tok2vec layer")
+ msg.divider("Pre-training tok2vec layer - starting at epoch %d" % 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)
@@ -174,7 +203,7 @@ def pretrain(
file_.write(srsly.json_dumps(log) + "\n")
skip_counter = 0
- for epoch in range(n_iter):
+ for epoch in range(epoch_start, n_iter + epoch_start):
for batch_id, batch in enumerate(
util.minibatch_by_words(((text, None) for text in texts), size=batch_size)
):
@@ -272,7 +301,7 @@ def get_vectors_loss(ops, docs, prediction, objective="L2"):
elif objective == "cosine":
loss, d_target = get_cossim_loss(prediction, target)
else:
- raise ValueError(Errors.E139.format(loss_func=objective))
+ raise ValueError(Errors.E142.format(loss_func=objective))
return loss, d_target
diff --git a/spacy/errors.py b/spacy/errors.py
index 176003e79..ed3d6afb9 100644
--- a/spacy/errors.py
+++ b/spacy/errors.py
@@ -82,6 +82,8 @@ class Warnings(object):
"parallel inference via multiprocessing.")
W017 = ("Alias '{alias}' already exists in the Knowledge base.")
W018 = ("Entity '{entity}' already exists in the Knowledge base.")
+ W019 = ("Changing vectors name from {old} to {new}, to avoid clash with "
+ "previously loaded vectors. See Issue #3853.")
@add_codes
@@ -399,7 +401,11 @@ class Errors(object):
E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input includes either the "
"`text` or `tokens` key. For more info, see the docs:\n"
"https://spacy.io/api/cli#pretrain-jsonl")
- E139 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
+ E139 = ("Knowledge base for component '{name}' not initialized. Did you forget to call set_kb()?")
+ E140 = ("The list of entities, prior probabilities and entity vectors should be of equal length.")
+ E141 = ("Entity vectors should be of length {required} instead of the provided {found}.")
+ E142 = ("Unsupported loss_function '{loss_func}'. Use either 'L2' or 'cosine'")
+ E143 = ("Labels for component '{name}' not initialized. Did you forget to call add_label()?")
@add_codes
diff --git a/spacy/gold.pxd b/spacy/gold.pxd
index a1550b1ef..8943a155a 100644
--- a/spacy/gold.pxd
+++ b/spacy/gold.pxd
@@ -31,6 +31,7 @@ cdef class GoldParse:
cdef public list ents
cdef public dict brackets
cdef public object cats
+ cdef public list links
cdef readonly list cand_to_gold
cdef readonly list gold_to_cand
diff --git a/spacy/gold.pyx b/spacy/gold.pyx
index 569979a5f..4fb22f3f0 100644
--- a/spacy/gold.pyx
+++ b/spacy/gold.pyx
@@ -427,7 +427,7 @@ cdef class GoldParse:
def __init__(self, doc, annot_tuples=None, words=None, tags=None,
heads=None, deps=None, entities=None, make_projective=False,
- cats=None, **_):
+ cats=None, links=None, **_):
"""Create a GoldParse.
doc (Doc): The document the annotations refer to.
@@ -450,6 +450,8 @@ cdef class GoldParse:
examples of a label to have the value 0.0. Labels not in the
dictionary are treated as missing - the gradient for those labels
will be zero.
+ links (iterable): A sequence of `(start_char, end_char, kb_id)` tuples,
+ representing the external ID of an entity in a knowledge base.
RETURNS (GoldParse): The newly constructed object.
"""
if words is None:
@@ -485,6 +487,7 @@ cdef class GoldParse:
self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
self.cats = {} if cats is None else dict(cats)
+ self.links = links
self.words = [None] * len(doc)
self.tags = [None] * len(doc)
self.heads = [None] * len(doc)
diff --git a/spacy/kb.pxd b/spacy/kb.pxd
index e34a0a9ba..40b22b275 100644
--- a/spacy/kb.pxd
+++ b/spacy/kb.pxd
@@ -1,53 +1,27 @@
"""Knowledge-base for entity or concept linking."""
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
+
from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int64_t
+from libc.stdio cimport FILE
from spacy.vocab cimport Vocab
from .typedefs cimport hash_t
-
-# Internal struct, for storage and disambiguation. This isn't what we return
-# to the user as the answer to "here's your entity". It's the minimum number
-# of bits we need to keep track of the answers.
-cdef struct _EntryC:
-
- # The hash of this entry's unique ID and name in the kB
- hash_t entity_hash
-
- # Allows retrieval of one or more vectors.
- # Each element of vector_rows should be an index into a vectors table.
- # Every entry should have the same number of vectors, so we can avoid storing
- # the number of vectors in each knowledge-base struct
- int32_t* vector_rows
-
- # Allows retrieval of a struct of non-vector features. We could make this a
- # pointer, but we have 32 bits left over in the struct after prob, so we'd
- # like this to only be 32 bits. We can also set this to -1, for the common
- # case where there are no features.
- int32_t feats_row
-
- # log probability of entity, based on corpus frequency
- float prob
-
-
-# Each alias struct stores a list of Entry pointers with their prior probabilities
-# for this specific mention/alias.
-cdef struct _AliasC:
-
- # All entry candidates for this alias
- vector[int64_t] entry_indices
-
- # Prior probability P(entity|alias) - should sum up to (at most) 1.
- vector[float] probs
+from .structs cimport KBEntryC, AliasC
+ctypedef vector[KBEntryC] entry_vec
+ctypedef vector[AliasC] alias_vec
+ctypedef vector[float] float_vec
+ctypedef vector[float_vec] float_matrix
# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
cdef class Candidate:
-
cdef readonly KnowledgeBase kb
cdef hash_t entity_hash
+ cdef float entity_freq
+ cdef vector[float] entity_vector
cdef hash_t alias_hash
cdef float prior_prob
@@ -55,9 +29,10 @@ cdef class Candidate:
cdef class KnowledgeBase:
cdef Pool mem
cpdef readonly Vocab vocab
+ cdef int64_t entity_vector_length
# This maps 64bit keys (hash of unique entity string)
- # to 64bit values (position of the _EntryC struct in the _entries vector).
+ # to 64bit values (position of the _KBEntryC struct in the _entries vector).
# The PreshMap is pretty space efficient, as it uses open addressing. So
# the only overhead is the vacancy rate, which is approximately 30%.
cdef PreshMap _entry_index
@@ -66,7 +41,7 @@ cdef class KnowledgeBase:
# over allocation.
# In total we end up with (N*128*1.3)+(N*128*1.3) bits for N entries.
# Storing 1m entries would take 41.6mb under this scheme.
- cdef vector[_EntryC] _entries
+ cdef entry_vec _entries
# This maps 64bit keys (hash of unique alias string)
# to 64bit values (position of the _AliasC struct in the _aliases_table vector).
@@ -76,7 +51,7 @@ cdef class KnowledgeBase:
# should be P(entity | mention), which is pretty important to know.
# We can pack both pieces of information into a 64-bit value, to keep things
# efficient.
- cdef vector[_AliasC] _aliases_table
+ cdef alias_vec _aliases_table
# This is the part which might take more space: storing various
# categorical features for the entries, and storing vectors for disambiguation
@@ -87,7 +62,7 @@ cdef class KnowledgeBase:
# model, that embeds different features of the entities into vectors. We'll
# still want some per-entity features, like the Wikipedia text or entity
# co-occurrence. Hopefully those vectors can be narrow, e.g. 64 dimensions.
- cdef object _vectors_table
+ cdef float_matrix _vectors_table
# It's very useful to track categorical features, at least for output, even
# if they're not useful in the model itself. For instance, we should be
@@ -96,53 +71,102 @@ cdef class KnowledgeBase:
# optional data, we can let users configure a DB as the backend for this.
cdef object _features_table
+
+ cdef inline int64_t c_add_vector(self, vector[float] entity_vector) nogil:
+ """Add an entity vector to the vectors table."""
+ cdef int64_t new_index = self._vectors_table.size()
+ self._vectors_table.push_back(entity_vector)
+ return new_index
+
+
cdef inline int64_t c_add_entity(self, hash_t entity_hash, float prob,
- int32_t* vector_rows, int feats_row):
- """Add an entry to the knowledge base."""
- # This is what we'll map the hash key to. It's where the entry will sit
+ int32_t vector_index, int feats_row) nogil:
+ """Add an entry to the vector of entries.
+ After calling this method, make sure to update also the _entry_index using the return value"""
+ # This is what we'll map the entity hash key to. It's where the entry will sit
# in the vector of entries, so we can get it later.
cdef int64_t new_index = self._entries.size()
- self._entries.push_back(
- _EntryC(
- entity_hash=entity_hash,
- vector_rows=vector_rows,
- feats_row=feats_row,
- prob=prob
- ))
- self._entry_index[entity_hash] = new_index
+
+ # Avoid struct initializer to enable nogil, cf https://github.com/cython/cython/issues/1642
+ cdef KBEntryC entry
+ entry.entity_hash = entity_hash
+ entry.vector_index = vector_index
+ entry.feats_row = feats_row
+ entry.prob = prob
+
+ self._entries.push_back(entry)
return new_index
- cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs):
- """Connect a mention to a list of potential entities with their prior probabilities ."""
+ cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs) nogil:
+ """Connect a mention to a list of potential entities with their prior probabilities .
+ After calling this method, make sure to update also the _alias_index using the return value"""
+ # This is what we'll map the alias hash key to. It's where the alias will be defined
+ # in the vector of aliases.
cdef int64_t new_index = self._aliases_table.size()
- self._aliases_table.push_back(
- _AliasC(
- entry_indices=entry_indices,
- probs=probs
- ))
- self._alias_index[alias_hash] = new_index
+ # Avoid struct initializer to enable nogil
+ cdef AliasC alias
+ alias.entry_indices = entry_indices
+ alias.probs = probs
+
+ self._aliases_table.push_back(alias)
return new_index
- cdef inline _create_empty_vectors(self):
+ cdef inline void _create_empty_vectors(self, hash_t dummy_hash) nogil:
"""
- Making sure the first element of each vector is a dummy,
+ Initializing the vectors and making sure the first element of each vector is a dummy,
because the PreshMap maps pointing to indices in these vectors can not contain 0 as value
cf. https://github.com/explosion/preshed/issues/17
"""
cdef int32_t dummy_value = 0
- self.vocab.strings.add("")
- self._entries.push_back(
- _EntryC(
- entity_hash=self.vocab.strings[""],
- vector_rows=&dummy_value,
- feats_row=dummy_value,
- prob=dummy_value
- ))
- self._aliases_table.push_back(
- _AliasC(
- entry_indices=[dummy_value],
- probs=[dummy_value]
- ))
+
+ # Avoid struct initializer to enable nogil
+ cdef KBEntryC entry
+ entry.entity_hash = dummy_hash
+ entry.vector_index = dummy_value
+ entry.feats_row = dummy_value
+ entry.prob = dummy_value
+
+ # Avoid struct initializer to enable nogil
+ cdef vector[int64_t] dummy_entry_indices
+ dummy_entry_indices.push_back(0)
+ cdef vector[float] dummy_probs
+ dummy_probs.push_back(0)
+
+ cdef AliasC alias
+ alias.entry_indices = dummy_entry_indices
+ alias.probs = dummy_probs
+
+ self._entries.push_back(entry)
+ self._aliases_table.push_back(alias)
+
+ cpdef load_bulk(self, loc)
+ cpdef set_entities(self, entity_list, prob_list, vector_list)
+cdef class Writer:
+ cdef FILE* _fp
+
+ cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1
+ cdef int write_vector_element(self, float element) except -1
+ cdef int write_entry(self, hash_t entry_hash, float entry_prob, int32_t vector_index) except -1
+
+ cdef int write_alias_length(self, int64_t alias_length) except -1
+ cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1
+ cdef int write_alias(self, int64_t entry_index, float prob) except -1
+
+ cdef int _write(self, void* value, size_t size) except -1
+
+cdef class Reader:
+ cdef FILE* _fp
+
+ cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1
+ cdef int read_vector_element(self, float* element) except -1
+ cdef int read_entry(self, hash_t* entity_hash, float* prob, int32_t* vector_index) except -1
+
+ cdef int read_alias_length(self, int64_t* alias_length) except -1
+ cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1
+ cdef int read_alias(self, int64_t* entry_index, float* prob) except -1
+
+ cdef int _read(self, void* value, size_t size) except -1
+
diff --git a/spacy/kb.pyx b/spacy/kb.pyx
index 3a0a8b918..7c2daa659 100644
--- a/spacy/kb.pyx
+++ b/spacy/kb.pyx
@@ -1,13 +1,30 @@
+# cython: infer_types=True
# cython: profile=True
# coding: utf8
from spacy.errors import Errors, Warnings, user_warning
+from pathlib import Path
+from cymem.cymem cimport Pool
+from preshed.maps cimport PreshMap
+
+from cpython.exc cimport PyErr_SetFromErrno
+
+from libc.stdio cimport fopen, fclose, fread, fwrite, feof, fseek
+from libc.stdint cimport int32_t, int64_t
+
+from .typedefs cimport hash_t
+
+from os import path
+from libcpp.vector cimport vector
+
cdef class Candidate:
- def __init__(self, KnowledgeBase kb, entity_hash, alias_hash, prior_prob):
+ def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
self.kb = kb
self.entity_hash = entity_hash
+ self.entity_freq = entity_freq
+ self.entity_vector = entity_vector
self.alias_hash = alias_hash
self.prior_prob = prior_prob
@@ -19,7 +36,7 @@ cdef class Candidate:
@property
def entity_(self):
"""RETURNS (unicode): ID/name of this entity in the KB"""
- return self.kb.vocab.strings[self.entity]
+ return self.kb.vocab.strings[self.entity_hash]
@property
def alias(self):
@@ -29,7 +46,15 @@ cdef class Candidate:
@property
def alias_(self):
"""RETURNS (unicode): ID of the original alias"""
- return self.kb.vocab.strings[self.alias]
+ return self.kb.vocab.strings[self.alias_hash]
+
+ @property
+ def entity_freq(self):
+ return self.entity_freq
+
+ @property
+ def entity_vector(self):
+ return self.entity_vector
@property
def prior_prob(self):
@@ -38,26 +63,41 @@ cdef class Candidate:
cdef class KnowledgeBase:
- def __init__(self, Vocab vocab):
+ def __init__(self, Vocab vocab, entity_vector_length):
self.vocab = vocab
+ self.mem = Pool()
+ self.entity_vector_length = entity_vector_length
+
self._entry_index = PreshMap()
self._alias_index = PreshMap()
- self.mem = Pool()
- self._create_empty_vectors()
+
+ self.vocab.strings.add("")
+ self._create_empty_vectors(dummy_hash=self.vocab.strings[""])
+
+ @property
+ def entity_vector_length(self):
+ """RETURNS (uint64): length of the entity vectors"""
+ return self.entity_vector_length
def __len__(self):
return self.get_size_entities()
def get_size_entities(self):
- return self._entries.size() - 1 # not counting dummy element on index 0
+ return len(self._entry_index)
+
+ def get_entity_strings(self):
+ return [self.vocab.strings[x] for x in self._entry_index]
def get_size_aliases(self):
- return self._aliases_table.size() - 1 # not counting dummy element on index 0
+ return len(self._alias_index)
- def add_entity(self, unicode entity, float prob=0.5, vectors=None, features=None):
+ def get_alias_strings(self):
+ return [self.vocab.strings[x] for x in self._alias_index]
+
+ def add_entity(self, unicode entity, float prob, vector[float] entity_vector):
"""
- Add an entity to the KB.
- Return the hash of the entity ID at the end
+ Add an entity to the KB, optionally specifying its log probability based on corpus frequency
+ Return the hash of the entity ID/name at the end.
"""
cdef hash_t entity_hash = self.vocab.strings.add(entity)
@@ -66,40 +106,72 @@ cdef class KnowledgeBase:
user_warning(Warnings.W018.format(entity=entity))
return
- cdef int32_t dummy_value = 342
- self.c_add_entity(entity_hash=entity_hash, prob=prob,
- vector_rows=&dummy_value, feats_row=dummy_value)
- # TODO self._vectors_table.get_pointer(vectors),
- # self._features_table.get(features))
+ # Raise an error if the provided entity vector is not of the correct length
+ if len(entity_vector) != self.entity_vector_length:
+ raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
+
+ vector_index = self.c_add_vector(entity_vector=entity_vector)
+
+ new_index = self.c_add_entity(entity_hash=entity_hash,
+ prob=prob,
+ vector_index=vector_index,
+ feats_row=-1) # Features table currently not implemented
+ self._entry_index[entity_hash] = new_index
return entity_hash
+ cpdef set_entities(self, entity_list, prob_list, vector_list):
+ if len(entity_list) != len(prob_list) or len(entity_list) != len(vector_list):
+ raise ValueError(Errors.E140)
+
+ nr_entities = len(entity_list)
+ self._entry_index = PreshMap(nr_entities+1)
+ self._entries = entry_vec(nr_entities+1)
+
+ i = 0
+ cdef KBEntryC entry
+ while i < nr_entities:
+ entity_vector = vector_list[i]
+ if len(entity_vector) != self.entity_vector_length:
+ raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
+
+ entity_hash = self.vocab.strings.add(entity_list[i])
+ entry.entity_hash = entity_hash
+ entry.prob = prob_list[i]
+
+ vector_index = self.c_add_vector(entity_vector=vector_list[i])
+ entry.vector_index = vector_index
+
+ entry.feats_row = -1 # Features table currently not implemented
+
+ self._entries[i+1] = entry
+ self._entry_index[entity_hash] = i+1
+
+ i += 1
+
def add_alias(self, unicode alias, entities, probabilities):
"""
For a given alias, add its potential entities and prior probabilies to the KB.
Return the alias_hash at the end
"""
-
# Throw an error if the length of entities and probabilities are not the same
if not len(entities) == len(probabilities):
raise ValueError(Errors.E132.format(alias=alias,
entities_length=len(entities),
probabilities_length=len(probabilities)))
- # Throw an error if the probabilities sum up to more than 1
+ # Throw an error if the probabilities sum up to more than 1 (allow for some rounding errors)
prob_sum = sum(probabilities)
- if prob_sum > 1:
+ if prob_sum > 1.00001:
raise ValueError(Errors.E133.format(alias=alias, sum=prob_sum))
cdef hash_t alias_hash = self.vocab.strings.add(alias)
- # Return if this alias was added before
+ # Check whether this alias was added before
if alias_hash in self._alias_index:
user_warning(Warnings.W017.format(alias=alias))
return
- cdef hash_t entity_hash
-
cdef vector[int64_t] entry_indices
cdef vector[float] probs
@@ -112,20 +184,295 @@ cdef class KnowledgeBase:
entry_indices.push_back(int(entry_index))
probs.push_back(float(prob))
- self.c_add_aliases(alias_hash=alias_hash, entry_indices=entry_indices, probs=probs)
+ new_index = self.c_add_aliases(alias_hash=alias_hash, entry_indices=entry_indices, probs=probs)
+ self._alias_index[alias_hash] = new_index
return alias_hash
-
def get_candidates(self, unicode alias):
- """ TODO: where to put this functionality ?"""
cdef hash_t alias_hash = self.vocab.strings[alias]
alias_index = <int64_t>self._alias_index.get(alias_hash)
alias_entry = self._aliases_table[alias_index]
return [Candidate(kb=self,
entity_hash=self._entries[entry_index].entity_hash,
+ entity_freq=self._entries[entry_index].prob,
+ entity_vector=self._vectors_table[self._entries[entry_index].vector_index],
alias_hash=alias_hash,
prior_prob=prob)
for (entry_index, prob) in zip(alias_entry.entry_indices, alias_entry.probs)
if entry_index != 0]
+
+
+ def dump(self, loc):
+ cdef Writer writer = Writer(loc)
+ writer.write_header(self.get_size_entities(), self.entity_vector_length)
+
+ # dumping the entity vectors in their original order
+ i = 0
+ for entity_vector in self._vectors_table:
+ for element in entity_vector:
+ writer.write_vector_element(element)
+ i = i+1
+
+ # dumping the entry records in the order in which they are in the _entries vector.
+ # index 0 is a dummy object not stored in the _entry_index and can be ignored.
