release 4.0

huggingface · Apr 8, 2019 · cfe0eda · cfe0eda
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Showing 3 changed files with 8 additions and 4 deletions.
diff --git a/README.md b/README.md
@@ -189,7 +189,7 @@ Here is the full list of these parameters and their descriptions:
 |`max_dist_match` |int                      |The system will consider linking the current mention to a preceding one further than `max_dist` away if they share a noun or proper noun. In this case, it looks `max_dist_match` away instead. The default value is 500.
 |`blacklist`      |boolean                  |Should the system resolve coreferences for pronouns in the following list: `["i", "me", "my", "you", "your"]`. The default value is True (coreference resolved).
 |`store_scores`   |boolean                  |Should the system store the scores for the coreferences in annotations. The default value is True.
-|`conv_dict`      |dict(str, list(str))     |A conversion dictionary that you can use to replace the embeddings of *rare words* (keys) by an average of the embeddings of a list of *common words* (values). Ex: `conv_dict={"Angela": ["woman", "girl"]}` will help resolving coreferences for `Angela` by using the embeddings for the more common `woman` and `girl` instead of the embedding of `Angela`.
+|`conv_dict`      |dict(str, list(str))     |A conversion dictionary that you can use to replace the embeddings of *rare words* (keys) by an average of the embeddings of a list of *common words* (values). Ex: `conv_dict={"Angela": ["woman", "girl"]}` will help resolving coreferences for `Angela` by using the embeddings for the more common `woman` and `girl` instead of the embedding of `Angela`. This currently only works for single words (not for words groups).
 
 ### How to change a parameter
 
@@ -236,7 +236,7 @@ nlp.remove_pipe("neuralcoref")
 coref = neuralcoref.NeuralCoref(nlp.vocab, conv_dict={'Deepika': ['woman', 'actress']})
 nlp.add_pipe(coref, name='neuralcoref')
 # or after NeuralCoref is already in SpaCy's pipe, by modifying NeuralCoref in the pipeline
-nlp.pipeline[-1][-1].set_conv_dict({'Deepika': ['woman', 'actress']})
+nlp.get_pipe('neuralcoref').set_conv_dict({'Deepika': ['woman', 'actress']})
 
 # Let's try agin with the conversion dictionary:
 doc = nlp(u'Deepika has a dog. She loves him. The movie star has always been fond of animals')

diff --git a/neuralcoref/__init__.py b/neuralcoref/__init__.py
@@ -7,11 +7,15 @@
 import tempfile
 import logging
 
+# Filter Cython warnings that would force everybody to re-compile from source (like https://github.com/numpy/numpy/pull/432).
+import warnings
+warnings.filterwarnings("ignore", message="spacy.strings.StringStore size changed,")
+
 from .neuralcoref import NeuralCoref
 from .file_utils import NEURALCOREF_MODEL_URL, NEURALCOREF_MODEL_PATH, NEURALCOREF_CACHE, cached_path
 
 __all__ = ['NeuralCoref', 'add_to_pipe']
-__version__ = "3.9.0"
+__version__ = "4.0.0"
 
 logger = logging.getLogger(__name__)
 

diff --git a/setup.py b/setup.py
@@ -201,7 +201,7 @@ def setup_package():
                     extra_link_args=extra_link_args))
 
         setup(name='neuralcoref',
-            version='3.9',
+            version='4.0',
             description="Coreference Resolution in spaCy with Neural Networks",
             url='https://github.com/huggingface/neuralcoref',
             author='Thomas Wolf',