Tests with bge_m3_zeroshot_v2

model/HuggingFace/bge_m3_zeroshot_v2.py

+# https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0
+
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification", model="MoritzLaurer/bge-m3-zeroshot-v2.0")
+candidate_labels = [
+        "Diabetes",
+        "Cancer",
+        "Chronic respiratory disease",
+        "Cardiovascular diseases",
+        "Mental Health",
+        "Diabetes type 1",
+        "Diabetes type 2"
+        ]
+
+def classify(sequence):
+    results = classifier(sequence, candidate_labels)
+
+    return results
+
+    # print(f"Sequence: {sequence_to_classify}")
+    # print(f"Labels: {results["labels"]}")
+    # print(f"Scores: {results["scores"]}")

model/test_model.py

@@ -9,7 +9,8 @@ import time
 import statistics
 
 # from model.HuggingFace.BartLargeMnli import classify
-from model.HuggingFace.BioMedBERT import classify
+# from model.HuggingFace.BioMedBERT import classify
+from model.HuggingFace.bge_m3_zeroshot_v2 import classify
 
 TRESHOLD = 0.6
 
@@ -36,7 +37,8 @@ def predict(article, file):
     return classify(title + " \n " + articleTitle + " \n " + abstract)
 
 # with open("./results/BartLargeMnli.txt", "w") as file:
-with open("./results/BiomedBERT.txt", "w") as file:
+# with open("./results/BiomedBERT.txt", "w") as file:
+with open("./results/bge_m3_zeroshot_v2.txt", "w") as file:
 
     predictions = []
     selected_scores = []