added proxy model for humanizer highlighter

predictors.py

@@ -19,6 +19,7 @@ from scipy.special import softmax
 import yaml
 import os
 from utils import *
+import joblib
 
 with open("config.yaml", "r") as file:
     params = yaml.safe_load(file)
@@ -55,11 +56,19 @@ for model_name, model in zip(mc_label_map, text_1on1_models):
     ).to(device)
 
 # proxy models for explainability
-mini_model_name = "polygraf-ai/bc-model-bert-mini"
-bc_tokenizer_mini = AutoTokenizer.from_pretrained(mini_model_name)
-bc_model_mini = AutoModelForSequenceClassification.from_pretrained(mini_model_name).to(
-    device
-)
+mini_bc_model_name = "polygraf-ai/bc-model-bert-mini"
+bc_tokenizer_mini = AutoTokenizer.from_pretrained(mini_bc_model_name)
+bc_model_mini = AutoModelForSequenceClassification.from_pretrained(
+    mini_bc_model_name
+).to(device)
+mini_humanizer_model_name = "polygraf-ai/quillbot-detector-bert-mini-9K"
+humanizer_tokenizer_mini = AutoTokenizer.from_pretrained(mini_humanizer_model_name)
+humanizer_model_mini = AutoModelForSequenceClassification.from_pretrained(
+    mini_humanizer_model_name
+).to(device)
+
+# model score calibration
+iso_reg = joblib.load("isotonic_regression_model.joblib")
 
 
 def split_text_allow_complete_sentences_nltk(
@@ -164,8 +173,8 @@ def predict_for_explainanility(text, model_type=None):
     if model_type == "quillbot":
         cleaning = False
         max_length = 256
-        model = quillbot_model
-        tokenizer = quillbot_tokenizer
+        model = humanizer_model_mini
+        tokenizer = humanizer_tokenizer_mini
     elif model_type == "bc":
         cleaning = True
         max_length = 512
@@ -267,6 +276,12 @@ def predict_bc_scores(input):
     average_bc_scores = np.mean(bc_scores_array, axis=0)
     bc_score_list = average_bc_scores.tolist()
     bc_score = {"AI": bc_score_list[1], "HUMAN": bc_score_list[0]}
+    # print(f"Original BC scores: AI: {bc_score_list[1]}, HUMAN: {bc_score_list[0]}")
+    # isotonic regression calibration
+    # ai_score = iso_reg.predict([bc_score_list[1]])[0]
+    # human_score = 1 - ai_score
+    # bc_score = {"AI": ai_score, "HUMAN": human_score}
+    # print(f"Calibration BC scores: AI: {ai_score}, HUMAN: {human_score}")
     return bc_score
 
 

requirements.txt

@@ -24,4 +24,5 @@ pymupdf
 sentence-transformers
 Unidecode
 python-dotenv
-lime
+lime
+joblib