#55 - Add FlairNLP Sequence Tagging

* Implemented script to use the Flair SequenceTagger.
* Added option to not use sentences to the script.
* Fixed the whitespace problem!
* Added licenser header. Removed debug statement.
* Added class to contrib models table.
* Upgraded dkpro-cassis to 0.9.1 to ensure dependency compatibility with flair 0.13.1 (both can use more-itertools 0.8.14 now)

README.md

@@ -108,6 +108,7 @@ following table provides an overview about them:
 | SpacyPosClassifier | Part-of-speech prediction with [spaCy](https://spacy.io/) | no |
 | AdapterSequenceTagger | Sequence tagger using [Adapters](https://adapterhub.ml/) | no |
 | AdapterSentenceClassifier | Sentence classifier using [Adapters](https://adapterhub.ml/) | no |
+| FlairNERClassifier | Sequence tagger using [Flair](https://flairnlp.github.io/) | no |
 
 For using trainable recommenders it is important to check the checkbox *Trainable* when adding
 the external recommender to your project. To be able to get predictions of a added trainable

ariadne/contrib/flair.py

@@ -0,0 +1,75 @@
+# Licensed to the Technische Universität Darmstadt under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The Technische Universität Darmstadt
+# licenses this file to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pathlib import Path
+
+from cassis import Cas
+
+from flair.nn import Classifier as Tagger
+from flair.data import Sentence
+
+from ariadne.classifier import Classifier
+from ariadne.contrib.inception_util import create_prediction, SENTENCE_TYPE, TOKEN_TYPE
+
+
+class FlairNERClassifier(Classifier):
+    def __init__(self, model_name: str, model_directory: Path = None, split_sentences: bool = True):
+        super().__init__(model_directory=model_directory)
+        self._model = Tagger.load(model_name)
+        self._split_sentences = split_sentences
+
+    def predict(self, cas: Cas, layer: str, feature: str, project_id: str, document_id: str, user_id: str): 
+        # Extract the sentences from the CAS
+        if self._split_sentences:
+            cas_sents = cas.select(SENTENCE_TYPE)
+            sents = [Sentence(sent.get_covered_text(), use_tokenizer=False) for sent in cas_sents]
+            offsets = [sent.begin for sent in cas_sents]
+
+            # Find the named entities
+            self._model.predict(sents)
+
+            for offset, sent in zip(offsets, sents):
+                # For every entity returned by spacy, create an annotation in the CAS
+                for named_entity in sent.to_dict()["entities"]:
+                    begin = named_entity["start_pos"] + offset
+                    end = named_entity["end_pos"] + offset
+                    label = named_entity["labels"][0]["value"]
+                    prediction = create_prediction(cas, layer, feature, begin, end, label)
+                    cas.add(prediction) 
+
+        else:
+            cas_tokens = cas.select(TOKEN_TYPE)
+
+            # build sentence with correct whitespaces
+            # (when using sentences, this should not be a problem afaik)
+            text = ""
+            last_end = 0
+            for cas_token in cas_tokens:
+                if cas_token.begin == last_end:
+                    text += cas_token.get_covered_text()
+                else:
+                    text += " " + cas_token.get_covered_text()
+                last_end = cas_token.end
+
+            sent = Sentence(text, use_tokenizer=False)
+
+            self._model.predict(sent)
+
+            for named_entity in sent.to_dict()["entities"]:
+                begin = named_entity["start_pos"]
+                end = named_entity["end_pos"]
+                label = named_entity["labels"][0]["value"]
+                prediction = create_prediction(cas, layer, feature, begin, end, label)
+                cas.add(prediction)

setup.py

@@ -28,12 +28,12 @@
 HOMEPAGE = "https://inception-project.github.io/"
 EMAIL = "inception-users@googlegroups.com"
 AUTHOR = "The INCEpTION team"
-REQUIRES_PYTHON = ">=3.6.0"
+REQUIRES_PYTHON = ">=3.8.0"
 
 install_requires = [
     "flask",
     "filelock",
-    "dkpro-cassis>=0.7.6",
+    "dkpro-cassis>=0.9.1",
     "joblib",
     "gunicorn",
     "deprecation",
@@ -50,7 +50,8 @@
     "sentence-transformers~=2.2.2",
     "lightgbm~=4.2.0",
     "diskcache~=5.2.1",
-    "simalign~=0.4"
+    "simalign~=0.4",
+    "flair>=0.13.1"
 ]
 
 test_dependencies = [