+ i = 1
+ for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]):
+ entry = self._entries[entry_index]
+ assert entry.entity_hash == entry_hash
+ assert entry_index == i
+ writer.write_entry(entry.entity_hash, entry.prob, entry.vector_index)
+ i = i+1
+
+ writer.write_alias_length(self.get_size_aliases())
+
+ # dumping the aliases in the order in which they are in the _alias_index vector.
+ # index 0 is a dummy object not stored in the _aliases_table and can be ignored.
+ i = 1
+ for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]):
+ alias = self._aliases_table[alias_index]
+ assert alias_index == i
+
+ candidate_length = len(alias.entry_indices)
+ writer.write_alias_header(alias_hash, candidate_length)
+
+ for j in range(0, candidate_length):
+ writer.write_alias(alias.entry_indices[j], alias.probs[j])
+
+ i = i+1
+
+ writer.close()
+
+ cpdef load_bulk(self, loc):
+ cdef hash_t entity_hash
+ cdef hash_t alias_hash
+ cdef int64_t entry_index
+ cdef float prob
+ cdef int32_t vector_index
+ cdef KBEntryC entry
+ cdef AliasC alias
+ cdef float vector_element
+
+ cdef Reader reader = Reader(loc)
+
+ # STEP 0: load header and initialize KB
+ cdef int64_t nr_entities
+ cdef int64_t entity_vector_length
+ reader.read_header(&nr_entities, &entity_vector_length)
+
+ self.entity_vector_length = entity_vector_length
+ self._entry_index = PreshMap(nr_entities+1)
+ self._entries = entry_vec(nr_entities+1)
+ self._vectors_table = float_matrix(nr_entities+1)
+
+ # STEP 1: load entity vectors
+ cdef int i = 0
+ cdef int j = 0
+ while i < nr_entities:
+ entity_vector = float_vec(entity_vector_length)
+ j = 0
+ while j < entity_vector_length:
+ reader.read_vector_element(&vector_element)
+ entity_vector[j] = vector_element
+ j = j+1
+ self._vectors_table[i] = entity_vector
+ i = i+1
+
+ # STEP 2: load entities
+ # we assume that the entity data was written in sequence
+ # index 0 is a dummy object not stored in the _entry_index and can be ignored.
+ i = 1
+ while i <= nr_entities:
+ reader.read_entry(&entity_hash, &prob, &vector_index)
+
+ entry.entity_hash = entity_hash
+ entry.prob = prob
+ entry.vector_index = vector_index
+ entry.feats_row = -1 # Features table currently not implemented
+
+ self._entries[i] = entry
+ self._entry_index[entity_hash] = i
+
+ i += 1
+
+ # check that all entities were read in properly
+ assert nr_entities == self.get_size_entities()
+
+ # STEP 3: load aliases
+
+ cdef int64_t nr_aliases
+ reader.read_alias_length(&nr_aliases)
+ self._alias_index = PreshMap(nr_aliases+1)
+ self._aliases_table = alias_vec(nr_aliases+1)
+
+ cdef int64_t nr_candidates
+ cdef vector[int64_t] entry_indices
+ cdef vector[float] probs
+
+ i = 1
+ # we assume the alias data was written in sequence
+ # index 0 is a dummy object not stored in the _entry_index and can be ignored.
+ while i <= nr_aliases:
+ reader.read_alias_header(&alias_hash, &nr_candidates)
+ entry_indices = vector[int64_t](nr_candidates)
+ probs = vector[float](nr_candidates)
+
+ for j in range(0, nr_candidates):
+ reader.read_alias(&entry_index, &prob)
+ entry_indices[j] = entry_index
+ probs[j] = prob
+
+ alias.entry_indices = entry_indices
+ alias.probs = probs
+
+ self._aliases_table[i] = alias
+ self._alias_index[alias_hash] = i
+
+ i += 1
+
+ # check that all aliases were read in properly
+ assert nr_aliases == self.get_size_aliases()
+
+
+cdef class Writer:
+ def __init__(self, object loc):
+ if path.exists(loc):
+ assert not path.isdir(loc), "%s is directory." % loc
+ if isinstance(loc, Path):
+ loc = bytes(loc)
+ cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
+ self._fp = fopen(<char*>bytes_loc, 'wb')
+ assert self._fp != NULL
+ fseek(self._fp, 0, 0)
+
+ def close(self):
+ cdef size_t status = fclose(self._fp)
+ assert status == 0
+
+ cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1:
+ self._write(&nr_entries, sizeof(nr_entries))
+ self._write(&entity_vector_length, sizeof(entity_vector_length))
+
+ cdef int write_vector_element(self, float element) except -1:
+ self._write(&element, sizeof(element))
+
+ cdef int write_entry(self, hash_t entry_hash, float entry_prob, int32_t vector_index) except -1:
+ self._write(&entry_hash, sizeof(entry_hash))
+ self._write(&entry_prob, sizeof(entry_prob))
+ self._write(&vector_index, sizeof(vector_index))
+ # Features table currently not implemented and not written to file
+
+ cdef int write_alias_length(self, int64_t alias_length) except -1:
+ self._write(&alias_length, sizeof(alias_length))
+
+ cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1:
+ self._write(&alias_hash, sizeof(alias_hash))
+ self._write(&candidate_length, sizeof(candidate_length))
+
+ cdef int write_alias(self, int64_t entry_index, float prob) except -1:
+ self._write(&entry_index, sizeof(entry_index))
+ self._write(&prob, sizeof(prob))
+
+ cdef int _write(self, void* value, size_t size) except -1:
+ status = fwrite(value, size, 1, self._fp)
+ assert status == 1, status
+
+
+cdef class Reader:
+ def __init__(self, object loc):
+ assert path.exists(loc)
+ assert not path.isdir(loc)
+ if isinstance(loc, Path):
+ loc = bytes(loc)
+ cdef bytes bytes_loc = loc.encode('utf8') if type(loc) == unicode else loc
+ self._fp = fopen(<char*>bytes_loc, 'rb')
+ if not self._fp:
+ PyErr_SetFromErrno(IOError)
+ status = fseek(self._fp, 0, 0) # this can be 0 if there is no header
+
+ def __dealloc__(self):
+ fclose(self._fp)
+
+ cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1:
+ status = self._read(nr_entries, sizeof(int64_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading header from input file")
+
+ status = self._read(entity_vector_length, sizeof(int64_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading header from input file")
+
+ cdef int read_vector_element(self, float* element) except -1:
+ status = self._read(element, sizeof(float))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading entity vector from input file")
+
+ cdef int read_entry(self, hash_t* entity_hash, float* prob, int32_t* vector_index) except -1:
+ status = self._read(entity_hash, sizeof(hash_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading entity hash from input file")
+
+ status = self._read(prob, sizeof(float))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading entity prob from input file")
+
+ status = self._read(vector_index, sizeof(int32_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading entity vector from input file")
+
+ if feof(self._fp):
+ return 0
+ else:
+ return 1
+
+ cdef int read_alias_length(self, int64_t* alias_length) except -1:
+ status = self._read(alias_length, sizeof(int64_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading alias length from input file")
+
+ cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1:
+ status = self._read(alias_hash, sizeof(hash_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading alias hash from input file")
+
+ status = self._read(candidate_length, sizeof(int64_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading candidate length from input file")
+
+ cdef int read_alias(self, int64_t* entry_index, float* prob) except -1:
+ status = self._read(entry_index, sizeof(int64_t))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading entry index for alias from input file")
+
+ status = self._read(prob, sizeof(float))
+ if status < 1:
+ if feof(self._fp):
+ return 0 # end of file
+ raise IOError("error reading prob for entity/alias from input file")
+
+ cdef int _read(self, void* value, size_t size) except -1:
+ status = fread(value, size, 1, self._fp)
+ return status
+
+
diff --git a/spacy/lang/char_classes.py b/spacy/lang/char_classes.py
index cb2e817d5..fb320b2ff 100644
--- a/spacy/lang/char_classes.py
+++ b/spacy/lang/char_classes.py
@@ -9,6 +9,8 @@ _bengali = r"\u0980-\u09FF"
_hebrew = r"\u0591-\u05F4\uFB1D-\uFB4F"
+_hindi = r"\u0900-\u097F"
+
# Latin standard
_latin_u_standard = r"A-Z"
_latin_l_standard = r"a-z"
@@ -193,7 +195,7 @@ _ukrainian = r"а-щюяіїєґА-ЩЮЯІЇЄҐ"
_upper = LATIN_UPPER + _russian_upper + _tatar_upper + _greek_upper + _ukrainian_upper
_lower = LATIN_LOWER + _russian_lower + _tatar_lower + _greek_lower + _ukrainian_lower
-_uncased = _bengali + _hebrew + _persian + _sinhala
+_uncased = _bengali + _hebrew + _persian + _sinhala + _hindi
ALPHA = group_chars(LATIN + _russian + _tatar + _greek + _ukrainian + _uncased)
ALPHA_LOWER = group_chars(_lower + _uncased)
diff --git a/spacy/lang/ko/__init__.py b/spacy/lang/ko/__init__.py
new file mode 100644
index 000000000..f5dff75f1
--- /dev/null
+++ b/spacy/lang/ko/__init__.py
@@ -0,0 +1,120 @@
+# encoding: utf8
+from __future__ import unicode_literals, print_function
+
+import re
+import sys
+
+
+from .stop_words import STOP_WORDS
+from .tag_map import TAG_MAP
+from ...attrs import LANG
+from ...language import Language
+from ...tokens import Doc
+from ...compat import copy_reg
+from ...util import DummyTokenizer
+from ...compat import is_python3, is_python_pre_3_5
+
+is_python_post_3_7 = is_python3 and sys.version_info[1] >= 7
+
+# fmt: off
+if is_python_pre_3_5:
+ from collections import namedtuple
+ Morpheme = namedtuple("Morpheme", "surface lemma tag")
+elif is_python_post_3_7:
+ from dataclasses import dataclass
+
+ @dataclass(frozen=True)
+ class Morpheme:
+ surface: str
+ lemma: str
+ tag: str
+else:
+ from typing import NamedTuple
+
+ class Morpheme(NamedTuple):
+ surface: str
+ lemma: str
+ tag: str
+
+
+def try_mecab_import():
+ try:
+ from natto import MeCab
+ return MeCab
+ except ImportError:
+ raise ImportError(
+ "Korean support requires [mecab-ko](https://bitbucket.org/eunjeon/mecab-ko/src/master/README.md), "
+ "[mecab-ko-dic](https://bitbucket.org/eunjeon/mecab-ko-dic), "
+ "and [natto-py](https://github.com/buruzaemon/natto-py)"
+ )
+# fmt: on
+
+
+def check_spaces(text, tokens):
+ token_pattern = re.compile(r"\s?".join(f"({t})" for t in tokens))
+ m = token_pattern.match(text)
+ if m is not None:
+ for i in range(1, m.lastindex):
+ yield m.end(i) < m.start(i + 1)
+ yield False
+
+
+class KoreanTokenizer(DummyTokenizer):
+ def __init__(self, cls, nlp=None):
+ self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
+ self.Tokenizer = try_mecab_import()
+
+ def __call__(self, text):
+ dtokens = list(self.detailed_tokens(text))
+ surfaces = [dt.surface for dt in dtokens]
+ doc = Doc(self.vocab, words=surfaces, spaces=list(check_spaces(text, surfaces)))
+ for token, dtoken in zip(doc, dtokens):
+ first_tag, sep, eomi_tags = dtoken.tag.partition("+")
+ token.tag_ = first_tag # stem(어간) or pre-final(선어말 어미)
+ token.lemma_ = dtoken.lemma
+ doc.user_data["full_tags"] = [dt.tag for dt in dtokens]
+ return doc
+
+ def detailed_tokens(self, text):
+ # 품사 태그(POS)[0], 의미 부류(semantic class)[1], 종성 유무(jongseong)[2], 읽기(reading)[3],
+ # 타입(type)[4], 첫번째 품사(start pos)[5], 마지막 품사(end pos)[6], 표현(expression)[7], *
+ with self.Tokenizer("-F%f[0],%f[7]") as tokenizer:
+ for node in tokenizer.parse(text, as_nodes=True):
+ if node.is_eos():
+ break
+ surface = node.surface
+ feature = node.feature
+ tag, _, expr = feature.partition(",")
+ lemma, _, remainder = expr.partition("/")
+ if lemma == "*":
+ lemma = surface
+ yield Morpheme(surface, lemma, tag)
+
+
+class KoreanDefaults(Language.Defaults):
+ lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
+ lex_attr_getters[LANG] = lambda _text: "ko"
+ stop_words = STOP_WORDS
+ tag_map = TAG_MAP
+ writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
+
+ @classmethod
+ def create_tokenizer(cls, nlp=None):
+ return KoreanTokenizer(cls, nlp)
+
+
+class Korean(Language):
+ lang = "ko"
+ Defaults = KoreanDefaults
+
+ def make_doc(self, text):
+ return self.tokenizer(text)
+
+
+def pickle_korean(instance):
+ return Korean, tuple()
+
+
+copy_reg.pickle(Korean, pickle_korean)
+
+__all__ = ["Korean"]
diff --git a/spacy/lang/ko/examples.py b/spacy/lang/ko/examples.py
new file mode 100644
index 000000000..10a6ea9bd
--- /dev/null
+++ b/spacy/lang/ko/examples.py
@@ -0,0 +1,15 @@
+# coding: utf8
+from __future__ import unicode_literals
+"""
+Example sentences to test spaCy and its language models.
+
+>>> from spacy.lang.ko.examples import sentences
+>>> docs = nlp.pipe(sentences)
+"""
+
+sentences = [
+ "애플이 영국의 신생 기업을 10억 달러에 구매를 고려중이다.",
+ "자동 운전 자동차의 손해 배상 책임에 자동차 메이커에 일정한 부담을 요구하겠다.",
+ "자동 배달 로봇이 보도를 주행하는 것을 샌프란시스코시가 금지를 검토중이라고 합니다.",
+ "런던은 영국의 수도이자 가장 큰 도시입니다."
+]
diff --git a/spacy/lang/ko/stop_words.py b/spacy/lang/ko/stop_words.py
new file mode 100644
index 000000000..53cf6f29a
--- /dev/null
+++ b/spacy/lang/ko/stop_words.py
@@ -0,0 +1,68 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+STOP_WORDS = set("""
+이
+있
+하
+것
+들
+그
+되
+수
+이
+보
+않
+없
+나
+주
+아니
+등
+같
+때
+년
+가
+한
+지
+오
+말
+일
+그렇
+위하
+때문
+그것
+두
+말하
+알
+그러나
+받
+못하
+일
+그런
+또
+더
+많
+그리고
+좋
+크
+시키
+그러
+하나
+살
+데
+안
+어떤
+번
+나
+다른
+어떻
+들
+이렇
+점
+싶
+말
+좀
+원
+잘
+놓
+""".split())
diff --git a/spacy/lang/ko/tag_map.py b/spacy/lang/ko/tag_map.py
new file mode 100644
index 000000000..57317c969
--- /dev/null
+++ b/spacy/lang/ko/tag_map.py
@@ -0,0 +1,59 @@
+# encoding: utf8
+from __future__ import unicode_literals
+
+from ...symbols import POS, PUNCT, INTJ, X, SYM, ADJ, AUX, ADP, CONJ, NOUN, PRON
+from ...symbols import VERB, ADV, PROPN, NUM, DET
+
+# 은전한닢(mecab-ko-dic)의 품사 태그를 universal pos tag로 대응시킴
+# https://docs.google.com/spreadsheets/d/1-9blXKjtjeKZqsf4NzHeYJCrr49-nXeRF6D80udfcwY/edit#gid=589544265
+# https://universaldependencies.org/u/pos/
+TAG_MAP = {
+ # J.{1,2} 조사
+ "JKS": {POS: ADP},
+ "JKC": {POS: ADP},
+ "JKG": {POS: ADP},
+ "JKO": {POS: ADP},
+ "JKB": {POS: ADP},
+ "JKV": {POS: ADP},
+ "JKQ": {POS: ADP},
+ "JX": {POS: ADP}, # 보조사
+ "JC": {POS: CONJ}, # 접속 조사
+ "MAJ": {POS: CONJ}, # 접속 부사
+ "MAG": {POS: ADV}, # 일반 부사
+ "MM": {POS: DET}, # 관형사
+ "XPN": {POS: X}, # 접두사
+ # XS. 접미사
+ "XSN": {POS: X},
+ "XSV": {POS: X},
+ "XSA": {POS: X},
+ "XR": {POS: X}, # 어근
+ # E.{1,2} 어미
+ "EP": {POS: X},
+ "EF": {POS: X},
+ "EC": {POS: X},
+ "ETN": {POS: X},
+ "ETM": {POS: X},
+ "IC": {POS: INTJ}, # 감탄사
+ "VV": {POS: VERB}, # 동사
+ "VA": {POS: ADJ}, # 형용사
+ "VX": {POS: AUX}, # 보조 용언
+ "VCP": {POS: ADP}, # 긍정 지정사(이다)
+ "VCN": {POS: ADJ}, # 부정 지정사(아니다)
+ "NNG": {POS: NOUN}, # 일반 명사(general noun)
+ "NNB": {POS: NOUN}, # 의존 명사
+ "NNBC": {POS: NOUN}, # 의존 명사(단위: unit)
+ "NNP": {POS: PROPN}, # 고유 명사(proper noun)
+ "NP": {POS: PRON}, # 대명사
+ "NR": {POS: NUM}, # 수사(numerals)
+ "SN": {POS: NUM}, # 숫자
+ # S.{1,2} 부호
+ # 문장 부호
+ "SF": {POS: PUNCT}, # period or other EOS marker
+ "SE": {POS: PUNCT},
+ "SC": {POS: PUNCT}, # comma, etc.
+ "SSO": {POS: PUNCT}, # open bracket
+ "SSC": {POS: PUNCT}, # close bracket
+ "SY": {POS: SYM}, # 기타 기호
+ "SL": {POS: X}, # 외국어
+ "SH": {POS: X}, # 한자
+}
diff --git a/spacy/matcher/matcher.pyx b/spacy/matcher/matcher.pyx
index 2dd8c2940..86658ce99 100644
--- a/spacy/matcher/matcher.pyx
+++ b/spacy/matcher/matcher.pyx
@@ -262,13 +262,13 @@ cdef find_matches(TokenPatternC** patterns, int n, Doc doc, extensions=None,
cdef attr_t get_ent_id(const TokenPatternC* pattern) nogil:
+ # There have been a few bugs here.
# The code was originally designed to always have pattern[1].attrs.value
# be the ent_id when we get to the end of a pattern. However, Issue #2671
# showed this wasn't the case when we had a reject-and-continue before a
- # match. I still don't really understand what's going on here, but this
- # workaround does resolve the issue.
- while pattern.attrs.attr != ID and \
- (pattern.nr_attr > 0 or pattern.nr_extra_attr > 0 or pattern.nr_py > 0):
+ # match.
+ # The patch to #2671 was wrong though, which came up in #3839.
