--- license: apache-2.0 --- from gliner import GLiNER from utca.core import RenameAttribute from utca.implementation.predictors import GLiNERPredictor, GLiNERPredictorConfig from utca.implementation.tasks import ( GLiNER as UTCAGLiNER, GLiNERPreprocessor, GLiNERRelationExtraction, GLiNERRelationExtractionPreprocessor, ) import time from typing import Dict, List import json def measure_time(func): def wrapper(*args, **kwargs): start_time = time.time() result = func(*args, **kwargs) end_time = time.time() execution_time = end_time - start_time print(f"Execution time of {func.__name__}: {execution_time:.6f} seconds") return result return wrapper class GLiNERTester: def __init__(self, model_name: str = r"C:\Users\doren\PycharmProjects\GlinerFineTuning\data\checkpoint-100000", device: str = "cuda:0"): # Initialize the basic model for most tasks self.model = GLiNER.from_pretrained(model_name) # Initialize the relation extraction pipeline self.predictor = GLiNERPredictor( GLiNERPredictorConfig( model_name=model_name, device=device ) ) # Build the relation extraction pipeline self.relation_pipe = ( UTCAGLiNER( predictor=self.predictor, preprocess=GLiNERPreprocessor(threshold=0.5) ) | RenameAttribute("output", "entities") | GLiNERRelationExtraction( predictor=self.predictor, preprocess=( GLiNERPreprocessor(threshold=0.5) | GLiNERRelationExtractionPreprocessor() ) ) ) self.results = {} @measure_time def test_ner(self) -> Dict: """Test Named Entity Recognition capabilities""" print("\nTesting NER...") text = """ Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014. """ labels = ["founder", "computer", "software", "position", "date"] start_time = time.time() entities = self.model.predict_entities(text, labels) duration = time.time() - start_time return { "task": "ner", "entities": [{"text": e["text"], "label": e["label"], "score": e["score"]} for e in entities], "duration": duration } @measure_time def test_relation_extraction(self) -> Dict: """Test Relation Extraction capabilities""" print("\nTesting Relation Extraction...") text = """ Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect. """ start_time = time.time() result = self.relation_pipe.run({ "text": text, "labels": ["organisation", "founder", "position", "date"], "relations": [{ "relation": "founder", "pairs_filter": [("organisation", "founder")], "distance_threshold": 100, }, { "relation": "inception date", "pairs_filter": [("organisation", "date")], }, { "relation": "held position", "pairs_filter": [("founder", "position")], }] }) duration = time.time() - start_time return { "task": "relation_extraction", "relations": result["output"], "duration": duration } @measure_time def test_qa(self) -> Dict: """Test Question Answering capabilities""" print("\nTesting Question Answering...") question = "Who was the CEO of Microsoft?" text = """ Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014. """ input_ = question + text labels = ["answer"] start_time = time.time() answers = self.model.predict_entities(input_, labels) duration = time.time() - start_time return { "task": "question_answering", "answers": [{"text": a["text"], "score": a["score"]} for a in answers], "duration": duration } @measure_time def test_summarization(self) -> Dict: """Test Summarization capabilities""" print("\nTesting Summarization...") text = """ Several studies have reported its pharmacological activities, including anti-inflammatory, antimicrobial, and antitumoral effects. The effect of E-anethole was studied in the osteosarcoma MG-63 cell line, and the antiproliferative activity was evaluated by an MTT assay. It showed a GI50 value of 60.25 μM with apoptosis induction through the mitochondrial-mediated pathway. """ prompt = "Summarize the given text, highlighting the most important information:\n" input_ = prompt + text labels = ["summary"] start_time = time.time() summaries = self.model.predict_entities(input_, labels, threshold=0.1) duration = time.time() - start_time return { "task": "summarization", "summaries": [{"text": s["text"], "score": s["score"]} for