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hybrid_model.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForQuestionAnswering
+from transformers import pipeline, PretrainedConfig
+
+from huggingface_hub import PyTorchModelHubMixin
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import os
+import json
+        
+class HybridQAModel(nn.Module, PyTorchModelHubMixin):
+    #config_class = HybridQAConfig
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.load_models(config.extractive_id, config.generative_id)
+
+    def can_generate(self):
+        return False
+
+    def load_models(self, extractive_id, generative_id):
+        self.tokenizer_extractive = AutoTokenizer.from_pretrained(extractive_id)
+        self.tokenizer_generative = AutoTokenizer.from_pretrained(generative_id)
+
+        self.model_extractive = AutoModelForQuestionAnswering.from_pretrained(extractive_id)
+        self.model_generative = AutoModelForSeq2SeqLM.from_pretrained(generative_id)
+    
+    def predict(self, question, context):
+        result_gen, conf_gen = self.infer_generative(self.model_generative, self.tokenizer_generative, question)
+        result_ext, conf_ext = self.infer_extractive(self.model_extractive, self.tokenizer_extractive, question, context)
+
+        # print("Generative result: ",result_gen)
+        # print("Confidence: ", conf_gen)
+        # print("Extractive result: ", result_ext)
+        # print("Confidence: ", conf_ext)
+
+        if conf_gen > conf_ext:
+            return {'guess':result_gen, 'confidence':conf_gen}
+        else:
+            return {'guess':result_ext, 'confidence':conf_ext}
+        
+    def infer_generative(self, model, tokenizer, input_text, **generate_kwargs):
+        max_input_length = min(tokenizer.model_max_length, model.config.max_length)
+        input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=max_input_length)
+
+        with torch.no_grad():
+            output_ids = model.generate(input_ids, **generate_kwargs)
+        decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+        output_probs = F.softmax(output_ids.float(), dim=-1).squeeze(0)
+        entropy = -(output_probs * torch.log(output_probs)).sum(dim=-1)
+        confidence_score = 1 - entropy.item()
+            
+        model.save_pretrained("./base_models")
+        return decoded_output, confidence_score
+    
+    def infer_extractive(self, model, tokenizer, question, context):
+        qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+        result = qa_pipeline(question=question, context=context)
+        confidence_score = result['score']
+        
+        model.save_pretrained("./base_models")
+        return result['answer'], confidence_score
+
+    def save_pretrained(self, save_directory, **kwargs):
+        if not os.path.exists(save_directory):
+            os.makedirs(save_directory, exist_ok=True)
+        self.config.save_pretrained(save_directory, **kwargs)
+        self.model_extractive.save_pretrained(save_directory, **kwargs)
+        self.tokenizer_extractive.save_pretrained(save_directory, **kwargs)
+        self.model_generative.save_pretrained(save_directory, **kwargs)
+        self.tokenizer_generative.save_pretrained(save_directory, **kwargs)
+
+    def from_pretrained(cls, save_directory, *model_args, **model_kwargs):
+        config = PretrainedConfig.from_pretrained(save_directory)
+        model = HybridQAModel(config)
+        
+        model.model_extractive = AutoModelForQuestionAnswering.from_pretrained(save_directory)
+        model.tokenizer_extractive = AutoTokenizer.from_pretrained(save_directory)
+        model.model_generative = AutoModelForSeq2SeqLM.from_pretrained(save_directory)
+        model.tokenizer_generative = AutoTokenizer.from_pretrained(save_directory)
+        
+        return model