+ while pattern.attrs.attr != ID:
pattern += 1
return pattern.attrs.value
diff --git a/spacy/pipeline/entityruler.py b/spacy/pipeline/entityruler.py
index 4f89e4186..35b465ceb 100644
--- a/spacy/pipeline/entityruler.py
+++ b/spacy/pipeline/entityruler.py
@@ -10,7 +10,7 @@ from ..util import ensure_path, to_disk, from_disk
from ..tokens import Span
from ..matcher import Matcher, PhraseMatcher
-DEFAULT_ENT_ID_SEP = '||'
+DEFAULT_ENT_ID_SEP = "||"
class EntityRuler(object):
@@ -53,7 +53,9 @@ class EntityRuler(object):
self.matcher = Matcher(nlp.vocab)
if phrase_matcher_attr is not None:
self.phrase_matcher_attr = phrase_matcher_attr
- self.phrase_matcher = PhraseMatcher(nlp.vocab, attr=self.phrase_matcher_attr)
+ self.phrase_matcher = PhraseMatcher(
+ nlp.vocab, attr=self.phrase_matcher_attr
+ )
else:
self.phrase_matcher_attr = None
self.phrase_matcher = PhraseMatcher(nlp.vocab)
@@ -223,13 +225,14 @@ class EntityRuler(object):
"""
cfg = srsly.msgpack_loads(patterns_bytes)
if isinstance(cfg, dict):
- self.add_patterns(cfg.get('patterns', cfg))
- self.overwrite = cfg.get('overwrite', False)
- self.phrase_matcher_attr = cfg.get('phrase_matcher_attr', None)
+ self.add_patterns(cfg.get("patterns", cfg))
+ self.overwrite = cfg.get("overwrite", False)
+ self.phrase_matcher_attr = cfg.get("phrase_matcher_attr", None)
if self.phrase_matcher_attr is not None:
- self.phrase_matcher = PhraseMatcher(self.nlp.vocab,
- attr=self.phrase_matcher_attr)
- self.ent_id_sep = cfg.get('ent_id_sep', DEFAULT_ENT_ID_SEP)
+ self.phrase_matcher = PhraseMatcher(
+ self.nlp.vocab, attr=self.phrase_matcher_attr
+ )
+ self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
else:
self.add_patterns(cfg)
return self
@@ -242,11 +245,14 @@ class EntityRuler(object):
DOCS: https://spacy.io/api/entityruler#to_bytes
"""
- serial = OrderedDict((
- ('overwrite', self.overwrite),
- ('ent_id_sep', self.ent_id_sep),
- ('phrase_matcher_attr', self.phrase_matcher_attr),
- ('patterns', self.patterns)))
+ serial = OrderedDict(
+ (
+ ("overwrite", self.overwrite),
+ ("ent_id_sep", self.ent_id_sep),
+ ("phrase_matcher_attr", self.phrase_matcher_attr),
+ ("patterns", self.patterns),
+ )
+ )
return srsly.msgpack_dumps(serial)
def from_disk(self, path, **kwargs):
@@ -260,42 +266,52 @@ class EntityRuler(object):
DOCS: https://spacy.io/api/entityruler#from_disk
"""
path = ensure_path(path)
- if path.is_file():
- patterns = srsly.read_jsonl(path)
+ depr_patterns_path = path.with_suffix(".jsonl")
+ if depr_patterns_path.is_file():
+ patterns = srsly.read_jsonl(depr_patterns_path)
self.add_patterns(patterns)
else:
cfg = {}
deserializers = {
- 'patterns': lambda p: self.add_patterns(srsly.read_jsonl(p.with_suffix('.jsonl'))),
- 'cfg': lambda p: cfg.update(srsly.read_json(p))
+ "patterns": lambda p: self.add_patterns(
+ srsly.read_jsonl(p.with_suffix(".jsonl"))
+ ),
+ "cfg": lambda p: cfg.update(srsly.read_json(p)),
}
from_disk(path, deserializers, {})
- self.overwrite = cfg.get('overwrite', False)
- self.phrase_matcher_attr = cfg.get('phrase_matcher_attr')
- self.ent_id_sep = cfg.get('ent_id_sep', DEFAULT_ENT_ID_SEP)
+ self.overwrite = cfg.get("overwrite", False)
+ self.phrase_matcher_attr = cfg.get("phrase_matcher_attr")
+ self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
if self.phrase_matcher_attr is not None:
- self.phrase_matcher = PhraseMatcher(self.nlp.vocab,
- attr=self.phrase_matcher_attr)
+ self.phrase_matcher = PhraseMatcher(
+ self.nlp.vocab, attr=self.phrase_matcher_attr
+ )
return self
def to_disk(self, path, **kwargs):
"""Save the entity ruler patterns to a directory. The patterns will be
saved as newline-delimited JSON (JSONL).
- path (unicode / Path): The JSONL file to load.
+ path (unicode / Path): The JSONL file to save.
**kwargs: Other config paramters, mostly for consistency.
RETURNS (EntityRuler): The loaded entity ruler.
DOCS: https://spacy.io/api/entityruler#to_disk
"""
- cfg = {'overwrite': self.overwrite,
- 'phrase_matcher_attr': self.phrase_matcher_attr,
- 'ent_id_sep': self.ent_id_sep}
- serializers = {
- 'patterns': lambda p: srsly.write_jsonl(p.with_suffix('.jsonl'),
- self.patterns),
- 'cfg': lambda p: srsly.write_json(p, cfg)
- }
path = ensure_path(path)
- to_disk(path, serializers, {})
+ cfg = {
+ "overwrite": self.overwrite,
+ "phrase_matcher_attr": self.phrase_matcher_attr,
+ "ent_id_sep": self.ent_id_sep,
+ }
+ serializers = {
+ "patterns": lambda p: srsly.write_jsonl(
+ p.with_suffix(".jsonl"), self.patterns
+ ),
+ "cfg": lambda p: srsly.write_json(p, cfg),
+ }
+ if path.suffix == ".jsonl": # user wants to save only JSONL
+ srsly.write_jsonl(path, self.patterns)
+ else:
+ to_disk(path, serializers, {})
diff --git a/spacy/pipeline/pipes.pyx b/spacy/pipeline/pipes.pyx
index 1d4eeadce..891e8d4e3 100644
--- a/spacy/pipeline/pipes.pyx
+++ b/spacy/pipeline/pipes.pyx
@@ -3,16 +3,18 @@
# coding: utf8
from __future__ import unicode_literals
-cimport numpy as np
-
import numpy
import srsly
+import random
from collections import OrderedDict
from thinc.api import chain
from thinc.v2v import Affine, Maxout, Softmax
from thinc.misc import LayerNorm
-from thinc.neural.util import to_categorical, copy_array
+from thinc.neural.util import to_categorical
+from thinc.neural.util import get_array_module
+from spacy.kb import KnowledgeBase
+from ..cli.pretrain import get_cossim_loss
from .functions import merge_subtokens
from ..tokens.doc cimport Doc
from ..syntax.nn_parser cimport Parser
@@ -24,9 +26,9 @@ from ..vocab cimport Vocab
from ..syntax import nonproj
from ..attrs import POS, ID
from ..parts_of_speech import X
-from .._ml import Tok2Vec, build_tagger_model
+from .._ml import Tok2Vec, build_tagger_model, cosine
from .._ml import build_text_classifier, build_simple_cnn_text_classifier
-from .._ml import build_bow_text_classifier
+from .._ml import build_bow_text_classifier, build_nel_encoder
from .._ml import link_vectors_to_models, zero_init, flatten
from .._ml import masked_language_model, create_default_optimizer
from ..errors import Errors, TempErrors
@@ -229,7 +231,7 @@ class Tensorizer(Pipe):
vocab (Vocab): A `Vocab` instance. The model must share the same
`Vocab` instance with the `Doc` objects it will process.
- model (Model): A `Model` instance or `True` allocate one later.
+ model (Model): A `Model` instance or `True` to allocate one later.
**cfg: Config parameters.
EXAMPLE:
@@ -294,7 +296,7 @@ class Tensorizer(Pipe):
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
- drop (float): The droput rate.
+ drop (float): The dropout rate.
sgd (callable): An optimizer.
RETURNS (dict): Results from the update.
"""
@@ -386,7 +388,7 @@ class Tagger(Pipe):
def predict(self, docs):
self.require_model()
if not any(len(doc) for doc in docs):
- # Handle case where there are no tokens in any docs.
+ # Handle cases where there are no tokens in any docs.
n_labels = len(self.labels)
guesses = [self.model.ops.allocate((0, n_labels)) for doc in docs]
tokvecs = self.model.ops.allocate((0, self.model.tok2vec.nO))
@@ -900,6 +902,11 @@ class TextCategorizer(Pipe):
def labels(self):
return tuple(self.cfg.setdefault("labels", []))
+ def require_labels(self):
+ """Raise an error if the component's model has no labels defined."""
+ if not self.labels:
+ raise ValueError(Errors.E143.format(name=self.name))
+
@labels.setter
def labels(self, value):
self.cfg["labels"] = tuple(value)
@@ -929,6 +936,7 @@ class TextCategorizer(Pipe):
doc.cats[label] = float(scores[i, j])
def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
+ self.require_model()
scores, bp_scores = self.model.begin_update(docs, drop=drop)
loss, d_scores = self.get_loss(docs, golds, scores)
bp_scores(d_scores, sgd=sgd)
@@ -983,6 +991,7 @@ class TextCategorizer(Pipe):
def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
if self.model is True:
self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors")
+ self.require_labels()
self.model = self.Model(len(self.labels), **self.cfg)
link_vectors_to_models(self.vocab)
if sgd is None:
@@ -1001,7 +1010,7 @@ cdef class DependencyParser(Parser):
@property
def postprocesses(self):
- return [nonproj.deprojectivize, merge_subtokens]
+ return [nonproj.deprojectivize]
def add_multitask_objective(self, target):
if target == "cloze":
@@ -1063,52 +1072,252 @@ cdef class EntityRecognizer(Parser):
class EntityLinker(Pipe):
+ """Pipeline component for named entity linking.
+
+ DOCS: TODO
+ """
name = 'entity_linker'
@classmethod
- def Model(cls, nr_class=1, **cfg):
- # TODO: non-dummy EL implementation
- return None
+ def Model(cls, **cfg):
+ embed_width = cfg.get("embed_width", 300)
+ hidden_width = cfg.get("hidden_width", 128)
+ type_to_int = cfg.get("type_to_int", dict())
- def __init__(self, model=True, **cfg):
- self.model = False
+ model = build_nel_encoder(embed_width=embed_width, hidden_width=hidden_width, ner_types=len(type_to_int), **cfg)
+ return model
+
+ def __init__(self, vocab, **cfg):
+ self.vocab = vocab
+ self.model = True
+ self.kb = None
self.cfg = dict(cfg)
- self.kb = self.cfg["kb"]
+ self.sgd_context = None
+
+ def set_kb(self, kb):
+ self.kb = kb
+
+ def require_model(self):
+ # Raise an error if the component's model is not initialized.
+ if getattr(self, "model", None) in (None, True, False):
+ raise ValueError(Errors.E109.format(name=self.name))
+
+ def require_kb(self):
+ # Raise an error if the knowledge base is not initialized.
+ if getattr(self, "kb", None) in (None, True, False):
+ raise ValueError(Errors.E139.format(name=self.name))
+
+ def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs):
+ self.require_kb()
+ self.cfg["entity_width"] = self.kb.entity_vector_length
+
+ if self.model is True:
+ self.model = self.Model(**self.cfg)
+ self.sgd_context = self.create_optimizer()
+
+ if sgd is None:
+ sgd = self.create_optimizer()
+
+ return sgd
+
+ def update(self, docs, golds, state=None, drop=0.0, sgd=None, losses=None):
+ self.require_model()
+ self.require_kb()
+
+ if losses is not None:
+ losses.setdefault(self.name, 0.0)
+
+ if not docs or not golds:
+ return 0
+
+ if len(docs) != len(golds):
+ raise ValueError(Errors.E077.format(value="EL training", n_docs=len(docs),
+ n_golds=len(golds)))
+
+ if isinstance(docs, Doc):
+ docs = [docs]
+ golds = [golds]
+
+ context_docs = []
+ entity_encodings = []
+ cats = []
+ priors = []
+ type_vectors = []
+
+ type_to_int = self.cfg.get("type_to_int", dict())
+
+ for doc, gold in zip(docs, golds):
+ ents_by_offset = dict()
+ for ent in doc.ents:
+ ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)] = ent
+ for entity in gold.links:
+ start, end, gold_kb = entity
+ mention = doc.text[start:end]
+
+ gold_ent = ents_by_offset[str(ent.start_char) + "_" + str(ent.end_char)]
+ assert gold_ent is not None
+ type_vector = [0 for i in range(len(type_to_int))]
+ if len(type_to_int) > 0:
+ type_vector[type_to_int[gold_ent.label_]] = 1
+
+ candidates = self.kb.get_candidates(mention)
+ random.shuffle(candidates)
+ nr_neg = 0
+ for c in candidates:
+ kb_id = c.entity_
+ entity_encoding = c.entity_vector
+ entity_encodings.append(entity_encoding)
+ context_docs.append(doc)
+ type_vectors.append(type_vector)
+
+ if self.cfg.get("prior_weight", 1) > 0:
+ priors.append([c.prior_prob])
+ else:
+ priors.append([0])
+
+ if kb_id == gold_kb:
+ cats.append([1])
+ else:
+ nr_neg += 1
+ cats.append([0])
+
+ if len(entity_encodings) > 0:
+ assert len(priors) == len(entity_encodings) == len(context_docs) == len(cats) == len(type_vectors)
+
+ context_encodings, bp_context = self.model.tok2vec.begin_update(context_docs, drop=drop)
+ entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
+
+ mention_encodings = [list(context_encodings[i]) + list(entity_encodings[i]) + priors[i] + type_vectors[i]
+ for i in range(len(entity_encodings))]
+ pred, bp_mention = self.model.begin_update(self.model.ops.asarray(mention_encodings, dtype="float32"), drop=drop)
+ cats = self.model.ops.asarray(cats, dtype="float32")
+
+ loss, d_scores = self.get_loss(prediction=pred, golds=cats, docs=None)
+ mention_gradient = bp_mention(d_scores, sgd=sgd)
+
+ context_gradients = [list(x[0:self.cfg.get("context_width")]) for x in mention_gradient]
+ bp_context(self.model.ops.asarray(context_gradients, dtype="float32"), sgd=self.sgd_context)
+
+ if losses is not None:
+ losses[self.name] += loss
+ return loss
+ return 0
+
+ def get_loss(self, docs, golds, prediction):
+ d_scores = (prediction - golds)
+ loss = (d_scores ** 2).sum()
+ loss = loss / len(golds)
+ return loss, d_scores
+
+ def get_loss_old(self, docs, golds, scores):
+ # this loss function assumes we're only using positive examples
+ loss, gradients = get_cossim_loss(yh=scores, y=golds)
+ loss = loss / len(golds)
+ return loss, gradients
def __call__(self, doc):
- self.set_annotations([doc], scores=None, tensors=None)
+ entities, kb_ids = self.predict([doc])
+ self.set_annotations([doc], entities, kb_ids)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
- """Apply the pipe to a stream of documents.
- Both __call__ and pipe should delegate to the `predict()`
- and `set_annotations()` methods.
- """
for docs in util.minibatch(stream, size=batch_size):
docs = list(docs)
- self.set_annotations(docs, scores=None, tensors=None)
+ entities, kb_ids = self.predict(docs)
+ self.set_annotations(docs, entities, kb_ids)
yield from docs
- def set_annotations(self, docs, scores, tensors=None):
- """
- Currently implemented as taking the KB entry with highest prior probability for each named entity
- TODO: actually use context etc
- """
- for i, doc in enumerate(docs):
- for ent in doc.ents:
- candidates = self.kb.get_candidates(ent.text)
- if candidates:
- best_candidate = max(candidates, key=lambda c: c.prior_prob)
- for token in ent:
- token.ent_kb_id_ = best_candidate.entity_
+ def predict(self, docs):
+ self.require_model()
+ self.require_kb()
- def get_loss(self, docs, golds, scores):
- # TODO
- pass
+ final_entities = []
+ final_kb_ids = []
+
+ if not docs:
+ return final_entities, final_kb_ids
+
+ if isinstance(docs, Doc):
+ docs = [docs]
+
+ context_encodings = self.model.tok2vec(docs)
+ xp = get_array_module(context_encodings)
+
+ type_to_int = self.cfg.get("type_to_int", dict())
+
+ for i, doc in enumerate(docs):
+ if len(doc) > 0:
+ context_encoding = context_encodings[i]
+ for ent in doc.ents:
+ type_vector = [0 for i in range(len(type_to_int))]
+ if len(type_to_int) > 0:
+ type_vector[type_to_int[ent.label_]] = 1
+
+ candidates = self.kb.get_candidates(ent.text)
+ if candidates:
+ random.shuffle(candidates)
+
+ # this will set the prior probabilities to 0 (just like in training) if their weight is 0
+ prior_probs = xp.asarray([[c.prior_prob] for c in candidates])
+ prior_probs *= self.cfg.get("prior_weight", 1)
+ scores = prior_probs
+
+ if self.cfg.get("context_weight", 1) > 0:
+ entity_encodings = xp.asarray([c.entity_vector for c in candidates])
+ assert len(entity_encodings) == len(prior_probs)
+ mention_encodings = [list(context_encoding) + list(entity_encodings[i])
+ + list(prior_probs[i]) + type_vector
+ for i in range(len(entity_encodings))]
+ scores = self.model(self.model.ops.asarray(mention_encodings, dtype="float32"))
+
+ # TODO: thresholding
+ best_index = scores.argmax()
+ best_candidate = candidates[best_index]
+ final_entities.append(ent)
+ final_kb_ids.append(best_candidate.entity_)
+
+ return final_entities, final_kb_ids
+
+ def set_annotations(self, docs, entities, kb_ids=None):
+ for entity, kb_id in zip(entities, kb_ids):
+ for token in entity:
+ token.ent_kb_id_ = kb_id
+
+ def to_disk(self, path, exclude=tuple(), **kwargs):
+ serialize = OrderedDict()
+ serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
+ serialize["vocab"] = lambda p: self.vocab.to_disk(p)
+ serialize["kb"] = lambda p: self.kb.dump(p)
+ if self.model not in (None, True, False):
+ serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes())
+ exclude = util.get_serialization_exclude(serialize, exclude, kwargs)
+ util.to_disk(path, serialize, exclude)
+
+ def from_disk(self, path, exclude=tuple(), **kwargs):
+ def load_model(p):
+ if self.model is True:
+ self.model = self.Model(**self.cfg)
+ self.model.from_bytes(p.open("rb").read())
+
+ def load_kb(p):
+ kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=self.cfg["entity_width"])
+ kb.load_bulk(p)
+ self.set_kb(kb)
+
+ deserialize = OrderedDict()
+ deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
+ deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
+ deserialize["kb"] = load_kb
+ deserialize["model"] = load_model
+ exclude = util.get_serialization_exclude(deserialize, exclude, kwargs)
+ util.from_disk(path, deserialize, exclude)
+ return self
+
+ def rehearse(self, docs, sgd=None, losses=None, **config):
+ raise NotImplementedError
def add_label(self, label):
- # TODO
- pass
+ raise NotImplementedError
class Sentencizer(object):
diff --git a/spacy/scorer.py b/spacy/scorer.py
index 32716b852..b9994e3f2 100644
--- a/spacy/scorer.py
+++ b/spacy/scorer.py
@@ -52,6 +52,7 @@ class Scorer(object):
self.labelled = PRFScore()
self.tags = PRFScore()
self.ner = PRFScore()
+ self.ner_per_ents = dict()
self.eval_punct = eval_punct
@property
@@ -91,6 +92,15 @@ class Scorer(object):
"""RETURNS (float): Named entity accuracy (F-score)."""
return self.ner.fscore * 100
+ @property
+ def ents_per_type(self):
+ """RETURNS (dict): Scores per entity label.
+ """
+ return {
+ k: {"p": v.precision * 100, "r": v.recall * 100, "f": v.fscore * 100}
+ for k, v in self.ner_per_ents.items()
+ }
+
@property
def scores(self):
"""RETURNS (dict): All scores with keys `uas`, `las`, `ents_p`,
@@ -102,6 +112,7 @@ class Scorer(object):
"ents_p": self.ents_p,
"ents_r": self.ents_r,
"ents_f": self.ents_f,
+ "ents_per_type": self.ents_per_type,
"tags_acc": self.tags_acc,
"token_acc": self.token_acc,
}
@@ -149,13 +160,31 @@ class Scorer(object):
cand_deps.add((gold_i, gold_head, token.dep_.lower()))
if "-" not in [token[-1] for token in gold.orig_annot]:
cand_ents = set()
+ current_ent = {k.label_: set() for k in doc.ents}
+ current_gold = {k.label_: set() for k in doc.ents}
for ent in doc.ents:
+ if ent.label_ not in self.ner_per_ents:
+ self.ner_per_ents[ent.label_] = PRFScore()
first = gold.cand_to_gold[ent.start]
last = gold.cand_to_gold[ent.end - 1]
if first is None or last is None:
self.ner.fp += 1
+ self.ner_per_ents[ent.label_].fp += 1
else:
cand_ents.add((ent.label_, first, last))
+ current_ent[ent.label_].add(
+ tuple(x for x in cand_ents if x[0] == ent.label_)
+ )
+ current_gold[ent.label_].add(
+ tuple(x for x in gold_ents if x[0] == ent.label_)
+ )
+ # Scores per ent
+ [
+ v.score_set(current_ent[k], current_gold[k])
+ for k, v in self.ner_per_ents.items()
+ if k in current_ent
+ ]
+ # Score for all ents
self.ner.score_set(cand_ents, gold_ents)
self.tags.score_set(cand_tags, gold_tags)
self.labelled.score_set(cand_deps, gold_deps)
diff --git a/spacy/structs.pxd b/spacy/structs.pxd
index 154202c0d..e80b1b4d6 100644
--- a/spacy/structs.pxd
+++ b/spacy/structs.pxd
@@ -3,6 +3,10 @@ from libc.stdint cimport uint8_t, uint32_t, int32_t, uint64_t
from .typedefs cimport flags_t, attr_t, hash_t
from .parts_of_speech cimport univ_pos_t
+from libcpp.vector cimport vector
+from libc.stdint cimport int32_t, int64_t
+
+
cdef struct LexemeC:
flags_t flags
@@ -72,3 +76,32 @@ cdef struct TokenC:
attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
attr_t ent_kb_id
hash_t ent_id
+
+
+# Internal struct, for storage and disambiguation of entities.
