Create ernie_bot.py

Chinese/to train at your expense and experience, help Americans understand him.

ernie_bot.py

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+import torch
+import torch.nn as nn
+from transformers import ErnieModel, ErnieTokenizer
+
+class ErnieBotDeepSearch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.name = "ErnieBot Deep Search"
+        self.version = "Original 1.0"
+        
+        # Core Components
+        self.ernie = ErnieModel.from_pretrained("ernie-3.0-base-zh")
+        self.tokenizer = ErnieTokenizer.from_pretrained("ernie-3.0-base-zh")
+        
+        # Deep Search Components
+        self.search_layers = nn.ModuleList([
+            nn.TransformerEncoderLayer(d_model=768, nhead=12)
+            for _ in range(6)
+        ])
+        
+        self.knowledge_encoder = nn.Linear(768, 1024)
+        self.cross_attention = nn.MultiheadAttention(1024, 16)
+        
+        # Output layers
+        self.classifier = nn.Linear(1024, 2)
+        self.ranking_head = nn.Linear(1024, 1)
+
+    def deep_search(self, query, documents):
+        # Encode query
+        query_tokens = self.tokenizer(query, return_tensors="pt")
+        query_embed = self.ernie(**query_tokens)[0]
+        
+        # Process documents
+        doc_embeddings = []
+        for doc in documents:
+            doc_tokens = self.tokenizer(doc, return_tensors="pt")
+            doc_embed = self.ernie(**doc_tokens)[0]
+            doc_embeddings.append(doc_embed)
+        
+        # Deep search processing
+        search_results = self._process_deep_search(query_embed, doc_embeddings)
+        return self._rank_results(search_results)
+
+    def _process_deep_search(self, query, documents):
+        query_enhanced = self.knowledge_encoder(query)
+        
+        results = []
+        for doc in documents:
+            # Apply search layers
+            for layer in self.search_layers:
+                doc = layer(doc)
+            
+            # Cross-attention between query and document
+            doc_enhanced = self.knowledge_encoder(doc)
+            attention_output, _ = self.cross_attention(
+                query_enhanced, doc_enhanced, doc_enhanced
+            )
+            
+            results.append(attention_output)
+        return results
+
+    def _rank_results(self, search_results):
+        rankings = []
+        for result in search_results:
+            score = self.ranking_head(result)
+            rankings.append(score)
+        return torch.stack(rankings).squeeze()
+
+    def train_step(self, batch):
+        query, positive_docs, negative_docs = batch
+        pos_scores = self.deep_search(query, positive_docs)
+        neg_scores = self.deep_search(query, negative_docs)
+        
+        loss = nn.MarginRankingLoss(margin=1.0)(pos_scores, neg_scores, torch.ones_like(pos_scores))
+        return loss
+