#63 - Add Transformer-based NER classifier using Hugging Face models

* Add Transformer-based NER classifier using Hugging Face models
* Add test file to transformers classifier
* Fix the transformer classifier
* Update dependencies

ariadne/contrib/transformers.py

@@ -0,0 +1,42 @@
+# 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 transformers import  pipeline, AutoTokenizer, AutoModelForTokenClassification
+from ariadne.classifier import Classifier
+from ariadne.contrib.inception_util import create_prediction
+from cassis import Cas
+
+class TransformerNerClassifier(Classifier):
+    def __init__(self, model_name: str):
+        super().__init__()
+        # Load the Hugging Face model and tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=512)
+        self.model = AutoModelForTokenClassification.from_pretrained(model_name)
+        self.ner_pipeline = pipeline("ner", model=self.model, tokenizer=self.tokenizer, aggregation_strategy="first")
+
+
+
+    def predict(self, cas: Cas, layer: str, feature: str, project_id: str, document_id: str, user_id: str):
+
+        document_text = cas.sofa_string
+        predictions = self.ner_pipeline(document_text)
+        for prediction in predictions:
+            start_char = prediction['start']
+            end_char = prediction['end']
+            label = prediction['entity_group']
+            cas_prediction = create_prediction(cas, layer, feature, start_char, end_char, label)
+            cas.add(cas_prediction) 
+

setup.py

@@ -51,14 +51,15 @@
     "lightgbm~=4.2.0",
     "diskcache~=5.2.1",
     "simalign~=0.4",
-    "flair>=0.13.1"
+    "flair>=0.13.1",
+    "transformers[torch]~=4.41.1", # TransformerNerClassifier
 ]
 
 test_dependencies = [
     "tox",
     "pytest",
     "codecov",
-    "pytest-cov",
+    "pytest-cov", 
 ]
 
 dev_dependencies = [

tests/test_transformer_recommender.py

@@ -0,0 +1,34 @@
+# 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.
+import pytest
+
+pytest.importorskip("transformers")
+
+from ariadne.contrib.transformers import TransformerNerClassifier  
+from tests.util import load_obama, PREDICTED_TYPE, PREDICTED_FEATURE, PROJECT_ID, USER
+
+
+def test_predict_ner(tmpdir_factory):
+    cas = load_obama()
+    sut = TransformerNerClassifier("lfcc/lusa_events")
+
+    sut.predict(cas, PREDICTED_TYPE, PREDICTED_FEATURE, PROJECT_ID, "doc_42", USER)
+    predictions = list(cas.select(PREDICTED_TYPE))
+
+    assert len(predictions)
+
+    for prediction in predictions:
+        assert getattr(prediction, PREDICTED_FEATURE) is not None