+cdef struct KBEntryC:
+
+ # The hash of this entry's unique ID/name in the kB
+ hash_t entity_hash
+
+ # Allows retrieval of the entity vector, as an index into a vectors table of the KB.
+ # Can be expanded later to refer to multiple rows (compositional model to reduce storage footprint).
+ int32_t vector_index
+
+ # Allows retrieval of a struct of non-vector features.
+ # This is currently not implemented and set to -1 for the common case where there are no features.
+ int32_t feats_row
+
+ # log probability of entity, based on corpus frequency
+ float prob
+
+
+# Each alias struct stores a list of Entry pointers with their prior probabilities
+# for this specific mention/alias.
+cdef struct AliasC:
+
+ # All entry candidates for this alias
+ vector[int64_t] entry_indices
+
+ # Prior probability P(entity|alias) - should sum up to (at most) 1.
+ vector[float] probs
diff --git a/spacy/symbols.pxd b/spacy/symbols.pxd
index 051b92edb..5922ee588 100644
--- a/spacy/symbols.pxd
+++ b/spacy/symbols.pxd
@@ -460,3 +460,5 @@ cdef enum symbol_t:
xcomp
acl
+
+ ENT_KB_ID
diff --git a/spacy/symbols.pyx b/spacy/symbols.pyx
index 949621820..b65ae9628 100644
--- a/spacy/symbols.pyx
+++ b/spacy/symbols.pyx
@@ -86,6 +86,7 @@ IDS = {
"DEP": DEP,
"ENT_IOB": ENT_IOB,
"ENT_TYPE": ENT_TYPE,
+ "ENT_KB_ID": ENT_KB_ID,
"HEAD": HEAD,
"SENT_START": SENT_START,
"SPACY": SPACY,
diff --git a/spacy/tests/conftest.py b/spacy/tests/conftest.py
index 4bef85a1b..fdd86616d 100644
--- a/spacy/tests/conftest.py
+++ b/spacy/tests/conftest.py
@@ -124,6 +124,12 @@ def ja_tokenizer():
return get_lang_class("ja").Defaults.create_tokenizer()
+@pytest.fixture(scope="session")
+def ko_tokenizer():
+ pytest.importorskip("natto")
+ return get_lang_class("ko").Defaults.create_tokenizer()
+
+
@pytest.fixture(scope="session")
def lt_tokenizer():
return get_lang_class("lt").Defaults.create_tokenizer()
diff --git a/spacy/tests/lang/ko/__init__.py b/spacy/tests/lang/ko/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/spacy/tests/lang/ko/test_lemmatization.py b/spacy/tests/lang/ko/test_lemmatization.py
new file mode 100644
index 000000000..42c306c11
--- /dev/null
+++ b/spacy/tests/lang/ko/test_lemmatization.py
@@ -0,0 +1,12 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+
+@pytest.mark.parametrize(
+ "word,lemma", [("새로운", "새롭"), ("빨간", "빨갛"), ("클수록", "크"), ("뭡니까", "뭣"), ("됐다", "되")]
+)
+def test_ko_lemmatizer_assigns(ko_tokenizer, word, lemma):
+ test_lemma = ko_tokenizer(word)[0].lemma_
+ assert test_lemma == lemma
diff --git a/spacy/tests/lang/ko/test_tokenizer.py b/spacy/tests/lang/ko/test_tokenizer.py
new file mode 100644
index 000000000..cc7b5fd77
--- /dev/null
+++ b/spacy/tests/lang/ko/test_tokenizer.py
@@ -0,0 +1,46 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+# fmt: off
+TOKENIZER_TESTS = [("서울 타워 근처에 살고 있습니다.", "서울 타워 근처 에 살 고 있 습니다 ."),
+ ("영등포구에 있는 맛집 좀 알려주세요.", "영등포구 에 있 는 맛집 좀 알려 주 세요 .")]
+
+TAG_TESTS = [("서울 타워 근처에 살고 있습니다.",
+ "NNP NNG NNG JKB VV EC VX EF SF"),
+ ("영등포구에 있는 맛집 좀 알려주세요.",
+ "NNP JKB VV ETM NNG MAG VV VX EP SF")]
+
+FULL_TAG_TESTS = [("영등포구에 있는 맛집 좀 알려주세요.",
+ "NNP JKB VV ETM NNG MAG VV+EC VX EP+EF SF")]
+
+POS_TESTS = [("서울 타워 근처에 살고 있습니다.",
+ "PROPN NOUN NOUN ADP VERB X AUX X PUNCT"),
+ ("영등포구에 있는 맛집 좀 알려주세요.",
+ "PROPN ADP VERB X NOUN ADV VERB AUX X PUNCT")]
+# fmt: on
+
+
+@pytest.mark.parametrize("text,expected_tokens", TOKENIZER_TESTS)
+def test_ko_tokenizer(ko_tokenizer, text, expected_tokens):
+ tokens = [token.text for token in ko_tokenizer(text)]
+ assert tokens == expected_tokens.split()
+
+
+@pytest.mark.parametrize("text,expected_tags", TAG_TESTS)
+def test_ko_tokenizer_tags(ko_tokenizer, text, expected_tags):
+ tags = [token.tag_ for token in ko_tokenizer(text)]
+ assert tags == expected_tags.split()
+
+
+@pytest.mark.parametrize("text,expected_tags", FULL_TAG_TESTS)
+def test_ko_tokenizer_full_tags(ko_tokenizer, text, expected_tags):
+ tags = ko_tokenizer(text).user_data["full_tags"]
+ assert tags == expected_tags.split()
+
+
+@pytest.mark.parametrize("text,expected_pos", POS_TESTS)
+def test_ko_tokenizer_pos(ko_tokenizer, text, expected_pos):
+ pos = [token.pos_ for token in ko_tokenizer(text)]
+ assert pos == expected_pos.split()
diff --git a/spacy/tests/lang/lt/test_text.py b/spacy/tests/lang/lt/test_text.py
index d2550067b..cac32aa4d 100644
--- a/spacy/tests/lang/lt/test_text.py
+++ b/spacy/tests/lang/lt/test_text.py
@@ -5,16 +5,24 @@ import pytest
def test_lt_tokenizer_handles_long_text(lt_tokenizer):
- text = """Tokios sausros kriterijus atitinka pirmadienį atlikti skaičiavimai, palyginus faktinį ir žemiausią
-vidutinį daugiametį vandens lygį. Nustatyta, kad iš 48 šalies vandens matavimo stočių 28-iose stotyse vandens lygis
-yra žemesnis arba lygus žemiausiam vidutiniam daugiamečiam šiltojo laikotarpio vandens lygiui."""
- tokens = lt_tokenizer(text.replace("\n", ""))
+ text = """Tokios sausros kriterijus atitinka pirmadienį atlikti skaičiavimai, palyginus faktinį ir žemiausią vidutinį daugiametį vandens lygį. Nustatyta, kad iš 48 šalies vandens matavimo stočių 28-iose stotyse vandens lygis yra žemesnis arba lygus žemiausiam vidutiniam daugiamečiam šiltojo laikotarpio vandens lygiui."""
+ tokens = lt_tokenizer(text)
assert len(tokens) == 42
-@pytest.mark.parametrize('text,length', [
- ("177R Parodų rūmai–Ozo g. nuo vasario 18 d. bus skelbiamas interneto tinklalapyje.", 15),
- ("ISM universiteto doc. dr. Ieva Augutytė-Kvedaravičienė pastebi, kad tyrimais nustatyti elgesio pokyčiai.", 16)])
+@pytest.mark.parametrize(
+ "text,length",
+ [
+ (
+ "177R Parodų rūmai–Ozo g. nuo vasario 18 d. bus skelbiamas interneto tinklalapyje.",
+ 15,
+ ),
+ (
+ "ISM universiteto doc. dr. Ieva Augutytė-Kvedaravičienė pastebi, kad tyrimais nustatyti elgesio pokyčiai.",
+ 16,
+ ),
+ ],
+)
def test_lt_tokenizer_handles_punct_abbrev(lt_tokenizer, text, length):
tokens = lt_tokenizer(text)
assert len(tokens) == length
@@ -26,18 +34,22 @@ def test_lt_tokenizer_abbrev_exceptions(lt_tokenizer, text):
assert len(tokens) == 1
-@pytest.mark.parametrize("text,match", [
- ("10", True),
- ("1", True),
- ("10,000", True),
- ("10,00", True),
- ("999.0", True),
- ("vienas", True),
- ("du", True),
- ("milijardas", True),
- ("šuo", False),
- (",", False),
- ("1/2", True)])
+@pytest.mark.parametrize(
+ "text,match",
+ [
+ ("10", True),
+ ("1", True),
+ ("10,000", True),
+ ("10,00", True),
+ ("999.0", True),
+ ("vienas", True),
+ ("du", True),
+ ("milijardas", True),
+ ("šuo", False),
+ (",", False),
+ ("1/2", True),
+ ],
+)
def test_lt_lex_attrs_like_number(lt_tokenizer, text, match):
tokens = lt_tokenizer(text)
assert len(tokens) == 1
diff --git a/spacy/tests/matcher/test_matcher_api.py b/spacy/tests/matcher/test_matcher_api.py
index 54ddd6789..013700d52 100644
--- a/spacy/tests/matcher/test_matcher_api.py
+++ b/spacy/tests/matcher/test_matcher_api.py
@@ -5,7 +5,6 @@ import pytest
import re
from spacy.matcher import Matcher, DependencyMatcher
from spacy.tokens import Doc, Token
-from ..util import get_doc
@pytest.fixture
@@ -288,24 +287,43 @@ def deps():
def dependency_matcher(en_vocab):
def is_brown_yellow(text):
return bool(re.compile(r"brown|yellow|over").match(text))
+
IS_BROWN_YELLOW = en_vocab.add_flag(is_brown_yellow)
pattern1 = [
{"SPEC": {"NODE_NAME": "fox"}, "PATTERN": {"ORTH": "fox"}},
- {"SPEC": {"NODE_NAME": "q", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},"PATTERN": {"ORTH": "quick", "DEP": "amod"}},
- {"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">", "NBOR_NAME": "fox"}, "PATTERN": {IS_BROWN_YELLOW: True}},
+ {
+ "SPEC": {"NODE_NAME": "q", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},
+ "PATTERN": {"ORTH": "quick", "DEP": "amod"},
+ },
+ {
+ "SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">", "NBOR_NAME": "fox"},
+ "PATTERN": {IS_BROWN_YELLOW: True},
+ },
]
pattern2 = [
{"SPEC": {"NODE_NAME": "jumped"}, "PATTERN": {"ORTH": "jumped"}},
- {"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
- {"SPEC": {"NODE_NAME": "quick", "NBOR_RELOP": ".", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}}
+ {
+ "SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"},
+ "PATTERN": {"ORTH": "fox"},
+ },
+ {
+ "SPEC": {"NODE_NAME": "quick", "NBOR_RELOP": ".", "NBOR_NAME": "jumped"},
+ "PATTERN": {"ORTH": "fox"},
+ },
]
pattern3 = [
{"SPEC": {"NODE_NAME": "jumped"}, "PATTERN": {"ORTH": "jumped"}},
- {"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
- {"SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">>", "NBOR_NAME": "fox"}, "PATTERN": {"ORTH": "brown"}}
+ {
+ "SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"},
+ "PATTERN": {"ORTH": "fox"},
+ },
+ {
+ "SPEC": {"NODE_NAME": "r", "NBOR_RELOP": ">>", "NBOR_NAME": "fox"},
+ "PATTERN": {"ORTH": "brown"},
+ },
]
matcher = DependencyMatcher(en_vocab)
@@ -320,9 +338,9 @@ def test_dependency_matcher_compile(dependency_matcher):
assert len(dependency_matcher) == 3
-def test_dependency_matcher(dependency_matcher, text, heads, deps):
- doc = get_doc(dependency_matcher.vocab, text.split(), heads=heads, deps=deps)
- matches = dependency_matcher(doc)
- # assert matches[0][1] == [[3, 1, 2]]
- # assert matches[1][1] == [[4, 3, 3]]
- # assert matches[2][1] == [[4, 3, 2]]
+# def test_dependency_matcher(dependency_matcher, text, heads, deps):
+# doc = get_doc(dependency_matcher.vocab, text.split(), heads=heads, deps=deps)
+# matches = dependency_matcher(doc)
+# assert matches[0][1] == [[3, 1, 2]]
+# assert matches[1][1] == [[4, 3, 3]]
+# assert matches[2][1] == [[4, 3, 2]]
diff --git a/spacy/tests/pipeline/test_el.py b/spacy/tests/pipeline/test_el.py
deleted file mode 100644
index 61baece68..000000000
--- a/spacy/tests/pipeline/test_el.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-import pytest
-
-from spacy.kb import KnowledgeBase
-from spacy.lang.en import English
-
-
-@pytest.fixture
-def nlp():
- return English()
-
-
-def test_kb_valid_entities(nlp):
- """Test the valid construction of a KB with 3 entities and two aliases"""
- mykb = KnowledgeBase(nlp.vocab)
-
- # adding entities
- mykb.add_entity(entity=u'Q1', prob=0.9)
- mykb.add_entity(entity=u'Q2')
- mykb.add_entity(entity=u'Q3', prob=0.5)
-
- # adding aliases
- mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.8, 0.2])
- mykb.add_alias(alias=u'adam', entities=[u'Q2'], probabilities=[0.9])
-
- # test the size of the corresponding KB
- assert(mykb.get_size_entities() == 3)
- assert(mykb.get_size_aliases() == 2)
-
-
-def test_kb_invalid_entities(nlp):
- """Test the invalid construction of a KB with an alias linked to a non-existing entity"""
- mykb = KnowledgeBase(nlp.vocab)
-
- # adding entities
- mykb.add_entity(entity=u'Q1', prob=0.9)
- mykb.add_entity(entity=u'Q2', prob=0.2)
- mykb.add_entity(entity=u'Q3', prob=0.5)
-
- # adding aliases - should fail because one of the given IDs is not valid
- with pytest.raises(ValueError):
- mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q342'], probabilities=[0.8, 0.2])
-
-
-def test_kb_invalid_probabilities(nlp):
- """Test the invalid construction of a KB with wrong prior probabilities"""
- mykb = KnowledgeBase(nlp.vocab)
-
- # adding entities
- mykb.add_entity(entity=u'Q1', prob=0.9)
- mykb.add_entity(entity=u'Q2', prob=0.2)
- mykb.add_entity(entity=u'Q3', prob=0.5)
-
- # adding aliases - should fail because the sum of the probabilities exceeds 1
- with pytest.raises(ValueError):
- mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.8, 0.4])
-
-
-def test_kb_invalid_combination(nlp):
- """Test the invalid construction of a KB with non-matching entity and probability lists"""
- mykb = KnowledgeBase(nlp.vocab)
-
- # adding entities
- mykb.add_entity(entity=u'Q1', prob=0.9)
- mykb.add_entity(entity=u'Q2', prob=0.2)
- mykb.add_entity(entity=u'Q3', prob=0.5)
-
- # adding aliases - should fail because the entities and probabilities vectors are not of equal length
- with pytest.raises(ValueError):
- mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.3, 0.4, 0.1])
-
-
-def test_candidate_generation(nlp):
- """Test correct candidate generation"""
- mykb = KnowledgeBase(nlp.vocab)
-
- # adding entities
- mykb.add_entity(entity=u'Q1', prob=0.9)
- mykb.add_entity(entity=u'Q2', prob=0.2)
- mykb.add_entity(entity=u'Q3', prob=0.5)
-
- # adding aliases
- mykb.add_alias(alias=u'douglas', entities=[u'Q2', u'Q3'], probabilities=[0.8, 0.2])
- mykb.add_alias(alias=u'adam', entities=[u'Q2'], probabilities=[0.9])
-
- # test the size of the relevant candidates
- assert(len(mykb.get_candidates(u'douglas')) == 2)
- assert(len(mykb.get_candidates(u'adam')) == 1)
- assert(len(mykb.get_candidates(u'shrubbery')) == 0)
diff --git a/spacy/tests/pipeline/test_entity_linker.py b/spacy/tests/pipeline/test_entity_linker.py
new file mode 100644
index 000000000..cafc380ba
--- /dev/null
+++ b/spacy/tests/pipeline/test_entity_linker.py
@@ -0,0 +1,145 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+import pytest
+
+from spacy.kb import KnowledgeBase
+from spacy.lang.en import English
+from spacy.pipeline import EntityRuler
+
+
+@pytest.fixture
+def nlp():
+ return English()
+
+
+def test_kb_valid_entities(nlp):
+ """Test the valid construction of a KB with 3 entities and two aliases"""
+ mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+ # adding entities
+ mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+ mykb.add_entity(entity='Q2', prob=0.5, entity_vector=[2])
+ mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+ # adding aliases
+ mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
+ mykb.add_alias(alias='adam', entities=['Q2'], probabilities=[0.9])
+
+ # test the size of the corresponding KB
+ assert(mykb.get_size_entities() == 3)
+ assert(mykb.get_size_aliases() == 2)
+
+
+def test_kb_invalid_entities(nlp):
+ """Test the invalid construction of a KB with an alias linked to a non-existing entity"""
+ mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+ # adding entities
+ mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+ mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+ mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+ # adding aliases - should fail because one of the given IDs is not valid
+ with pytest.raises(ValueError):
+ mykb.add_alias(alias='douglas', entities=['Q2', 'Q342'], probabilities=[0.8, 0.2])
+
+
+def test_kb_invalid_probabilities(nlp):
+ """Test the invalid construction of a KB with wrong prior probabilities"""
+ mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+ # adding entities
+ mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+ mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+ mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+ # adding aliases - should fail because the sum of the probabilities exceeds 1
+ with pytest.raises(ValueError):
+ mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.4])
+
+
+def test_kb_invalid_combination(nlp):
+ """Test the invalid construction of a KB with non-matching entity and probability lists"""
+ mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+ # adding entities
+ mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+ mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+ mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+ # adding aliases - should fail because the entities and probabilities vectors are not of equal length
+ with pytest.raises(ValueError):
+ mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.3, 0.4, 0.1])
+
+
+def test_kb_invalid_entity_vector(nlp):
+ """Test the invalid construction of a KB with non-matching entity vector lengths"""
+ mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
+
+ # adding entities
+ mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1, 2, 3])
+
+ # this should fail because the kb's expected entity vector length is 3
+ with pytest.raises(ValueError):
+ mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+
+
+def test_candidate_generation(nlp):
+ """Test correct candidate generation"""
+ mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+ # adding entities
+ mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+ mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
+ mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
+
+ # adding aliases
+ mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
+ mykb.add_alias(alias='adam', entities=['Q2'], probabilities=[0.9])
+
+ # test the size of the relevant candidates
+ assert(len(mykb.get_candidates('douglas')) == 2)
+ assert(len(mykb.get_candidates('adam')) == 1)
+ assert(len(mykb.get_candidates('shrubbery')) == 0)
+
+
+def test_preserving_links_asdoc(nlp):
+ """Test that Span.as_doc preserves the existing entity links"""
+ mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
+
+ # adding entities
+ mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
+ mykb.add_entity(entity='Q2', prob=0.8, entity_vector=[1])
+
+ # adding aliases
+ mykb.add_alias(alias='Boston', entities=['Q1'], probabilities=[0.7])
+ mykb.add_alias(alias='Denver', entities=['Q2'], probabilities=[0.6])
+
+ # set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained)
+ sentencizer = nlp.create_pipe("sentencizer")
+ nlp.add_pipe(sentencizer)
+
+ ruler = EntityRuler(nlp)
+ patterns = [{"label": "GPE", "pattern": "Boston"},
+ {"label": "GPE", "pattern": "Denver"}]
+ ruler.add_patterns(patterns)
+ nlp.add_pipe(ruler)
+
+ el_pipe = nlp.create_pipe(name='entity_linker', config={"context_width": 64})
+ el_pipe.set_kb(mykb)
+ el_pipe.begin_training()
+ el_pipe.context_weight = 0
+ el_pipe.prior_weight = 1
+ nlp.add_pipe(el_pipe, last=True)
+
+ # test whether the entity links are preserved by the `as_doc()` function
+ text = "She lives in Boston. He lives in Denver."
