NEC with GLiNER

src/common_interface.py

@@ -4,18 +4,19 @@ Makes evaluating models easier.
 """
 from src.models.llms_interface import available_models as llms
 from src.models.GLiNER import find_entities as find_entities_gliner
+from src.models.GLiNER import classify_entity as classify_entity_gliner
 from src.experiments.NER_with_T5 import classify_entity as classify_entity_t5
 from src.experiments.NER_with_LLMs.NER_with_LLMs import find_entities as find_entities_llm
 
 
 def classify_entity(model_name, sentence, entity, labels):
     """
-    Entity Classification
+    NEC. Returns label (string) for entity.
     """
     if model_name == "T5":
         return classify_entity_t5(sentence, entity, labels)
     elif model_name == "GLiNER":
-        pass  # todo
+        return classify_entity_gliner(sentence, entity, labels)
 
 
 def predict_mask_mlm(model_name, masked_sentence, labels):
@@ -32,7 +33,7 @@ def predict_mask_nli(model_name, masked_sentence, labels):
 
 def find_entities(model_name, sentence, labels):
     """
-    NER
+    NER. Returns list of pairs [(entity, label), ...]
     """
     if model_name in llms:
         return find_entities_llm(model_name, sentence, labels)

src/experiments/GLiNER_evalutaion/evaluation.py

@@ -4,6 +4,6 @@ Evaluates GLiNER as SotA  and plots results using reusable functions in plotter.
 
 from src.metrics import NER_metrics, read_NER_metrics
 
-NER_metrics("GLiNER", "CoNLL", "results", test_instances=100)
+# NER_metrics("GLiNER", "CoNLL", "results", test_instances=100)
 
 read_NER_metrics("results")

src/experiments/NEC_evaluation/evaluation.py

+"""
+This file evaluates all NEC approaches.
+"""
+
+# todo: perform tests on datasets and store results
+
+# todo read results and compare the models / plot the results

src/experiments/NER_with_T5.py

 from src.models.T5 import infer_nli
 
+
 def classify_entity(sentence, entity, labels):
     print("classify entity")
     for label in labels:

src/models/GLiNER.py

@@ -16,3 +16,13 @@ def find_entities(sentence, labels):
         entity_list.append((entity["text"], entity["label"]))
 
     return entity_list
+
+
+def classify_entity(sentence, entity, labels):
+    entity_list = find_entities(sentence, labels)
+    for e in entity_list:
+        if e[0] == entity:
+            return e[1]  # Return label
+
+    return ""
+

src/models/T5.py

@@ -10,9 +10,10 @@ tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 print("Finished loading model: T5 NLI")
 
+
 def infer_nli(premise, hypothesis):
     input_text = f"nli hypothesis: {hypothesis} premise: {premise}"
-    
+
     print("tokenize")
     inputs = tokenizer(input_text, return_tensors="pt")
 
@@ -24,19 +25,21 @@ def infer_nli(premise, hypothesis):
 
     return result
 
+
 def preprocess_data(sample):
     input_text = f"nli hypothesis: {sample['hypothesis']} premise: {sample['premise']}"
     target_text = label_map[bool(sample['entailment'])]
-    
+
     tokenized_input = tokenizer(input_text, padding="max_length", truncation=True, max_length=512)
     tokenized_target = tokenizer(target_text, padding="max_length", truncation=True, max_length=10)
 
     tokenized_input["labels"] = tokenized_target["input_ids"]
     return tokenized_input
 
+
 def finetune_model(premises, hypotheses, entailment):
     # TODO: should we use dataset on a higher level as well?
-    data_dict = { "premise": premises, "hypothesis": hypotheses, "entailment": entailment}
+    data_dict = {"premise": premises, "hypothesis": hypotheses, "entailment": entailment}
     dataset = Dataset.from_dict(data_dict)
 
     print(dataset)
@@ -78,6 +81,3 @@ def finetune_model(premises, hypotheses, entailment):
     )
 
     trainer.train()
-
-
-

src/plotter.py

@@ -14,3 +14,6 @@ def plot_bars(data, x_column, y_column, grouping, title, ylabel, xlabel):
     """
     Reusable barchart plotting function
     """
+
+
+# todo: bar chart with three grouped columns for each model: precision, recall, f1-score
\ No newline at end of file

tests/test_NEC.py

 from src.common_interface import classify_entity
 
-tested_models = ["T5"]
+tested_models = ["GLiNER", "T5"]
 
-test_sentences = ["Barack Obama was the president of the United States."]
+test_sentence = "Barack Obama was the president of the United States."
 
-test_entity = ["Barack Obama"]
+test_entities = ["Barack Obama", "United States"]
 
-true_labels = ["person"]
+labels = ["person", "organization", "time", "location", "miscellaneous"]
 
-labels = ["person", "organization", "time", "location"]
 
 print("Test NEC")
 
 for model in tested_models:
-    for index in range(len(test_sentences)):
-        classify_entity(model, test_sentences[index], test_entity[index], labels)
+    print("\n")
+    for test_entity in test_entities:
+        print(f"{model} prediction for {test_entity}:")
+        print(classify_entity(model, test_sentence, test_entity, labels))