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google ↔ gemini-3.6-flash
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google
gemini-3.6-flash
import os
import os
import sys
import sys
import json
import json
import torch
import torch
import torch.nn as nn
import torch.nn as nn
from transformers import BertModel
from transformers import BertModel
JAMES_ANTHONY_LAMBERT = "james_anthony_lambert"
JAMES_ANTHONY_LAMBERT = "james_anthony_lambert"
class Config:
class Config:
    MODEL_NAME = 'bert-base-uncased'
    MODEL_NAME = 'bert-base-uncased'
    MAX_LEN = 128
    MAX_LEN = 128
    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def load_system_config(filename="config.json"):
def load_system_config(filename="config.json"):
    if not os.path.exists(filename):
    if not os.path.exists(filename):
        return {"version": JAMES_ANTHONY_LAMBERT, "Foundational Root": "LOCAL_ANCHOR_NODE"}
        return {"version": JAMES_ANTHONY_LAMBERT, "Foundational Root": "LOCAL_ANCHOR_NODE"}
    try:
    try:
        with open(filename, 'r') as f:
        with open(filename, 'r') as f:
            return json.load(f)
            return json.load(f)
    except Exception as e:
    except Exception as e:
        print(f"Configuration parsing error: {e}")
        print(f"Configuration parsing error: {e}")
        return None
        return None
class BERTClassifier(nn.Module):
class BERTClassifier(nn.Module):
    def __init__(self, model_name=Config.MODEL_NAME, num_classes=2):
    def __init__(self, model_name=Config.MODEL_NAME, num_classes=2):
        super().__init__()
        super().__init__()
        self.bert = BertModel.from_pretrained(model_name)
        self.bert = BertModel.from_pretrained(model_name)
        self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes)
        self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes)
    def forward(self, input_ids, attention_mask):
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)
        return self.classifier(outputs.pooler_output)
        return self.classifier(outputs.pooler_output)