+ doc = nlp(text)
+ for ent in doc.ents:
+ orig_text = ent.text
+ orig_kb_id = ent.kb_id_
+ sent_doc = ent.sent.as_doc()
+ for s_ent in sent_doc.ents:
+ if s_ent.text == orig_text:
+ assert s_ent.kb_id_ == orig_kb_id
diff --git a/spacy/tests/pipeline/test_entity_ruler.py b/spacy/tests/pipeline/test_entity_ruler.py
index a371be38b..5ab1a3af0 100644
--- a/spacy/tests/pipeline/test_entity_ruler.py
+++ b/spacy/tests/pipeline/test_entity_ruler.py
@@ -111,7 +111,7 @@ def test_entity_ruler_serialize_bytes(nlp, patterns):
assert len(new_ruler.patterns) == len(ruler.patterns)
for pattern in ruler.patterns:
assert pattern in new_ruler.patterns
- assert new_ruler.labels == ruler.labels
+ assert sorted(new_ruler.labels) == sorted(ruler.labels)
def test_entity_ruler_serialize_phrase_matcher_attr_bytes(nlp, patterns):
diff --git a/spacy/tests/regression/test_issue2001-2500.py b/spacy/tests/regression/test_issue2001-2500.py
index 82b3a81a9..4292c8d23 100644
--- a/spacy/tests/regression/test_issue2001-2500.py
+++ b/spacy/tests/regression/test_issue2001-2500.py
@@ -4,6 +4,7 @@ from __future__ import unicode_literals
import pytest
import numpy
from spacy.tokens import Doc
+from spacy.matcher import Matcher
from spacy.displacy import render
from spacy.gold import iob_to_biluo
from spacy.lang.it import Italian
@@ -123,6 +124,15 @@ def test_issue2396(en_vocab):
assert (span.get_lca_matrix() == matrix).all()
+def test_issue2464(en_vocab):
+ """Test problem with successive ?. This is the same bug, so putting it here."""
+ matcher = Matcher(en_vocab)
+ doc = Doc(en_vocab, words=["a", "b"])
+ matcher.add("4", None, [{"OP": "?"}, {"OP": "?"}])
+ matches = matcher(doc)
+ assert len(matches) == 3
+
+
def test_issue2482():
"""Test we can serialize and deserialize a blank NER or parser model."""
nlp = Italian()
diff --git a/spacy/tests/regression/test_issue3001-3500.py b/spacy/tests/regression/test_issue3001-3500.py
new file mode 100644
index 000000000..3b0c2f1ed
--- /dev/null
+++ b/spacy/tests/regression/test_issue3001-3500.py
@@ -0,0 +1,334 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import pytest
+from spacy.lang.en import English
+from spacy.lang.de import German
+from spacy.pipeline import EntityRuler, EntityRecognizer
+from spacy.matcher import Matcher, PhraseMatcher
+from spacy.tokens import Doc
+from spacy.vocab import Vocab
+from spacy.attrs import ENT_IOB, ENT_TYPE
+from spacy.compat import pickle, is_python2, unescape_unicode
+from spacy import displacy
+from spacy.util import decaying
+import numpy
+import re
+
+from ..util import get_doc
+
+
+def test_issue3002():
+ """Test that the tokenizer doesn't hang on a long list of dots"""
+ nlp = German()
+ doc = nlp(
+ "880.794.982.218.444.893.023.439.794.626.120.190.780.624.990.275.671 ist eine lange Zahl"
+ )
+ assert len(doc) == 5
+
+
+def test_issue3009(en_vocab):
+ """Test problem with matcher quantifiers"""
+ patterns = [
+ [{"LEMMA": "have"}, {"LOWER": "to"}, {"LOWER": "do"}, {"POS": "ADP"}],
+ [
+ {"LEMMA": "have"},
+ {"IS_ASCII": True, "IS_PUNCT": False, "OP": "*"},
+ {"LOWER": "to"},
+ {"LOWER": "do"},
+ {"POS": "ADP"},
+ ],
+ [
+ {"LEMMA": "have"},
+ {"IS_ASCII": True, "IS_PUNCT": False, "OP": "?"},
+ {"LOWER": "to"},
+ {"LOWER": "do"},
+ {"POS": "ADP"},
+ ],
+ ]
+ words = ["also", "has", "to", "do", "with"]
+ tags = ["RB", "VBZ", "TO", "VB", "IN"]
+ doc = get_doc(en_vocab, words=words, tags=tags)
+ matcher = Matcher(en_vocab)
+ for i, pattern in enumerate(patterns):
+ matcher.add(str(i), None, pattern)
+ matches = matcher(doc)
+ assert matches
+
+
+def test_issue3012(en_vocab):
+ """Test that the is_tagged attribute doesn't get overwritten when we from_array
+ without tag information."""
+ words = ["This", "is", "10", "%", "."]
+ tags = ["DT", "VBZ", "CD", "NN", "."]
+ pos = ["DET", "VERB", "NUM", "NOUN", "PUNCT"]
+ ents = [(2, 4, "PERCENT")]
+ doc = get_doc(en_vocab, words=words, tags=tags, pos=pos, ents=ents)
+ assert doc.is_tagged
+
+ expected = ("10", "NUM", "CD", "PERCENT")
+ assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
+
+ header = [ENT_IOB, ENT_TYPE]
+ ent_array = doc.to_array(header)
+ doc.from_array(header, ent_array)
+
+ assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
+
+ # Serializing then deserializing
+ doc_bytes = doc.to_bytes()
+ doc2 = Doc(en_vocab).from_bytes(doc_bytes)
+ assert (doc2[2].text, doc2[2].pos_, doc2[2].tag_, doc2[2].ent_type_) == expected
+
+
+def test_issue3199():
+ """Test that Span.noun_chunks works correctly if no noun chunks iterator
+ is available. To make this test future-proof, we're constructing a Doc
+ with a new Vocab here and setting is_parsed to make sure the noun chunks run.
+ """
+ doc = Doc(Vocab(), words=["This", "is", "a", "sentence"])
+ doc.is_parsed = True
+ assert list(doc[0:3].noun_chunks) == []
+
+
+def test_issue3209():
+ """Test issue that occurred in spaCy nightly where NER labels were being
+ mapped to classes incorrectly after loading the model, when the labels
+ were added using ner.add_label().
+ """
+ nlp = English()
+ ner = nlp.create_pipe("ner")
+ nlp.add_pipe(ner)
+
+ ner.add_label("ANIMAL")
+ nlp.begin_training()
+ move_names = ["O", "B-ANIMAL", "I-ANIMAL", "L-ANIMAL", "U-ANIMAL"]
+ assert ner.move_names == move_names
+ nlp2 = English()
+ nlp2.add_pipe(nlp2.create_pipe("ner"))
+ nlp2.from_bytes(nlp.to_bytes())
+ assert nlp2.get_pipe("ner").move_names == move_names
+
+
+def test_issue3248_1():
+ """Test that the PhraseMatcher correctly reports its number of rules, not
+ total number of patterns."""
+ nlp = English()
+ matcher = PhraseMatcher(nlp.vocab)
+ matcher.add("TEST1", None, nlp("a"), nlp("b"), nlp("c"))
+ matcher.add("TEST2", None, nlp("d"))
+ assert len(matcher) == 2
+
+
+def test_issue3248_2():
+ """Test that the PhraseMatcher can be pickled correctly."""
+ nlp = English()
+ matcher = PhraseMatcher(nlp.vocab)
+ matcher.add("TEST1", None, nlp("a"), nlp("b"), nlp("c"))
+ matcher.add("TEST2", None, nlp("d"))
+ data = pickle.dumps(matcher)
+ new_matcher = pickle.loads(data)
+ assert len(new_matcher) == len(matcher)
+
+
+def test_issue3277(es_tokenizer):
+ """Test that hyphens are split correctly as prefixes."""
+ doc = es_tokenizer("—Yo me llamo... –murmuró el niño– Emilio Sánchez Pérez.")
+ assert len(doc) == 14
+ assert doc[0].text == "\u2014"
+ assert doc[5].text == "\u2013"
+ assert doc[9].text == "\u2013"
+
+
+def test_issue3288(en_vocab):
+ """Test that retokenization works correctly via displaCy when punctuation
+ is merged onto the preceeding token and tensor is resized."""
+ words = ["Hello", "World", "!", "When", "is", "this", "breaking", "?"]
+ heads = [1, 0, -1, 1, 0, 1, -2, -3]
+ deps = ["intj", "ROOT", "punct", "advmod", "ROOT", "det", "nsubj", "punct"]
+ doc = get_doc(en_vocab, words=words, heads=heads, deps=deps)
+ doc.tensor = numpy.zeros((len(words), 96), dtype="float32")
+ displacy.render(doc)
+
+
+def test_issue3289():
+ """Test that Language.to_bytes handles serializing a pipeline component
+ with an uninitialized model."""
+ nlp = English()
+ nlp.add_pipe(nlp.create_pipe("textcat"))
+ bytes_data = nlp.to_bytes()
+ new_nlp = English()
+ new_nlp.add_pipe(nlp.create_pipe("textcat"))
+ new_nlp.from_bytes(bytes_data)
+
+
+def test_issue3328(en_vocab):
+ doc = Doc(en_vocab, words=["Hello", ",", "how", "are", "you", "doing", "?"])
+ matcher = Matcher(en_vocab)
+ patterns = [
+ [{"LOWER": {"IN": ["hello", "how"]}}],
+ [{"LOWER": {"IN": ["you", "doing"]}}],
+ ]
+ matcher.add("TEST", None, *patterns)
+ matches = matcher(doc)
+ assert len(matches) == 4
+ matched_texts = [doc[start:end].text for _, start, end in matches]
+ assert matched_texts == ["Hello", "how", "you", "doing"]
+
+
+@pytest.mark.xfail
+def test_issue3331(en_vocab):
+ """Test that duplicate patterns for different rules result in multiple
+ matches, one per rule.
+ """
+ matcher = PhraseMatcher(en_vocab)
+ matcher.add("A", None, Doc(en_vocab, words=["Barack", "Obama"]))
+ matcher.add("B", None, Doc(en_vocab, words=["Barack", "Obama"]))
+ doc = Doc(en_vocab, words=["Barack", "Obama", "lifts", "America"])
+ matches = matcher(doc)
+ assert len(matches) == 2
+ match_ids = [en_vocab.strings[matches[0][0]], en_vocab.strings[matches[1][0]]]
+ assert sorted(match_ids) == ["A", "B"]
+
+
+def test_issue3345():
+ """Test case where preset entity crosses sentence boundary."""
+ nlp = English()
+ doc = Doc(nlp.vocab, words=["I", "live", "in", "New", "York"])
+ doc[4].is_sent_start = True
+ ruler = EntityRuler(nlp, patterns=[{"label": "GPE", "pattern": "New York"}])
+ ner = EntityRecognizer(doc.vocab)
+ # Add the OUT action. I wouldn't have thought this would be necessary...
+ ner.moves.add_action(5, "")
+ ner.add_label("GPE")
+ doc = ruler(doc)
+ # Get into the state just before "New"
+ state = ner.moves.init_batch([doc])[0]
+ ner.moves.apply_transition(state, "O")
+ ner.moves.apply_transition(state, "O")
+ ner.moves.apply_transition(state, "O")
+ # Check that B-GPE is valid.
+ assert ner.moves.is_valid(state, "B-GPE")
+
+
+if is_python2:
+ # If we have this test in Python 3, pytest chokes, as it can't print the
+ # string above in the xpass message.
+ prefix_search = (
+ b"^\xc2\xa7|^%|^=|^\xe2\x80\x94|^\xe2\x80\x93|^\\+(?![0-9])"
+ b"|^\xe2\x80\xa6|^\xe2\x80\xa6\xe2\x80\xa6|^,|^:|^;|^\\!|^\\?"
+ b"|^\xc2\xbf|^\xd8\x9f|^\xc2\xa1|^\\(|^\\)|^\\[|^\\]|^\\{|^\\}"
+ b"|^<|^>|^_|^#|^\\*|^&|^\xe3\x80\x82|^\xef\xbc\x9f|^\xef\xbc\x81|"
+ b"^\xef\xbc\x8c|^\xe3\x80\x81|^\xef\xbc\x9b|^\xef\xbc\x9a|"
+ b"^\xef\xbd\x9e|^\xc2\xb7|^\xe0\xa5\xa4|^\xd8\x8c|^\xd8\x9b|"
+ b"^\xd9\xaa|^\\.\\.+|^\xe2\x80\xa6|^\\'|^\"|^\xe2\x80\x9d|"
+ b"^\xe2\x80\x9c|^`|^\xe2\x80\x98|^\xc2\xb4|^\xe2\x80\x99|"
+ b"^\xe2\x80\x9a|^,|^\xe2\x80\x9e|^\xc2\xbb|^\xc2\xab|^\xe3\x80\x8c|"
+ b"^\xe3\x80\x8d|^\xe3\x80\x8e|^\xe3\x80\x8f|^\xef\xbc\x88|"
+ b"^\xef\xbc\x89|^\xe3\x80\x94|^\xe3\x80\x95|^\xe3\x80\x90|"
+ b"^\xe3\x80\x91|^\xe3\x80\x8a|^\xe3\x80\x8b|^\xe3\x80\x88|"
+ b"^\xe3\x80\x89|^\\$|^\xc2\xa3|^\xe2\x82\xac|^\xc2\xa5|^\xe0\xb8\xbf|"
+ b"^US\\$|^C\\$|^A\\$|^\xe2\x82\xbd|^\xef\xb7\xbc|^\xe2\x82\xb4|"
+ b"^[\\u00A6\\u00A9\\u00AE\\u00B0\\u0482\\u058D\\u058E\\u060E\\u060F"
+ b"\\u06DE\\u06E9\\u06FD\\u06FE\\u07F6\\u09FA\\u0B70\\u0BF3-\\u0BF8"
+ b"\\u0BFA\\u0C7F\\u0D4F\\u0D79\\u0F01-\\u0F03\\u0F13\\u0F15-\\u0F17"
+ b"\\u0F1A-\\u0F1F\\u0F34\\u0F36\\u0F38\\u0FBE-\\u0FC5\\u0FC7-\\u0FCC"
+ b"\\u0FCE\\u0FCF\\u0FD5-\\u0FD8\\u109E\\u109F\\u1390-\\u1399\\u1940"
+ b"\\u19DE-\\u19FF\\u1B61-\\u1B6A\\u1B74-\\u1B7C\\u2100\\u2101\\u2103"
+ b"-\\u2106\\u2108\\u2109\\u2114\\u2116\\u2117\\u211E-\\u2123\\u2125"
+ b"\\u2127\\u2129\\u212E\\u213A\\u213B\\u214A\\u214C\\u214D\\u214F"
+ b"\\u218A\\u218B\\u2195-\\u2199\\u219C-\\u219F\\u21A1\\u21A2\\u21A4"
+ b"\\u21A5\\u21A7-\\u21AD\\u21AF-\\u21CD\\u21D0\\u21D1\\u21D3\\u21D5"
+ b"-\\u21F3\\u2300-\\u2307\\u230C-\\u231F\\u2322-\\u2328\\u232B"
+ b"-\\u237B\\u237D-\\u239A\\u23B4-\\u23DB\\u23E2-\\u2426\\u2440"
+ b"-\\u244A\\u249C-\\u24E9\\u2500-\\u25B6\\u25B8-\\u25C0\\u25C2"
+ b"-\\u25F7\\u2600-\\u266E\\u2670-\\u2767\\u2794-\\u27BF\\u2800"
+ b"-\\u28FF\\u2B00-\\u2B2F\\u2B45\\u2B46\\u2B4D-\\u2B73\\u2B76"
+ b"-\\u2B95\\u2B98-\\u2BC8\\u2BCA-\\u2BFE\\u2CE5-\\u2CEA\\u2E80"
+ b"-\\u2E99\\u2E9B-\\u2EF3\\u2F00-\\u2FD5\\u2FF0-\\u2FFB\\u3004"
+ b"\\u3012\\u3013\\u3020\\u3036\\u3037\\u303E\\u303F\\u3190\\u3191"
+ b"\\u3196-\\u319F\\u31C0-\\u31E3\\u3200-\\u321E\\u322A-\\u3247\\u3250"
+ b"\\u3260-\\u327F\\u328A-\\u32B0\\u32C0-\\u32FE\\u3300-\\u33FF\\u4DC0"
+ b"-\\u4DFF\\uA490-\\uA4C6\\uA828-\\uA82B\\uA836\\uA837\\uA839\\uAA77"
+ b"-\\uAA79\\uFDFD\\uFFE4\\uFFE8\\uFFED\\uFFEE\\uFFFC\\uFFFD\\U00010137"
+ b"-\\U0001013F\\U00010179-\\U00010189\\U0001018C-\\U0001018E"
+ b"\\U00010190-\\U0001019B\\U000101A0\\U000101D0-\\U000101FC\\U00010877"
+ b"\\U00010878\\U00010AC8\\U0001173F\\U00016B3C-\\U00016B3F\\U00016B45"
+ b"\\U0001BC9C\\U0001D000-\\U0001D0F5\\U0001D100-\\U0001D126\\U0001D129"
+ b"-\\U0001D164\\U0001D16A-\\U0001D16C\\U0001D183\\U0001D184\\U0001D18C"
+ b"-\\U0001D1A9\\U0001D1AE-\\U0001D1E8\\U0001D200-\\U0001D241\\U0001D245"
+ b"\\U0001D300-\\U0001D356\\U0001D800-\\U0001D9FF\\U0001DA37-\\U0001DA3A"
+ b"\\U0001DA6D-\\U0001DA74\\U0001DA76-\\U0001DA83\\U0001DA85\\U0001DA86"
+ b"\\U0001ECAC\\U0001F000-\\U0001F02B\\U0001F030-\\U0001F093\\U0001F0A0"
+ b"-\\U0001F0AE\\U0001F0B1-\\U0001F0BF\\U0001F0C1-\\U0001F0CF\\U0001F0D1"
+ b"-\\U0001F0F5\\U0001F110-\\U0001F16B\\U0001F170-\\U0001F1AC\\U0001F1E6"
+ b"-\\U0001F202\\U0001F210-\\U0001F23B\\U0001F240-\\U0001F248\\U0001F250"
+ b"\\U0001F251\\U0001F260-\\U0001F265\\U0001F300-\\U0001F3FA\\U0001F400"
+ b"-\\U0001F6D4\\U0001F6E0-\\U0001F6EC\\U0001F6F0-\\U0001F6F9\\U0001F700"
+ b"-\\U0001F773\\U0001F780-\\U0001F7D8\\U0001F800-\\U0001F80B\\U0001F810"
+ b"-\\U0001F847\\U0001F850-\\U0001F859\\U0001F860-\\U0001F887\\U0001F890"
+ b"-\\U0001F8AD\\U0001F900-\\U0001F90B\\U0001F910-\\U0001F93E\\U0001F940"
+ b"-\\U0001F970\\U0001F973-\\U0001F976\\U0001F97A\\U0001F97C-\\U0001F9A2"
+ b"\\U0001F9B0-\\U0001F9B9\\U0001F9C0-\\U0001F9C2\\U0001F9D0-\\U0001F9FF"
+ b"\\U0001FA60-\\U0001FA6D]"
+ )
+
+ def test_issue3356():
+ pattern = re.compile(unescape_unicode(prefix_search.decode("utf8")))
+ assert not pattern.search("hello")
+
+
+def test_issue3410():
+ texts = ["Hello world", "This is a test"]
+ nlp = English()
+ matcher = Matcher(nlp.vocab)
+ phrasematcher = PhraseMatcher(nlp.vocab)
+ with pytest.deprecated_call():
+ docs = list(nlp.pipe(texts, n_threads=4))
+ with pytest.deprecated_call():
+ docs = list(nlp.tokenizer.pipe(texts, n_threads=4))
+ with pytest.deprecated_call():
+ list(matcher.pipe(docs, n_threads=4))
+ with pytest.deprecated_call():
+ list(phrasematcher.pipe(docs, n_threads=4))
+
+
+def test_issue3447():
+ sizes = decaying(10.0, 1.0, 0.5)
+ size = next(sizes)
+ assert size == 10.0
+ size = next(sizes)
+ assert size == 10.0 - 0.5
+ size = next(sizes)
+ assert size == 10.0 - 0.5 - 0.5
+
+
+@pytest.mark.xfail(reason="default suffix rules avoid one upper-case letter before dot")
+def test_issue3449():
+ nlp = English()
+ nlp.add_pipe(nlp.create_pipe("sentencizer"))
+ text1 = "He gave the ball to I. Do you want to go to the movies with I?"