s in summaries], "duration": duration } @measure_time def test_sentiment_extraction(self) -> Dict: """Test Sentiment Extraction capabilities""" print("\nTesting Sentiment Extraction...") text = """ I recently purchased the Sony WH-1000XM4 headphones and I'm thoroughly impressed. The noise-canceling is excellent, though the price is a bit high. The sound quality is amazing but the app could use some improvements. """ labels = ["positive sentiment", "negative sentiment"] start_time = time.time() sentiments = self.model.predict_entities(text, labels) duration = time.time() - start_time return { "task": "sentiment_extraction", "sentiments": [{"text": s["text"], "label": s["label"], "score": s["score"]} for s in sentiments], "duration": duration } @measure_time def test_entity_disambiguation(self) -> Dict: """Test Entity Disambiguation capabilities""" print("\nTesting Entity Disambiguation...") text = """ Paris is the capital of France. Paris Hilton is an American media personality. Mercury is a planet in our solar system. Mercury is also a chemical element. """ labels = ["location Paris", "person Paris", "planet Mercury", "element Mercury"] start_time = time.time() entities = self.model.predict_entities(text, labels) duration = time.time() - start_time return { "task": "entity_disambiguation", "entities": [{"text": e["text"], "label": e["label"], "score": e["score"]} for e in entities], "duration": duration } def run_all_tests(self) -> Dict: """Run all available tests and store results""" print("Starting GLiNER comprehensive test suite...") self.results = { "ner": self.test_ner(), "relation_extraction": self.test_relation_extraction(), "qa": self.test_qa(), "summarization": self.test_summarization(), "sentiment_extraction": self.test_sentiment_extraction(), "entity_disambiguation": self.test_entity_disambiguation() } # Save results to JSON file with open('gliner_test_results.json', 'w') as f: json.dump(self.results, f, indent=4) print("\nAll tests completed. Results saved to 'gliner_test_results.json'") return self.results def main(): # Initialize tester with GPU if available try: tester = GLiNERTester(device="cuda:0") print("Using GPU for testing") except: tester = GLiNERTester(device="cpu") print("Using CPU for testing") # Run all tests results = tester.run_all_tests() # Print summary of results print("\nTest Summary:") for task, result in results.items(): print(f"\n{task.upper()}:") print(f"Duration: {result['duration']:.2f} seconds") print(f"Results: ", result) if 'entities' in result: print(f"Found {len(result['entities'])} entities") elif 'answers' in result: print(f"Found {len(result['answers'])} answers") elif 'summaries' in result: print(f"Generated {len(result['summaries'])} summary segments") elif 'sentiments' in result: print(f"Found {len(result['sentiments'])} sentiment expressions") if __name__ == "__main__": main() Test Summary: NER: Duration: 0.41 seconds Results: {'task': 'ner', 'entities': [{'text': 'Bill Gates', 'label': 'founder', 'score': 0.999995768070221}, {'text': 'Paul Allen', 'label': 'founder', 'score': 0.9999948740005493}, {'text': 'April 4, 1975', 'label': 'date', 'score': 0.9999996423721313}, {'text': 'BASIC interpreters', 'label': 'software', 'score': 0.9999961853027344}, {'text': 'Altair 8800', 'label': 'computer', 'score': 0.9999923706054688}, {'text': 'chairman', 'label': 'position', 'score': 0.9999326467514038}, {'text': 'chief executive officer', 'label': 'position', 'score': 0.9999247193336487}, {'text': 'president', 'label': 'position', 'score': 0.9999806880950928}, {'text': 'chief software architect', 'label': 'position', 'score': 0.9999625086784363}, {'text': 'largest \n individual shareholder', 'label': 'position', 'score': 0.9741785526275635}], 'duration': 0.4105691909790039} Found 10 entities RELATION_EXTRACTION: Duration: 0.31 seconds Results: {'task': 'relation_extraction', 'relations': [{'source': {'start': 9, 'end': 18, 'span': 'Microsoft', 'score': 0.9999996423721313, 'entity': 'organisation'}, 