+ text2 = "He gave the ball to I. Do you want to go to the movies with I?"
+ text3 = "He gave the ball to I.\nDo you want to go to the movies with I?"
+ t1 = nlp(text1)
+ t2 = nlp(text2)
+ t3 = nlp(text3)
+ assert t1[5].text == "I"
+ assert t2[5].text == "I"
+ assert t3[5].text == "I"
+
+
+def test_issue3468():
+ """Test that sentence boundaries are set correctly so Doc.is_sentenced can
+ be restored after serialization."""
+ nlp = English()
+ nlp.add_pipe(nlp.create_pipe("sentencizer"))
+ doc = nlp("Hello world")
+ assert doc[0].is_sent_start
+ assert doc.is_sentenced
+ assert len(list(doc.sents)) == 1
+ doc_bytes = doc.to_bytes()
+ new_doc = Doc(nlp.vocab).from_bytes(doc_bytes)
+ assert new_doc[0].is_sent_start
+ assert new_doc.is_sentenced
+ assert len(list(new_doc.sents)) == 1
diff --git a/spacy/tests/regression/test_issue3002.py b/spacy/tests/regression/test_issue3002.py
deleted file mode 100644
index 54e661d1f..000000000
--- a/spacy/tests/regression/test_issue3002.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from spacy.lang.de import German
-
-
-def test_issue3002():
- """Test that the tokenizer doesn't hang on a long list of dots"""
- nlp = German()
- doc = nlp('880.794.982.218.444.893.023.439.794.626.120.190.780.624.990.275.671 ist eine lange Zahl')
- assert len(doc) == 5
diff --git a/spacy/tests/regression/test_issue3009.py b/spacy/tests/regression/test_issue3009.py
deleted file mode 100644
index 25f208903..000000000
--- a/spacy/tests/regression/test_issue3009.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-import pytest
-from spacy.matcher import Matcher
-from spacy.tokens import Doc
-
-
-PATTERNS = [
- ("1", [[{"LEMMA": "have"}, {"LOWER": "to"}, {"LOWER": "do"}, {"POS": "ADP"}]]),
- (
- "2",
- [
- [
- {"LEMMA": "have"},
- {"IS_ASCII": True, "IS_PUNCT": False, "OP": "*"},
- {"LOWER": "to"},
- {"LOWER": "do"},
- {"POS": "ADP"},
- ]
- ],
- ),
- (
- "3",
- [
- [
- {"LEMMA": "have"},
- {"IS_ASCII": True, "IS_PUNCT": False, "OP": "?"},
- {"LOWER": "to"},
- {"LOWER": "do"},
- {"POS": "ADP"},
- ]
- ],
- ),
-]
-
-
-@pytest.fixture
-def doc(en_tokenizer):
- doc = en_tokenizer("also has to do with")
- doc[0].tag_ = "RB"
- doc[1].tag_ = "VBZ"
- doc[2].tag_ = "TO"
- doc[3].tag_ = "VB"
- doc[4].tag_ = "IN"
- return doc
-
-
-@pytest.fixture
-def matcher(en_tokenizer):
- return Matcher(en_tokenizer.vocab)
-
-
-@pytest.mark.parametrize("pattern", PATTERNS)
-def test_issue3009(doc, matcher, pattern):
- """Test problem with matcher quantifiers"""
- matcher.add(pattern[0], None, *pattern[1])
- matches = matcher(doc)
- assert matches
-
-
-def test_issue2464(matcher):
- """Test problem with successive ?. This is the same bug, so putting it here."""
- doc = Doc(matcher.vocab, words=["a", "b"])
- matcher.add("4", None, [{"OP": "?"}, {"OP": "?"}])
- matches = matcher(doc)
- assert len(matches) == 3
diff --git a/spacy/tests/regression/test_issue3012.py b/spacy/tests/regression/test_issue3012.py
deleted file mode 100644
index 8fdc8b318..000000000
--- a/spacy/tests/regression/test_issue3012.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from ...attrs import ENT_IOB, ENT_TYPE
-from ...tokens import Doc
-from ..util import get_doc
-
-
-def test_issue3012(en_vocab):
- """Test that the is_tagged attribute doesn't get overwritten when we from_array
- without tag information."""
- words = ["This", "is", "10", "%", "."]
- tags = ["DT", "VBZ", "CD", "NN", "."]
- pos = ["DET", "VERB", "NUM", "NOUN", "PUNCT"]
- ents = [(2, 4, "PERCENT")]
- doc = get_doc(en_vocab, words=words, tags=tags, pos=pos, ents=ents)
- assert doc.is_tagged
-
- expected = ("10", "NUM", "CD", "PERCENT")
- assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
-
- header = [ENT_IOB, ENT_TYPE]
- ent_array = doc.to_array(header)
- doc.from_array(header, ent_array)
-
- assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
-
- # serializing then deserializing
- doc_bytes = doc.to_bytes()
- doc2 = Doc(en_vocab).from_bytes(doc_bytes)
- assert (doc2[2].text, doc2[2].pos_, doc2[2].tag_, doc2[2].ent_type_) == expected
diff --git a/spacy/tests/regression/test_issue3199.py b/spacy/tests/regression/test_issue3199.py
deleted file mode 100644
index d80a55330..000000000
--- a/spacy/tests/regression/test_issue3199.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from spacy.tokens import Doc
-from spacy.vocab import Vocab
-
-
-def test_issue3199():
- """Test that Span.noun_chunks works correctly if no noun chunks iterator
- is available. To make this test future-proof, we're constructing a Doc
- with a new Vocab here and setting is_parsed to make sure the noun chunks run.
- """
- doc = Doc(Vocab(), words=["This", "is", "a", "sentence"])
- doc.is_parsed = True
- assert list(doc[0:3].noun_chunks) == []
diff --git a/spacy/tests/regression/test_issue3209.py b/spacy/tests/regression/test_issue3209.py
deleted file mode 100644
index 469e38b8c..000000000
--- a/spacy/tests/regression/test_issue3209.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from spacy.lang.en import English
-
-
-def test_issue3209():
- """Test issue that occurred in spaCy nightly where NER labels were being
- mapped to classes incorrectly after loading the model, when the labels
- were added using ner.add_label().
- """
- nlp = English()
- ner = nlp.create_pipe("ner")
- nlp.add_pipe(ner)
-
- ner.add_label("ANIMAL")
- nlp.begin_training()
- move_names = ["O", "B-ANIMAL", "I-ANIMAL", "L-ANIMAL", "U-ANIMAL"]
- assert ner.move_names == move_names
- nlp2 = English()
- nlp2.add_pipe(nlp2.create_pipe("ner"))
- nlp2.from_bytes(nlp.to_bytes())
- assert nlp2.get_pipe("ner").move_names == move_names
diff --git a/spacy/tests/regression/test_issue3248.py b/spacy/tests/regression/test_issue3248.py
deleted file mode 100644
index c4b592f3c..000000000
--- a/spacy/tests/regression/test_issue3248.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-from spacy.matcher import PhraseMatcher
-from spacy.lang.en import English
-from spacy.compat import pickle
-
-
-def test_issue3248_1():
- """Test that the PhraseMatcher correctly reports its number of rules, not
- total number of patterns."""
- nlp = English()
- matcher = PhraseMatcher(nlp.vocab)
- matcher.add("TEST1", None, nlp("a"), nlp("b"), nlp("c"))
- matcher.add("TEST2", None, nlp("d"))
- assert len(matcher) == 2
-
-
-def test_issue3248_2():
- """Test that the PhraseMatcher can be pickled correctly."""
- nlp = English()
- matcher = PhraseMatcher(nlp.vocab)
- matcher.add("TEST1", None, nlp("a"), nlp("b"), nlp("c"))
- matcher.add("TEST2", None, nlp("d"))
- data = pickle.dumps(matcher)
- new_matcher = pickle.loads(data)
- assert len(new_matcher) == len(matcher)
diff --git a/spacy/tests/regression/test_issue3277.py b/spacy/tests/regression/test_issue3277.py
deleted file mode 100644
index 88ea67774..000000000
--- a/spacy/tests/regression/test_issue3277.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-
-def test_issue3277(es_tokenizer):
- """Test that hyphens are split correctly as prefixes."""
- doc = es_tokenizer("—Yo me llamo... –murmuró el niño– Emilio Sánchez Pérez.")
- assert len(doc) == 14
- assert doc[0].text == "\u2014"
- assert doc[5].text == "\u2013"
- assert doc[9].text == "\u2013"
diff --git a/spacy/tests/regression/test_issue3288.py b/spacy/tests/regression/test_issue3288.py
deleted file mode 100644
index 188bf361c..000000000
--- a/spacy/tests/regression/test_issue3288.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-import numpy
-from spacy import displacy
-
-from ..util import get_doc
-
-
-def test_issue3288(en_vocab):
- """Test that retokenization works correctly via displaCy when punctuation
- is merged onto the preceeding token and tensor is resized."""
- words = ["Hello", "World", "!", "When", "is", "this", "breaking", "?"]
- heads = [1, 0, -1, 1, 0, 1, -2, -3]
- deps = ["intj", "ROOT", "punct", "advmod", "ROOT", "det", "nsubj", "punct"]
- doc = get_doc(en_vocab, words=words, heads=heads, deps=deps)
- doc.tensor = numpy.zeros((len(words), 96), dtype="float32")
- displacy.render(doc)
diff --git a/spacy/tests/regression/test_issue3289.py b/spacy/tests/regression/test_issue3289.py
deleted file mode 100644
index 0e64f07ce..000000000
--- a/spacy/tests/regression/test_issue3289.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-from spacy.lang.en import English
-
-
-def test_issue3289():
- """Test that Language.to_bytes handles serializing a pipeline component
- with an uninitialized model."""
- nlp = English()
- nlp.add_pipe(nlp.create_pipe("textcat"))
- bytes_data = nlp.to_bytes()
- new_nlp = English()
- new_nlp.add_pipe(nlp.create_pipe("textcat"))
- new_nlp.from_bytes(bytes_data)
diff --git a/spacy/tests/regression/test_issue3328.py b/spacy/tests/regression/test_issue3328.py
deleted file mode 100644
index c397feebb..000000000
--- a/spacy/tests/regression/test_issue3328.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-from spacy.matcher import Matcher
-from spacy.tokens import Doc
-
-
-def test_issue3328(en_vocab):
- doc = Doc(en_vocab, words=["Hello", ",", "how", "are", "you", "doing", "?"])
- matcher = Matcher(en_vocab)
- patterns = [
- [{"LOWER": {"IN": ["hello", "how"]}}],
- [{"LOWER": {"IN": ["you", "doing"]}}],
- ]
- matcher.add("TEST", None, *patterns)
- matches = matcher(doc)
- assert len(matches) == 4
- matched_texts = [doc[start:end].text for _, start, end in matches]
- assert matched_texts == ["Hello", "how", "you", "doing"]
diff --git a/spacy/tests/regression/test_issue3331.py b/spacy/tests/regression/test_issue3331.py
deleted file mode 100644
index c30712f81..000000000
--- a/spacy/tests/regression/test_issue3331.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-import pytest
-from spacy.matcher import PhraseMatcher
-from spacy.tokens import Doc
-
-
-@pytest.mark.xfail
-def test_issue3331(en_vocab):
- """Test that duplicate patterns for different rules result in multiple
- matches, one per rule.
- """
- matcher = PhraseMatcher(en_vocab)
- matcher.add("A", None, Doc(en_vocab, words=["Barack", "Obama"]))
- matcher.add("B", None, Doc(en_vocab, words=["Barack", "Obama"]))
- doc = Doc(en_vocab, words=["Barack", "Obama", "lifts", "America"])
- matches = matcher(doc)
- assert len(matches) == 2
- match_ids = [en_vocab.strings[matches[0][0]], en_vocab.strings[matches[1][0]]]
- assert sorted(match_ids) == ["A", "B"]
diff --git a/spacy/tests/regression/test_issue3345.py b/spacy/tests/regression/test_issue3345.py
deleted file mode 100644
index c358fd7bc..000000000
--- a/spacy/tests/regression/test_issue3345.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from spacy.lang.en import English
-from spacy.tokens import Doc
-from spacy.pipeline import EntityRuler, EntityRecognizer
-
-
-def test_issue3345():
- """Test case where preset entity crosses sentence boundary."""
- nlp = English()
- doc = Doc(nlp.vocab, words=["I", "live", "in", "New", "York"])
- doc[4].is_sent_start = True
- ruler = EntityRuler(nlp, patterns=[{"label": "GPE", "pattern": "New York"}])
- ner = EntityRecognizer(doc.vocab)
- # Add the OUT action. I wouldn't have thought this would be necessary...
- ner.moves.add_action(5, "")
- ner.add_label("GPE")
- doc = ruler(doc)
- # Get into the state just before "New"
- state = ner.moves.init_batch([doc])[0]
- ner.moves.apply_transition(state, "O")
- ner.moves.apply_transition(state, "O")
- ner.moves.apply_transition(state, "O")
- # Check that B-GPE is valid.
- assert ner.moves.is_valid(state, "B-GPE")
diff --git a/spacy/tests/regression/test_issue3356.py b/spacy/tests/regression/test_issue3356.py
deleted file mode 100644
index f8d16459c..000000000
--- a/spacy/tests/regression/test_issue3356.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-import re
-from spacy import compat
-
-prefix_search = (
- b"^\xc2\xa7|^%|^=|^\xe2\x80\x94|^\xe2\x80\x93|^\\+(?![0-9])"
- b"|^\xe2\x80\xa6|^\xe2\x80\xa6\xe2\x80\xa6|^,|^:|^;|^\\!|^\\?"
- b"|^\xc2\xbf|^\xd8\x9f|^\xc2\xa1|^\\(|^\\)|^\\[|^\\]|^\\{|^\\}"
- b"|^<|^>|^_|^#|^\\*|^&|^\xe3\x80\x82|^\xef\xbc\x9f|^\xef\xbc\x81|"
- b"^\xef\xbc\x8c|^\xe3\x80\x81|^\xef\xbc\x9b|^\xef\xbc\x9a|"
- b"^\xef\xbd\x9e|^\xc2\xb7|^\xe0\xa5\xa4|^\xd8\x8c|^\xd8\x9b|"
- b"^\xd9\xaa|^\\.\\.+|^\xe2\x80\xa6|^\\'|^\"|^\xe2\x80\x9d|"
- b"^\xe2\x80\x9c|^`|^\xe2\x80\x98|^\xc2\xb4|^\xe2\x80\x99|"
- b"^\xe2\x80\x9a|^,|^\xe2\x80\x9e|^\xc2\xbb|^\xc2\xab|^\xe3\x80\x8c|"
- b"^\xe3\x80\x8d|^\xe3\x80\x8e|^\xe3\x80\x8f|^\xef\xbc\x88|"
- b"^\xef\xbc\x89|^\xe3\x80\x94|^\xe3\x80\x95|^\xe3\x80\x90|"
- b"^\xe3\x80\x91|^\xe3\x80\x8a|^\xe3\x80\x8b|^\xe3\x80\x88|"
- b"^\xe3\x80\x89|^\\$|^\xc2\xa3|^\xe2\x82\xac|^\xc2\xa5|^\xe0\xb8\xbf|"
- b"^US\\$|^C\\$|^A\\$|^\xe2\x82\xbd|^\xef\xb7\xbc|^\xe2\x82\xb4|"
- b"^[\\u00A6\\u00A9\\u00AE\\u00B0\\u0482\\u058D\\u058E\\u060E\\u060F"
- b"\\u06DE\\u06E9\\u06FD\\u06FE\\u07F6\\u09FA\\u0B70\\u0BF3-\\u0BF8"
- b"\\u0BFA\\u0C7F\\u0D4F\\u0D79\\u0F01-\\u0F03\\u0F13\\u0F15-\\u0F17"
- b"\\u0F1A-\\u0F1F\\u0F34\\u0F36\\u0F38\\u0FBE-\\u0FC5\\u0FC7-\\u0FCC"
- b"\\u0FCE\\u0FCF\\u0FD5-\\u0FD8\\u109E\\u109F\\u1390-\\u1399\\u1940"
- b"\\u19DE-\\u19FF\\u1B61-\\u1B6A\\u1B74-\\u1B7C\\u2100\\u2101\\u2103"
- b"-\\u2106\\u2108\\u2109\\u2114\\u2116\\u2117\\u211E-\\u2123\\u2125"
- b"\\u2127\\u2129\\u212E\\u213A\\u213B\\u214A\\u214C\\u214D\\u214F"
- b"\\u218A\\u218B\\u2195-\\u2199\\u219C-\\u219F\\u21A1\\u21A2\\u21A4"
- b"\\u21A5\\u21A7-\\u21AD\\u21AF-\\u21CD\\u21D0\\u21D1\\u21D3\\u21D5"
- b"-\\u21F3\\u2300-\\u2307\\u230C-\\u231F\\u2322-\\u2328\\u232B"
- b"-\\u237B\\u237D-\\u239A\\u23B4-\\u23DB\\u23E2-\\u2426\\u2440"
- b"-\\u244A\\u249C-\\u24E9\\u2500-\\u25B6\\u25B8-\\u25C0\\u25C2"
- b"-\\u25F7\\u2600-\\u266E\\u2670-\\u2767\\u2794-\\u27BF\\u2800"
- b"-\\u28FF\\u2B00-\\u2B2F\\u2B45\\u2B46\\u2B4D-\\u2B73\\u2B76"
- b"-\\u2B95\\u2B98-\\u2BC8\\u2BCA-\\u2BFE\\u2CE5-\\u2CEA\\u2E80"
- b"-\\u2E99\\u2E9B-\\u2EF3\\u2F00-\\u2FD5\\u2FF0-\\u2FFB\\u3004"
- b"\\u3012\\u3013\\u3020\\u3036\\u3037\\u303E\\u303F\\u3190\\u3191"
- b"\\u3196-\\u319F\\u31C0-\\u31E3\\u3200-\\u321E\\u322A-\\u3247\\u3250"
- b"\\u3260-\\u327F\\u328A-\\u32B0\\u32C0-\\u32FE\\u3300-\\u33FF\\u4DC0"
- b"-\\u4DFF\\uA490-\\uA4C6\\uA828-\\uA82B\\uA836\\uA837\\uA839\\uAA77"
- b"-\\uAA79\\uFDFD\\uFFE4\\uFFE8\\uFFED\\uFFEE\\uFFFC\\uFFFD\\U00010137"
- b"-\\U0001013F\\U00010179-\\U00010189\\U0001018C-\\U0001018E"
- b"\\U00010190-\\U0001019B\\U000101A0\\U000101D0-\\U000101FC\\U00010877"
- b"\\U00010878\\U00010AC8\\U0001173F\\U00016B3C-\\U00016B3F\\U00016B45"
- b"\\U0001BC9C\\U0001D000-\\U0001D0F5\\U0001D100-\\U0001D126\\U0001D129"
- b"-\\U0001D164\\U0001D16A-\\U0001D16C\\U0001D183\\U0001D184\\U0001D18C"
- b"-\\U0001D1A9\\U0001D1AE-\\U0001D1E8\\U0001D200-\\U0001D241\\U0001D245"
- b"\\U0001D300-\\U0001D356\\U0001D800-\\U0001D9FF\\U0001DA37-\\U0001DA3A"
- b"\\U0001DA6D-\\U0001DA74\\U0001DA76-\\U0001DA83\\U0001DA85\\U0001DA86"
- b"\\U0001ECAC\\U0001F000-\\U0001F02B\\U0001F030-\\U0001F093\\U0001F0A0"
- b"-\\U0001F0AE\\U0001F0B1-\\U0001F0BF\\U0001F0C1-\\U0001F0CF\\U0001F0D1"
- b"-\\U0001F0F5\\U0001F110-\\U0001F16B\\U0001F170-\\U0001F1AC\\U0001F1E6"
- b"-\\U0001F202\\U0001F210-\\U0001F23B\\U0001F240-\\U0001F248\\U0001F250"
- b"\\U0001F251\\U0001F260-\\U0001F265\\U0001F300-\\U0001F3FA\\U0001F400"
- b"-\\U0001F6D4\\U0001F6E0-\\U0001F6EC\\U0001F6F0-\\U0001F6F9\\U0001F700"
- b"-\\U0001F773\\U0001F780-\\U0001F7D8\\U0001F800-\\U0001F80B\\U0001F810"
- b"-\\U0001F847\\U0001F850-\\U0001F859\\U0001F860-\\U0001F887\\U0001F890"
- b"-\\U0001F8AD\\U0001F900-\\U0001F90B\\U0001F910-\\U0001F93E\\U0001F940"
- b"-\\U0001F970\\U0001F973-\\U0001F976\\U0001F97A\\U0001F97C-\\U0001F9A2"
- b"\\U0001F9B0-\\U0001F9B9\\U0001F9C0-\\U0001F9C2\\U0001F9D0-\\U0001F9FF"
- b"\\U0001FA60-\\U0001FA6D]"
-)
-
-
-if compat.is_python2:
- # If we have this test in Python 3, pytest chokes, as it can't print the
- # string above in the xpass message.
- def test_issue3356():
- pattern = re.compile(compat.unescape_unicode(prefix_search.decode("utf8")))
- assert not pattern.search("hello")
diff --git a/spacy/tests/regression/test_issue3410.py b/spacy/tests/regression/test_issue3410.py
deleted file mode 100644
index 5d2ac5ba3..000000000
--- a/spacy/tests/regression/test_issue3410.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-import pytest
-from spacy.lang.en import English
-from spacy.matcher import Matcher, PhraseMatcher
-
-
-def test_issue3410():
- texts = ["Hello world", "This is a test"]
- nlp = English()
- matcher = Matcher(nlp.vocab)
- phrasematcher = PhraseMatcher(nlp.vocab)
- with pytest.deprecated_call():
- docs = list(nlp.pipe(texts, n_threads=4))
- with pytest.deprecated_call():
- docs = list(nlp.tokenizer.pipe(texts, n_threads=4))
- with pytest.deprecated_call():
- list(matcher.pipe(docs, n_threads=4))
- with pytest.deprecated_call():
- list(phrasematcher.pipe(docs, n_threads=4))
diff --git a/spacy/tests/regression/test_issue3447.py b/spacy/tests/regression/test_issue3447.py
deleted file mode 100644
index 0ca1f9e67..000000000
--- a/spacy/tests/regression/test_issue3447.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from spacy.util import decaying
-
-
-def test_issue3447():
- sizes = decaying(10.0, 1.0, 0.5)
- size = next(sizes)
- assert size == 10.0
- size = next(sizes)
- assert size == 10.0 - 0.5
- size = next(sizes)
- assert size == 10.0 - 0.5 - 0.5
diff --git a/spacy/tests/regression/test_issue3449.py b/spacy/tests/regression/test_issue3449.py
deleted file mode 100644
index deff49fd6..000000000
--- a/spacy/tests/regression/test_issue3449.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-import pytest
-
-from spacy.lang.en import English
-
-
-@pytest.mark.xfail(reason="default suffix rules avoid one upper-case letter before dot")
-def test_issue3449():
- nlp = English()
- nlp.add_pipe(nlp.create_pipe("sentencizer"))
- text1 = "He gave the ball to I. Do you want to go to the movies with I?"
- text2 = "He gave the ball to I. Do you want to go to the movies with I?"
- text3 = "He gave the ball to I.\nDo you want to go to the movies with I?"
- t1 = nlp(text1)
- t2 = nlp(text2)
- t3 = nlp(text3)
- assert t1[5].text == "I"
- assert t2[5].text == "I"
- assert t3[5].text == "I"
diff --git a/spacy/tests/regression/test_issue3468.py b/spacy/tests/regression/test_issue3468.py
deleted file mode 100644
index ebbed2640..000000000
--- a/spacy/tests/regression/test_issue3468.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# coding: utf8
-from __future__ import unicode_literals
-
-from spacy.lang.en import English
-from spacy.tokens import Doc
-
-
-def test_issue3468():
- """Test that sentence boundaries are set correctly so Doc.is_sentenced can
- be restored after serialization."""
- nlp = English()
- nlp.add_pipe(nlp.create_pipe("sentencizer"))
- doc = nlp("Hello world")
- assert doc[0].is_sent_start
- assert doc.is_sentenced
- assert len(list(doc.sents)) == 1
- doc_bytes = doc.to_bytes()
- new_doc = Doc(nlp.vocab).from_bytes(doc_bytes)
- assert new_doc[0].is_sent_start
- assert new_doc.is_sentenced
- assert len(list(new_doc.sents)) == 1
diff --git a/spacy/tests/regression/test_issue3526.py b/spacy/tests/regression/test_issue3526.py
index 3949c4b1c..c6f513730 100644
--- a/spacy/tests/regression/test_issue3526.py
+++ b/spacy/tests/regression/test_issue3526.py
@@ -7,6 +7,7 @@ from spacy.language import Language
from spacy.pipeline import EntityRuler
from spacy import load
import srsly
+
from ..util import make_tempdir
@@ -61,10 +62,9 @@ def test_entity_ruler_from_disk_old_format_safe(patterns, en_vocab):
nlp = Language(vocab=en_vocab)
ruler = EntityRuler(nlp, patterns=patterns, overwrite_ents=True)
with make_tempdir() as tmpdir:
- out_file = tmpdir / "entity_ruler.jsonl"
- srsly.write_jsonl(out_file, ruler.patterns)
- new_ruler = EntityRuler(nlp)
- new_ruler = new_ruler.from_disk(out_file)
+ out_file = tmpdir / "entity_ruler"
+ srsly.write_jsonl(out_file.with_suffix(".jsonl"), ruler.patterns)
+ new_ruler = EntityRuler(nlp).from_disk(out_file)
for pattern in ruler.patterns:
assert pattern in new_ruler.patterns
assert len(new_ruler) == len(ruler)
@@ -79,8 +79,10 @@ def test_entity_ruler_in_pipeline_from_issue(patterns, en_vocab):
nlp.add_pipe(ruler)
with make_tempdir() as tmpdir:
nlp.to_disk(tmpdir)
- assert nlp.pipeline[-1][-1].patterns == [{"label": "ORG", "pattern": "Apple"}]
- assert nlp.pipeline[-1][-1].overwrite is True
+ ruler = nlp.get_pipe("entity_ruler")
+ assert ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
+ assert ruler.overwrite is True
nlp2 = load(tmpdir)
- assert nlp2.pipeline[-1][-1].patterns == [{"label": "ORG", "pattern": "Apple"}]
- assert nlp2.pipeline[-1][-1].overwrite is True
+ new_ruler = nlp2.get_pipe("entity_ruler")
+ assert new_ruler.patterns == [{"label": "ORG", "pattern": "Apple"}]
+ assert new_ruler.overwrite is True
diff --git a/spacy/tests/regression/test_issue3611.py b/spacy/tests/regression/test_issue3611.py
new file mode 100644
index 000000000..29aa5370d
--- /dev/null
+++ b/spacy/tests/regression/test_issue3611.py
@@ -0,0 +1,51 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import pytest
+import spacy
+from spacy.util import minibatch, compounding
+
+
+def test_issue3611():
+ """ Test whether adding n-grams in the textcat works even when n > token length of some docs """
+ unique_classes = ["offensive", "inoffensive"]
+ x_train = ["This is an offensive text",
+ "This is the second offensive text",
+ "inoff"]
+ y_train = ["offensive", "offensive", "inoffensive"]
+
+ # preparing the data
+ pos_cats = list()
+ for train_instance in y_train:
+ pos_cats.append({label: label == train_instance for label in unique_classes})
+ train_data = list(zip(x_train, [{'cats': cats} for cats in pos_cats]))
+
+ # set up the spacy model with a text categorizer component
+ nlp = spacy.blank('en')
+
+ textcat = nlp.create_pipe(
+ "textcat",
+ config={
+ "exclusive_classes": True,
+ "architecture": "bow",
+ "ngram_size": 2
+ }
+ )
+
+ for label in unique_classes:
+ textcat.add_label(label)
+ nlp.add_pipe(textcat, last=True)
+
+ # training the network
+ other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'textcat']
+ with nlp.disable_pipes(*other_pipes):
+ optimizer = nlp.begin_training()
+ for i in range(3):
+ losses = {}
+ batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
+
+ for batch in batches:
+ texts, annotations = zip(*batch)
+ nlp.update(docs=texts, golds=annotations, sgd=optimizer, drop=0.1, losses=losses)
+
+
diff --git a/spacy/tests/regression/test_issue3625.py b/spacy/tests/regression/test_issue3625.py
new file mode 100644
index 000000000..e3e0f25ee
--- /dev/null
+++ b/spacy/tests/regression/test_issue3625.py
@@ -0,0 +1,10 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from spacy.lang.hi import Hindi
+
+def test_issue3625():
+ """Test that default punctuation rules applies to hindi unicode characters"""
+ nlp = Hindi()
+ doc = nlp(u"hi. how हुए. होटल, होटल")
+ assert [token.text for token in doc] == ['hi', '.', 'how', 'हुए', '.', 'होटल', ',', 'होटल']
\ No newline at end of file
diff --git a/spacy/tests/regression/test_issue3839.py b/spacy/tests/regression/test_issue3839.py
index fa915faf0..34d6bb46e 100644
--- a/spacy/tests/regression/test_issue3839.py
+++ b/spacy/tests/regression/test_issue3839.py
@@ -6,7 +6,6 @@ from spacy.matcher import Matcher
from spacy.tokens import Doc
-@pytest.mark.xfail
def test_issue3839(en_vocab):
"""Test that match IDs returned by the matcher are correct, are in the string """
doc = Doc(en_vocab, words=["terrific", "group", "of", "people"])
diff --git a/spacy/tests/regression/test_issue3869.py b/spacy/tests/regression/test_issue3869.py
new file mode 100644
index 000000000..42584b133
--- /dev/null
+++ b/spacy/tests/regression/test_issue3869.py
@@ -0,0 +1,31 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+import pytest
+
+from spacy.attrs import IS_ALPHA
+from spacy.lang.en import English
+
+
+@pytest.mark.parametrize(
+ "sentence",
+ [
+ 'The story was to the effect that a young American student recently called on Professor Christlieb with a letter of introduction.',
+ 'The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale\'s #1.',
+ 'The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale\'s number one',
+ 'Indeed, making the one who remains do all the work has installed him into a position of such insolent tyranny, it will take a month at least to reduce him to his proper proportions.',
+ "It was a missed assignment, but it shouldn't have resulted in a turnover ..."
+ ],
+)
+def test_issue3869(sentence):
+ """Test that the Doc's count_by function works consistently"""
+ nlp = English()
+ doc = nlp(sentence)
+
+ count = 0
+ for token in doc:
+ count += token.is_alpha
+
+ assert count == doc.count_by(IS_ALPHA).get(1, 0)
+
+
diff --git a/spacy/tests/regression/test_issue3880.py b/spacy/tests/regression/test_issue3880.py
new file mode 100644
index 000000000..6de373f11
--- /dev/null
+++ b/spacy/tests/regression/test_issue3880.py
@@ -0,0 +1,22 @@
+# coding: utf8
+from __future__ import unicode_literals
+
+from spacy.lang.en import English
+
+
+def test_issue3880():
+ """Test that `nlp.pipe()` works when an empty string ends the batch.
+
+ Fixed in v7.0.5 of Thinc.
+ """
+ texts = ["hello", "world", "", ""]
+ nlp = English()
+ nlp.add_pipe(nlp.create_pipe("parser"))
+ nlp.add_pipe(nlp.create_pipe("ner"))
+ nlp.add_pipe(nlp.create_pipe("tagger"))
+ nlp.get_pipe("parser").add_label("dep")
+ nlp.get_pipe("ner").add_label("PERSON")
+ nlp.get_pipe("tagger").add_label("NN")
+ nlp.begin_training()
+ for doc in nlp.pipe(texts):
+ pass
diff --git a/spacy/tests/serialize/test_serialize_kb.py b/spacy/tests/serialize/test_serialize_kb.py
new file mode 100644
index 000000000..fa7253fa1
--- /dev/null
+++ b/spacy/tests/serialize/test_serialize_kb.py
@@ -0,0 +1,74 @@
+# coding: utf-8
+from __future__ import unicode_literals
+
+from ..util import make_tempdir
+from ...util import ensure_path
+
+from spacy.kb import KnowledgeBase
+
+
+def test_serialize_kb_disk(en_vocab):
+ # baseline assertions
+ kb1 = _get_dummy_kb(en_vocab)
+ _check_kb(kb1)
+
+ # dumping to file & loading back in
+ with make_tempdir() as d:
+ dir_path = ensure_path(d)
+ if not dir_path.exists():
+ dir_path.mkdir()
+ file_path = dir_path / "kb"
+ kb1.dump(str(file_path))
+
+ kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
+ kb2.load_bulk(str(file_path))
+
+ # final assertions
+ _check_kb(kb2)
+
+
+def _get_dummy_kb(vocab):
+ kb = KnowledgeBase(vocab=vocab, entity_vector_length=3)
+
+ kb.add_entity(entity='Q53', prob=0.33, entity_vector=[0, 5, 3])
+ kb.add_entity(entity='Q17', prob=0.2, entity_vector=[7, 1, 0])
+ kb.add_entity(entity='Q007', prob=0.7, entity_vector=[0, 0, 7])
+ kb.add_entity(entity='Q44', prob=0.4, entity_vector=[4, 4, 4])
+
+ kb.add_alias(alias='double07', entities=['Q17', 'Q007'], probabilities=[0.1, 0.9])
+ kb.add_alias(alias='guy', entities=['Q53', 'Q007', 'Q17', 'Q44'], probabilities=[0.3, 0.3, 0.2, 0.1])
+ kb.add_alias(alias='random', entities=['Q007'], probabilities=[1.0])
+
+ return kb
+
+
+def _check_kb(kb):
+ # check entities
+ assert kb.get_size_entities() == 4
+ for entity_string in ['Q53', 'Q17', 'Q007', 'Q44']:
+ assert entity_string in kb.get_entity_strings()
+ for entity_string in ['', 'Q0']:
+ assert entity_string not in kb.get_entity_strings()
+
+ # check aliases
+ assert kb.get_size_aliases() == 3
+ for alias_string in ['double07', 'guy', 'random']:
+ assert alias_string in kb.get_alias_strings()
+ for alias_string in ['nothingness', '', 'randomnoise']:
+ assert alias_string not in kb.get_alias_strings()
+
+ # check candidates & probabilities
+ candidates = sorted(kb.get_candidates('double07'), key=lambda x: x.entity_)
+ assert len(candidates) == 2
+
+ assert candidates[0].entity_ == 'Q007'
+ assert 0.6999 < candidates[0].entity_freq < 0.701
+ assert candidates[0].entity_vector == [0, 0, 7]
+ assert candidates[0].alias_ == 'double07'
+ assert 0.899 < candidates[0].prior_prob < 0.901
+
+ assert candidates[1].entity_ == 'Q17'
+ assert 0.199 < candidates[1].entity_freq < 0.201
+ assert candidates[1].entity_vector == [7, 1, 0]
+ assert candidates[1].alias_ == 'double07'
+ assert 0.099 < candidates[1].prior_prob < 0.101
diff --git a/spacy/tokens/_serialize.py b/spacy/tokens/_serialize.py
index 43ea78242..41f524839 100644
--- a/spacy/tokens/_serialize.py
+++ b/spacy/tokens/_serialize.py
@@ -11,29 +11,27 @@ from ..tokens import Doc
from ..attrs import SPACY, ORTH
-class Binder(object):
+class DocBox(object):
"""Serialize analyses from a collection of doc objects."""
- def __init__(self, attrs=None):
- """Create a Binder object, to hold serialized annotations.
+ def __init__(self, attrs=None, store_user_data=False):
+ """Create a DocBox object, to hold serialized annotations.
attrs (list): List of attributes to serialize. 'orth' and 'spacy' are
always serialized, so they're not required. Defaults to None.
"""
attrs = attrs or []
- self.attrs = list(attrs)
# Ensure ORTH is always attrs[0]
- if ORTH in self.attrs:
- self.attrs.pop(ORTH)
- if SPACY in self.attrs:
- self.attrs.pop(SPACY)
+ self.attrs = [attr for attr in attrs if attr != ORTH and attr != SPACY]
self.attrs.insert(0, ORTH)
self.tokens = []
self.spaces = []
+ self.user_data = []
self.strings = set()
+ self.store_user_data = store_user_data
def add(self, doc):
- """Add a doc's annotations to the binder for serialization."""
+ """Add a doc's annotations to the DocBox for serialization."""
array = doc.to_array(self.attrs)
if len(array.shape) == 1:
array = array.reshape((array.shape[0], 1))
@@ -43,27 +41,35 @@ class Binder(object):
spaces = spaces.reshape((spaces.shape[0], 1))
self.spaces.append(numpy.asarray(spaces, dtype=bool))
self.strings.update(w.text for w in doc)
+ if self.store_user_data:
+ self.user_data.append(srsly.msgpack_dumps(doc.user_data))
def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab."""
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
- for tokens, spaces in zip(self.tokens, self.spaces):
+ for i in range(len(self.tokens)):
+ tokens = self.tokens[i]
+ spaces = self.spaces[i]
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.from_array(self.attrs, tokens)
+ if self.store_user_data:
+ doc.user_data.update(srsly.msgpack_loads(self.user_data[i]))
yield doc
def merge(self, other):
- """Extend the annotations of this binder with the annotations from another."""
+ """Extend the annotations of this DocBox with the annotations from another."""
assert self.attrs == other.attrs
self.tokens.extend(other.tokens)
self.spaces.extend(other.spaces)
self.strings.update(other.strings)
+ if self.store_user_data:
+ self.user_data.extend(other.user_data)
def to_bytes(self):
- """Serialize the binder's annotations into a byte string."""
+ """Serialize the DocBox's annotations into a byte string."""
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
lengths = [len(tokens) for tokens in self.tokens]
@@ -74,10 +80,12 @@ class Binder(object):
"lengths": numpy.asarray(lengths, dtype="int32").tobytes("C"),
"strings": list(self.strings),
}
+ if self.store_user_data:
+ msg["user_data"] = self.user_data
return gzip.compress(srsly.msgpack_dumps(msg))
def from_bytes(self, string):
- """Deserialize the binder's annotations from a byte string."""
+ """Deserialize the DocBox's annotations from a byte string."""
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
@@ -89,29 +97,38 @@ class Binder(object):
flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
+ if self.store_user_data and "user_data" in msg:
+ self.user_data = list(msg["user_data"])
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
return self
-def merge_bytes(binder_strings):
- """Concatenate multiple serialized binders into one byte string."""
- output = None
- for byte_string in binder_strings:
- binder = Binder().from_bytes(byte_string)
- if output is None:
- output = binder
- else:
- output.merge(binder)
- return output.to_bytes()
+def merge_boxes(boxes):
+ merged = None
+ for byte_string in boxes:
+ if byte_string is not None:
+ box = DocBox(store_user_data=True).from_bytes(byte_string)
+ if merged is None:
+ merged = box
+ else:
+ merged.merge(box)
+ if merged is not None:
+ return merged.to_bytes()
+ else:
+ return b""
-def pickle_binder(binder):
- return (unpickle_binder, (binder.to_bytes(),))
+def pickle_box(box):
+ return (unpickle_box, (box.to_bytes(),))
-def unpickle_binder(byte_string):
- return Binder().from_bytes(byte_string)
+def unpickle_box(byte_string):
+ return DocBox().from_bytes(byte_string)
-copy_reg.pickle(Binder, pickle_binder, unpickle_binder)
+copy_reg.pickle(DocBox, pickle_box, unpickle_box)
+# Compatibility, as we had named it this previously.