'relation': 'founder', 'target': {'start': 34, 'end': 44, 'span': 'Bill Gates', 'score': 0.9999998211860657, 'entity': 'founder'}, 'score': 0.9999523162841797}, {'source': {'start': 9, 'end': 18, 'span': 'Microsoft', 'score': 0.9999996423721313, 'entity': 'organisation'}, 'relation': 'founder', 'target': {'start': 49, 'end': 59, 'span': 'Paul Allen', 'score': 0.9999998807907104, 'entity': 'founder'}, 'score': 0.999999463558197}, {'source': {'start': 9, 'end': 18, 'span': 'Microsoft', 'score': 0.9999996423721313, 'entity': 'organisation'}, 'relation': 'inception date', 'target': {'start': 63, 'end': 76, 'span': 'April 4, 1975', 'score': 1.0, 'entity': 'date'}, 'score': 0.9999998807907104}, {'source': {'start': 167, 'end': 176, 'span': 'Microsoft', 'score': 0.9999998807907104, 'entity': 'organisation'}, 'relation': 'inception date', 'target': {'start': 63, 'end': 76, 'span': 'April 4, 1975', 'score': 1.0, 'entity': 'date'}, 'score': 0.9999998807907104}, {'source': {'start': 34, 'end': 44, 'span': 'Bill Gates', 'score': 0.9999998211860657, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 206, 'end': 214, 'span': 'chairman', 'score': 0.9999998807907104, 'entity': 'position'}, 'score': 0.999997615814209}, {'source': {'start': 34, 'end': 44, 'span': 'Bill Gates', 'score': 0.9999998211860657, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 225, 'end': 248, 'span': 'chief executive officer', 'score': 0.9999997019767761, 'entity': 'position'}, 'score': 0.9999843835830688}, {'source': {'start': 34, 'end': 44, 'span': 'Bill Gates', 'score': 0.9999998211860657, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 250, 'end': 259, 'span': 'president', 'score': 0.9999998807907104, 'entity': 'position'}, 'score': 0.9999969005584717}, {'source': {'start': 34, 'end': 44, 'span': 'Bill Gates', 'score': 0.9999998211860657, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 264, 'end': 288, 'span': 'chief software architect', 'score': 0.9999998807907104, 'entity': 'position'}, 'score': 0.9999908208847046}], 'duration': 0.30675745010375977} QA: Duration: 0.48 seconds Results: {'task': 'question_answering', 'answers': [{'text': 'Bill Gates', 'score': 0.9978553056716919}], 'duration': 0.4841444492340088} Found 1 answers SUMMARIZATION: Duration: 0.42 seconds Results: {'task': 'summarization', 'summaries': [{'text': 'Several studies have reported its pharmacological activities, including anti-inflammatory, \n antimicrobial, and antitumoral effects.', 'score': 0.8983121514320374}, {'text': 'The effect of E-anethole was studied in the osteosarcoma \n MG-63 cell line, and the antiproliferative activity was evaluated by an MTT assay.', 'score': 0.7457365393638611}, {'text': '25 μM with apoptosis induction through the mitochondrial-mediated pathway.', 'score': 0.8508360981941223}], 'duration': 0.41564154624938965} Generated 3 summary segments SENTIMENT_EXTRACTION: Duration: 0.36 seconds Results: {'task': 'sentiment_extraction', 'sentiments': [{'text': 'impressed', 'label': 'positive sentiment', 'score': 0.7771905660629272}, {'text': 'excellent', 'label': 'positive sentiment', 'score': 0.6963109374046326}, {'text': 'price is a bit high', 'label': 'negative sentiment', 'score': 0.8551780581474304}, {'text': 'amazing', 'label': 'positive sentiment', 'score': 0.6874173879623413}, {'text': 'app could use some improvements', 'label': 'negative sentiment', 'score': 0.7845857739448547}], 'duration': 0.358095645904541} Found 5 sentiment expressions ENTITY_DISAMBIGUATION: Duration: 0.32 seconds Results: {'task': 'entity_disambiguation', 'entities': [{'text': 'capital of France', 'label': 'location Paris', 'score': 0.8064324855804443}, {'text': 'Paris Hilton', 'label': 'person Paris', 'score': 0.9987842440605164}, {'text': 'Mercury', 'label': 'planet Mercury', 'score': 0.9934960603713989}, {'text': 'Mercury', 'label': 'planet Mercury', 'score': 0.9940248131752014}, {'text': 'chemical element', 'label': 'element Mercury', 'score': 0.9640767574310303}], 'duration': 0.32335710525512695} Found 5 entities