+Binder = DocBox
+
+__all__ = ["DocBox"]
diff --git a/spacy/tokens/doc.pxd b/spacy/tokens/doc.pxd
index 7cdc2316a..62665fcc5 100644
--- a/spacy/tokens/doc.pxd
+++ b/spacy/tokens/doc.pxd
@@ -1,6 +1,5 @@
from cymem.cymem cimport Pool
cimport numpy as np
-from preshed.counter cimport PreshCounter
from ..vocab cimport Vocab
from ..structs cimport TokenC, LexemeC
diff --git a/spacy/tokens/doc.pyx b/spacy/tokens/doc.pyx
index 131c43d37..c1883f9c0 100644
--- a/spacy/tokens/doc.pyx
+++ b/spacy/tokens/doc.pyx
@@ -9,6 +9,7 @@ cimport cython
cimport numpy as np
from libc.string cimport memcpy, memset
from libc.math cimport sqrt
+from collections import Counter
import numpy
import numpy.linalg
@@ -22,7 +23,7 @@ from ..lexeme cimport Lexeme, EMPTY_LEXEME
from ..typedefs cimport attr_t, flags_t
from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, CLUSTER
from ..attrs cimport LENGTH, POS, LEMMA, TAG, DEP, HEAD, SPACY, ENT_IOB
-from ..attrs cimport ENT_TYPE, SENT_START, attr_id_t
+from ..attrs cimport ENT_TYPE, ENT_KB_ID, SENT_START, attr_id_t
from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t
from ..attrs import intify_attrs, IDS
@@ -64,6 +65,8 @@ cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
return token.ent_iob
elif feat_name == ENT_TYPE:
return token.ent_type
+ elif feat_name == ENT_KB_ID:
+ return token.ent_kb_id
else:
return Lexeme.get_struct_attr(token.lex, feat_name)
@@ -85,13 +88,14 @@ cdef class Doc:
Python-level `Token` and `Span` objects are views of this array, i.e.
they don't own the data themselves.
- EXAMPLE: Construction 1
+ EXAMPLE:
+ Construction 1
>>> doc = nlp(u'Some text')
Construction 2
>>> from spacy.tokens import Doc
>>> doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
- spaces=[True, False, False])
+ >>> spaces=[True, False, False])
DOCS: https://spacy.io/api/doc
"""
@@ -237,6 +241,8 @@ cdef class Doc:
return True
if self.is_parsed:
return True
+ if len(self) < 2:
+ return True
for i in range(1, self.length):
if self.c[i].sent_start == -1 or self.c[i].sent_start == 1:
return True
@@ -248,6 +254,8 @@ cdef class Doc:
*any* of the tokens has a named entity tag set (even if the others are
uknown values).
"""
+ if len(self) == 0:
+ return True
for i in range(self.length):
if self.c[i].ent_iob != 0:
return True
@@ -690,7 +698,7 @@ cdef class Doc:
# Handle 1d case
return output if len(attr_ids) >= 2 else output.reshape((self.length,))
- def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
+ def count_by(self, attr_id_t attr_id, exclude=None, object counts=None):
"""Count the frequencies of a given attribute. Produces a dict of
`{attribute (int): count (ints)}` frequencies, keyed by the values of
the given attribute ID.
@@ -705,19 +713,18 @@ cdef class Doc:
cdef size_t count
if counts is None:
- counts = PreshCounter()
+ counts = Counter()
output_dict = True
else:
output_dict = False
# Take this check out of the loop, for a bit of extra speed
if exclude is None:
for i in range(self.length):
- counts.inc(get_token_attr(&self.c[i], attr_id), 1)
+ counts[get_token_attr(&self.c[i], attr_id)] += 1
else:
for i in range(self.length):
if not exclude(self[i]):
- attr = get_token_attr(&self.c[i], attr_id)
- counts.inc(attr, 1)
+ counts[get_token_attr(&self.c[i], attr_id)] += 1
if output_dict:
return dict(counts)
@@ -850,7 +857,7 @@ cdef class Doc:
DOCS: https://spacy.io/api/doc#to_bytes
"""
- array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE]
+ array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE] # TODO: ENT_KB_ID ?
if self.is_tagged:
array_head.append(TAG)
# If doc parsed add head and dep attribute
@@ -1004,6 +1011,7 @@ cdef class Doc:
"""
cdef unicode tag, lemma, ent_type
deprecation_warning(Warnings.W013.format(obj="Doc"))
+ # TODO: ENT_KB_ID ?
if len(args) == 3:
deprecation_warning(Warnings.W003)
tag, lemma, ent_type = args
diff --git a/spacy/tokens/span.pyx b/spacy/tokens/span.pyx
index 97b6a1adc..3f4f4418b 100644
--- a/spacy/tokens/span.pyx
+++ b/spacy/tokens/span.pyx
@@ -210,7 +210,7 @@ cdef class Span:
words = [t.text for t in self]
spaces = [bool(t.whitespace_) for t in self]
cdef Doc doc = Doc(self.doc.vocab, words=words, spaces=spaces)
- array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE]
+ array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE, ENT_KB_ID]
if self.doc.is_tagged:
array_head.append(TAG)
# If doc parsed add head and dep attribute
diff --git a/spacy/tokens/token.pxd b/spacy/tokens/token.pxd
index bb9f7d070..ec5df3fac 100644
--- a/spacy/tokens/token.pxd
+++ b/spacy/tokens/token.pxd
@@ -53,6 +53,8 @@ cdef class Token:
return token.ent_iob
elif feat_name == ENT_TYPE:
return token.ent_type
+ elif feat_name == ENT_KB_ID:
+ return token.ent_kb_id
elif feat_name == SENT_START:
return token.sent_start
else:
@@ -79,5 +81,7 @@ cdef class Token:
token.ent_iob = value
elif feat_name == ENT_TYPE:
token.ent_type = value
+ elif feat_name == ENT_KB_ID:
+ token.ent_kb_id = value
elif feat_name == SENT_START:
token.sent_start = value
diff --git a/website/docs/api/annotation.md b/website/docs/api/annotation.md
index a5bb30b6f..ed0e0b3e0 100644
--- a/website/docs/api/annotation.md
+++ b/website/docs/api/annotation.md
@@ -520,7 +520,9 @@ spaCy takes training data in JSON format. The built-in
[`convert`](/api/cli#convert) command helps you convert the `.conllu` format
used by the
[Universal Dependencies corpora](https://github.com/UniversalDependencies) to
-spaCy's training format.
+spaCy's training format. To convert one or more existing `Doc` objects to
+spaCy's JSON format, you can use the
+[`gold.docs_to_json`](/api/goldparse#docs_to_json) helper.
> #### Annotating entities
>
diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md
index a69e62219..7af134e40 100644
--- a/website/docs/api/cli.md
+++ b/website/docs/api/cli.md
@@ -284,9 +284,9 @@ same between pretraining and training. The API and errors around this need some
improvement.
```bash
-$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
-[--depth] [--embed-rows] [--loss_func] [--dropout] [--seed] [--n-iter] [--use-vectors]
-[--n-save_every]
+$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir]
+[--width] [--depth] [--embed-rows] [--loss_func] [--dropout] [--batch-size] [--max-length] [--min-length]
+[--seed] [--n-iter] [--use-vectors] [--n-save_every] [--init-tok2vec] [--epoch-start]
```
| Argument | Type | Description |
@@ -306,7 +306,8 @@ $ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
| `--n-iter`, `-i` | option | Number of iterations to pretrain. |
| `--use-vectors`, `-uv` | flag | Whether to use the static vectors as input features. |
| `--n-save-every`, `-se` | option | Save model every X batches. |
-| `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental.|
+| `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental.|
+| `--epoch-start`, `-es` <Tag variant="new">2.1.5</Tag> | option | The epoch to start counting at. Only relevant when using `--init-tok2vec` and the given weight file has been renamed. Prevents unintended overwriting of existing weight files.|
| **CREATES** | weights | The pre-trained weights that can be used to initialize `spacy train`. |
### JSONL format for raw text {#pretrain-jsonl}
diff --git a/website/docs/api/doc.md b/website/docs/api/doc.md
index f5a94335f..bf9801564 100644
--- a/website/docs/api/doc.md
+++ b/website/docs/api/doc.md
@@ -264,7 +264,7 @@ ancestor is found, e.g. if span excludes a necessary ancestor.
| ----------- | -------------------------------------- | ----------------------------------------------- |
| **RETURNS** | `numpy.ndarray[ndim=2, dtype='int32']` | The lowest common ancestor matrix of the `Doc`. |
-## Doc.to_json {#to_json, tag="method" new="2.1"}
+## Doc.to_json {#to_json tag="method" new="2.1"}
Convert a Doc to JSON. The format it produces will be the new format for the
[`spacy train`](/api/cli#train) command (not implemented yet). If custom
diff --git a/website/docs/api/entityruler.md b/website/docs/api/entityruler.md
index dcbf99da5..5c05450f8 100644
--- a/website/docs/api/entityruler.md
+++ b/website/docs/api/entityruler.md
@@ -30,14 +30,14 @@ be a token pattern (list) or a phrase pattern (string). For example:
> ruler = EntityRuler(nlp, overwrite_ents=True)
> ```
-| Name | Type | Description |
-| ---------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `nlp` | `Language` | The shared nlp object to pass the vocab to the matchers and process phrase patterns. |
-| `patterns` | iterable | Optional patterns to load in. |
-| `phrase_matcher_attr` | int / unicode | Optional attr to pass to the internal [`PhraseMatcher`](/api/phtasematcher). defaults to `None`
-| `overwrite_ents` | bool | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. |
-| `**cfg` | - | Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to `spacy.load`. |
-| **RETURNS** | `EntityRuler` | The newly constructed object. |
+| Name | Type | Description |
+| --------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `nlp` | `Language` | The shared nlp object to pass the vocab to the matchers and process phrase patterns. |
+| `patterns` | iterable | Optional patterns to load in. |
+| `phrase_matcher_attr` | int / unicode | Optional attr to pass to the internal [`PhraseMatcher`](/api/phtasematcher). defaults to `None` |
+| `overwrite_ents` | bool | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. |
+| `**cfg` | - | Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to `spacy.load`. |
+| **RETURNS** | `EntityRuler` | The newly constructed object. |
## EntityRuler.\_\len\_\_ {#len tag="method"}
@@ -123,35 +123,41 @@ of dicts) or a phrase pattern (string). For more details, see the usage guide on
## EntityRuler.to_disk {#to_disk tag="method"}
Save the entity ruler patterns to a directory. The patterns will be saved as
-newline-delimited JSON (JSONL).
+newline-delimited JSON (JSONL). If a file with the suffix `.jsonl` is provided,
+only the patterns are saved as JSONL. If a directory name is provided, a
+`patterns.jsonl` and `cfg` file with the component configuration is exported.
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
-> ruler.to_disk("/path/to/rules.jsonl")
+> ruler.to_disk("/path/to/patterns.jsonl") # saves patterns only
+> ruler.to_disk("/path/to/entity_ruler") # saves patterns and config
> ```
-| Name | Type | Description |
-| ------ | ---------------- | ---------------------------------------------------------------------------------------------------------------- |
-| `path` | unicode / `Path` | A path to a file, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
+| Name | Type | Description |
+| ------ | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a JSONL file or directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
## EntityRuler.from_disk {#from_disk tag="method"}
-Load the entity ruler from a file. Expects a file containing newline-delimited
-JSON (JSONL) with one entry per line.
+Load the entity ruler from a file. Expects either a file containing
+newline-delimited JSON (JSONL) with one entry per line, or a directory
+containing a `patterns.jsonl` file and a `cfg` file with the component
+configuration.
> #### Example
>
> ```python
> ruler = EntityRuler(nlp)
-> ruler.from_disk("/path/to/rules.jsonl")
+> ruler.from_disk("/path/to/patterns.jsonl") # loads patterns only
+> ruler.from_disk("/path/to/entity_ruler") # loads patterns and config
> ```
-| Name | Type | Description |
-| ----------- | ---------------- | --------------------------------------------------------------------------- |
-| `path` | unicode / `Path` | A path to a JSONL file. Paths may be either strings or `Path`-like objects. |
-| **RETURNS** | `EntityRuler` | The modified `EntityRuler` object. |
+| Name | Type | Description |
+| ----------- | ---------------- | ---------------------------------------------------------------------------------------- |
+| `path` | unicode / `Path` | A path to a JSONL file or directory. Paths may be either strings or `Path`-like objects. |
+| **RETURNS** | `EntityRuler` | The modified `EntityRuler` object. |
## EntityRuler.to_bytes {#to_bytes tag="method"}
diff --git a/website/docs/api/goldparse.md b/website/docs/api/goldparse.md
index ca5b6a811..13f68a85d 100644
--- a/website/docs/api/goldparse.md
+++ b/website/docs/api/goldparse.md
@@ -55,6 +55,27 @@ Whether the provided syntactic annotations form a projective dependency tree.
## Utilities {#util}
+### gold.docs_to_json {#docs_to_json tag="function"}
+
+Convert a list of Doc objects into the
+[JSON-serializable format](/api/annotation#json-input) used by the
+[`spacy train`](/api/cli#train) command.
+
+> #### Example
+>
+> ```python
+> from spacy.gold import docs_to_json
+>
+> doc = nlp(u"I like London")
+> json_data = docs_to_json([doc])
+> ```
+
+| Name | Type | Description |
+| ----------- | ---------------- | ------------------------------------------ |
+| `docs` | iterable / `Doc` | The `Doc` object(s) to convert. |
+| `id` | int | ID to assign to the JSON. Defaults to `0`. |
+| **RETURNS** | list | The data in spaCy's JSON format. |
+
### gold.biluo_tags_from_offsets {#biluo_tags_from_offsets tag="function"}
Encode labelled spans into per-token tags, using the
diff --git a/website/docs/api/scorer.md b/website/docs/api/scorer.md
index e6a8595fd..2af4ec0ce 100644
--- a/website/docs/api/scorer.md
+++ b/website/docs/api/scorer.md
@@ -46,13 +46,14 @@ 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). |
-| `scores` | dict | All scores with keys `uas`, `las`, `ents_p`, `ents_r`, `ents_f`, `tags_acc` and `token_acc`. |
+| 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. |
+| `scores` | dict | All scores with keys `uas`, `las`, `ents_p`, `ents_r`, `ents_f`, `ents_per_type`, `tags_acc` and `token_acc`. |
diff --git a/website/docs/api/tokenizer.md b/website/docs/api/tokenizer.md
index 5bc0df625..67e67f5c9 100644
--- a/website/docs/api/tokenizer.md
+++ b/website/docs/api/tokenizer.md
@@ -9,7 +9,10 @@ Segment text, and create `Doc` objects with the discovered segment boundaries.
## Tokenizer.\_\_init\_\_ {#init tag="method"}
-Create a `Tokenizer`, to create `Doc` objects given unicode text.
+Create a `Tokenizer`, to create `Doc` objects given unicode text. For examples
+of how to construct a custom tokenizer with different tokenization rules, see
+the
+[usage documentation](https://spacy.io/usage/linguistic-features#native-tokenizers).
> #### Example
>
@@ -18,11 +21,14 @@ Create a `Tokenizer`, to create `Doc` objects given unicode text.
> from spacy.tokenizer import Tokenizer
> from spacy.lang.en import English
> nlp = English()
+> # Create a blank Tokenizer with just the English vocab
> tokenizer = Tokenizer(nlp.vocab)
>
> # Construction 2
> from spacy.lang.en import English
> nlp = English()
+> # Create a Tokenizer with the default settings for English
+> # including punctuation rules and exceptions
> tokenizer = nlp.Defaults.create_tokenizer(nlp)
> ```
diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md
index 91513588c..b84bf4e12 100644
--- a/website/docs/usage/training.md
+++ b/website/docs/usage/training.md
@@ -39,6 +39,9 @@ mkdir models
python -m spacy train es models ancora-json/es_ancora-ud-train.json ancora-json/es_ancora-ud-dev.json
```
+You can also use the [`gold.docs_to_json`](/api/goldparse#docs_to_json) helper
+to convert a list of `Doc` objects to spaCy's JSON training format.
+
#### Understanding the training output
When you train a model using the [`spacy train`](/api/cli#train) command, you'll
@@ -630,13 +633,13 @@ should be somewhat larger, especially if your documents are long.
### Learning rate, regularization and gradient clipping {#tips-hyperparams}
-By default spaCy uses the Adam solver, with default settings (`learn_rate=0.001`,
-`beta1=0.9`, `beta2=0.999`). Some researchers have said they found
-these settings terrible on their problems – but they've always performed very
-well in training spaCy's models, in combination with the rest of our recipe. You
-can change these settings directly, by modifying the corresponding attributes on
-the `optimizer` object. You can also set environment variables, to adjust the
-defaults.
+By default spaCy uses the Adam solver, with default settings
+(`learn_rate=0.001`, `beta1=0.9`, `beta2=0.999`). Some researchers have said
+they found these settings terrible on their problems – but they've always
+performed very well in training spaCy's models, in combination with the rest of
+our recipe. You can change these settings directly, by modifying the
+corresponding attributes on the `optimizer` object. You can also set environment
+variables, to adjust the defaults.
There are two other key hyper-parameters of the solver: `L2` **regularization**,
and **gradient clipping** (`max_grad_norm`). Gradient clipping is a hack that's
diff --git a/website/meta/languages.json b/website/meta/languages.json
index cfa468d7f..ef336ef5f 100644
--- a/website/meta/languages.json
+++ b/website/meta/languages.json
@@ -104,6 +104,7 @@
{ "code": "ga", "name": "Irish" },
{ "code": "bn", "name": "Bengali", "has_examples": true },
{ "code": "hi", "name": "Hindi", "example": "यह एक वाक्य है।", "has_examples": true },
+ { "code": "mr", "name": "Marathi" },
{ "code": "kn", "name": "Kannada" },
{ "code": "ta", "name": "Tamil", "has_examples": true },
{
@@ -153,6 +154,20 @@
"example": "これは文章です。",
"has_examples": true
},
+ {
+ "code": "ko",
+ "name": "Korean",
+ "dependencies": [
+ {
+ "name": "mecab-ko",
+ "url": "https://bitbucket.org/eunjeon/mecab-ko/src/master/README.md"
+ },
+ { "name": "mecab-ko-dic", "url": "https://bitbucket.org/eunjeon/mecab-ko-dic" },
+ { "name": "natto-py", "url": "https://github.com/buruzaemon/natto-py" }
+ ],
+ "example": "이것은 문장입니다.",
+ "has_examples": true
+ },
{
"code": "vi",
"name": "Vietnamese",
diff --git a/website/src/widgets/landing.js b/website/src/widgets/landing.js
index f55aa5aa3..e9dec87f4 100644
--- a/website/src/widgets/landing.js
+++ b/website/src/widgets/landing.js
@@ -152,20 +152,21 @@ const Landing = ({ data }) => {
<LandingBannerGrid>
<LandingBanner
title="spaCy IRL 2019: Two days of NLP"
- label="Join us in Berlin"
- to="https://irl.spacy.io/2019"
- button="Get tickets"
+ label="Watch the videos"
+ to="https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc"
+ button="Watch the videos"
background="#ffc194"
backgroundImage={irlBackground}
color="#1a1e23"
small
>
- We're pleased to invite the spaCy community and other folks working on Natural
+ We were pleased to invite the spaCy community and other folks working on Natural
Language Processing to Berlin this summer for a small and intimate event{' '}
- <strong>July 5-6, 2019</strong>. The event includes a hands-on training day for
- teams using spaCy in production, followed by a one-track conference. We've
- booked a beautiful venue, hand-picked an awesome lineup of speakers and
- scheduled plenty of social time to get to know each other and exchange ideas.
+ <strong>July 6, 2019</strong>. We booked a beautiful venue, hand-picked an
+ awesome lineup of speakers and scheduled plenty of social time to get to know
+ each other and exchange ideas. The YouTube playlist includes 12 talks about NLP
+ research, development and applications, with keynotes by Sebastian Ruder
+ (DeepMind) and Yoav Goldberg (Allen AI).
</LandingBanner>
<LandingBanner