{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":30741,"offset":30731,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u5728\u65b0\u7684 `CrossMamba` \u8bbe\u8ba1\u4e2d\uff0c\u4f60\u91c7\u7528\u4e86\u4e00\u4e9b\u7279\u5b9a\u7684\u7ec4\u4ef6\u548c\u65b9\u6cd5\u6765\u5904\u7406\u53cc\u6a21\u6001\u8f93\u5165\u3002\u4ee5\u4e0b\u662f\u5bf9\u5176\u5173\u952e\u64cd\u4f5c\u7684\u8be6\u7ec6\u89e3\u6790\uff1a\n\n### \u4ee3\u7801\u7ed3\u6784\u5206\u6790\n\n1. **\u521d\u59cb\u5316\u7ec4\u4ef6**\uff1a\n   - **Mamba\u5b9e\u4f8b** (`self.cross_mamba`)\uff1a\u7528\u4e8e\u5904\u7406\u6a21\u6001\u4e4b\u95f4\u7684\u878d\u5408\uff0c\u91c7\u7528 `v3` \u7c7b\u578b\u7684 `bimamba`\u3002\n   - **\u5c42\u5f52\u4e00\u5316** (`LayerNorm`)\uff1a`norm1` \u548c `norm2`\uff0c\u7528\u4e8e\u89c4\u8303\u5316\u7279\u5f81\uff0c\u51cf\u8f7b\u7279\u5f81\u5206\u5e03\u53d8\u5316\u5e26\u6765\u7684\u5f71\u54cd\u3002\n   - **\u6df1\u5ea6\u5377\u79ef** (`dwconv`)\uff1a3x3 \u5377\u79ef\u5c42\u4ee5\u589e\u5f3a\u5c40\u90e8\u7279\u5f81\u63d0\u53d6\u3002\n   - **tanh\u6fc0\u6d3b**\uff1a\u5f15\u5165\u4e86 `Tanh` \u975e\u7ebf\u6027\u6fc0\u6d3b\u51fd\u6570\uff0c\u7528\u4e8e\u8c03\u6574\u878d\u5408\u4e2d\u7684\u7279\u5f81\u6743\u91cd\u3002\n\n2. **\u524d\u5411\u4f20\u64ad\u6d41\u7a0b**\uff1a\n   - **\u6b8b\u5dee\u8fde\u63a5**\uff1a\u901a\u8fc7 `m1_resi = m1 + m1_resi` \u6765\u4fdd\u6301\u539f\u59cb\u7279\u5f81\u4fe1\u606f\u3002\n   \n   - **\u5dee\u5f02\u5f52\u4e00\u5316**\uff1a\n     - \u8ba1\u7b97 `m1` \u548c `m2` \u7684\u5dee\u5f02\u5e76\u4f7f\u7528 `norm1` \u8fdb\u884c\u5c42\u5f52\u4e00\u5316\uff0c\u8fd9\u79cd\u64cd\u4f5c\u786e\u4fdd\u4e86\u7279\u5f81\u5dee\u5f02\u5728\u5904\u7406\u524d\u5448\u73b0\u826f\u597d\u7684\u6570\u503c\u7a33\u5b9a\u6027\u3002\n     - \u4f7f\u7528 `Tanh` \u6fc0\u6d3b\u51fd\u6570\u5bf9\u5dee\u5f02\u7279\u5f81\u8fdb\u884c\u975e\u7ebf\u6027\u53d8\u6362\uff0c\u5f97\u5230\u7684 `probability_weights` \u7528\u6765\u5f3a\u8c03\u6216\u51cf\u5f31\u6a21\u6001\u7279\u5f81\u3002\n\n   - **\u7279\u5f81\u878d\u5408**\uff1a\n     - \u5c06 `m1` \u4e0e `probability_weights` \u76f8\u4e58\uff0c\u5f97\u5230\u52a0\u6743\u540e\u7684 `weighted_m1`\u3002\n     - \u5c06 `weighted_m1` \u548c `m1` \u76f8\u52a0\u751f\u6210 `combined_feature`\uff0c\u878d\u5408\u540e\u7684\u7279\u5f81\u4f5c\u4e3a\u878d\u5408\u6a21\u5757\u7684\u8f93\u5165\u3002\n   \n   - **\u6a21\u6001\u878d\u5408**\uff1a\n     - \u8c03\u7528 `cross_mamba`\uff0c\u7ed3\u5408\u989d\u5916\u7684\u5d4c\u5165 `extra_emb=m2`\uff0c\u8fdb\u884c\u6a21\u6001\u4fe1\u606f\u7684\u6df1\u5ea6\u878d\u5408\u3002\n     - \u6700\u7ec8\u8f93\u51fa `global_f` \u4ee3\u8868\u878d\u5408\u540e\u7684\u5168\u5c40\u7279\u5f81\u3002\n\n### \u4e3b\u8981\u529f\u80fd\u4e0e\u64cd\u4f5c\u5206\u6790\n\n- **\u5dee\u5f02\u8ba1\u7b97\u548c\u975e\u7ebf\u6027\u5904\u7406**\uff1a\u901a\u8fc7\u8ba1\u7b97\u548c\u975e\u7ebf\u6027\u53d8\u6362\u6a21\u6001\u7279\u5f81\u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u76ee\u7684\u662f\u5728\u4e8e\u7a81\u51fa\u4e0d\u540c\u6a21\u6001\u4e4b\u95f4\u7684\u91cd\u8981\u4fe1\u606f\u3002`Tanh` \u5bf9\u7279\u5f81\u53d8\u5316\u8fdb\u884c\u5e73\u6ed1\uff0c\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u907f\u514d\u4e86\u56e0\u6781\u7aef\u7279\u5f81\u503c\u5bfc\u81f4\u7684\u6570\u503c\u4e0d\u7a33\u5b9a\u3002\n\n- **\u52a0\u6743\u878d\u5408\u7b56\u7565**\uff1a\u901a\u8fc7\u52a0\u6743\u8c03\u6574\uff08`m1 * probability_weights`\uff09\u4ee5\u4fbf\u5728\u878d\u5408\u8fc7\u7a0b\u4e2d\u7ed9\u4e88\u7279\u5b9a\u7279\u5f81\u66f4\u9ad8\u7684\u5f71\u54cd\u529b\u3002\u8fd9\u79cd\u65b9\u5f0f\u65e8\u5728\u81ea\u9002\u5e94\u5730\u52a0\u5f3a\u91cd\u8981\u6a21\u6001\u4fe1\u606f\uff0c\u7136\u800c\uff0c\u8fc7\u4e8e\u5e73\u6ed1\u7684\u6743\u91cd\u53ef\u80fd\u65e0\u6cd5\u6709\u6548\u533a\u5206\u51fa\u7cbe\u51c6\u7684\u91cd\u8981\u7279\u5f81\u3002\n\n- **\u4f7f\u7528 `CrossMamba` \u8fdb\u884c\u6df1\u5ea6\u6a21\u6001\u4ea4\u4e92**\uff1a`CrossMamba` \u501f\u52a9 `Mamba` \u5c42\u5c06\u5904\u7406\u8fc7\u7684\u7279\u5f81\u4e0e\u989d\u5916\u5d4c\u5165\u8fdb\u884c\u6df1\u5165\u878d\u5408\uff0c\u5229\u7528\u5176\u5185\u90e8\u7684\u9ad8\u7ea7\u5904\u7406\u673a\u5236\u5b9e\u73b0\u591a\u6a21\u6001\u7684\u4ea4\u4e92\u4f18\u5316\u3002\n\n### \u603b\u7ed3\n\n\u8fd9\u4e2a\u8bbe\u8ba1\u5c55\u793a\u4e86\u4e00\u79cd\u5c42\u6b21\u5316\u7684\u7279\u5f81\u878d\u5408\u65b9\u6cd5\uff0c\u901a\u8fc7\u8ba1\u7b97\u6a21\u6001\u95f4\u5dee\u5f02\u3001\u5f15\u5165\u975e\u7ebf\u6027\u8c03\u6574\u3001\u5e26\u6743\u878d\u5408\u7279\u5f81\uff0c\u5e76\u5229\u7528 `CrossMamba` \u8fdb\u884c\u5168\u5c40\u7efc\u5408\u5904\u7406\uff0c\u53ef\u4ee5\u5728\u7406\u8bba\u4e0a\u6355\u6349\u5230\u6a21\u6001\u95f4\u7684\u590d\u6742\u5173\u7cfb\u3002\u4f46\u9700\u8981\u6ce8\u610f\u5904\u7406\u7cbe\u5ea6\u4e0e\u7ec6\u8282\uff0c\u786e\u4fdd\u5dee\u5f02\u8ba1\u7b97\u3001\u53c2\u6570\u521d\u59cb\u5316\u4ee5\u53ca `tanh` \u7684\u6709\u6548\u6027\uff0c\u4ee5\u4fbf\u6a21\u578b\u80fd\u591f\u5bf9\u4e8e\u590d\u6742\u6570\u636e\u4fdd\u6301\u826f\u597d\u7684\u6027\u80fd\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":2033,"n_turns_in_conversation":28,"n_words":164,"source_row_offset":30731,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"9cdf143effe3d52ae9c45d6a","turn_index":8,"user_text":"\u7efc\u4e0a\uff0c\u5206\u6790\u4e00\u4e0bclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.norm2 = LayerNorm(dim, 'with_bias')\n        self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)\n        self.tanh = nn.Tanh()\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.tanh(diff_feature)\n        weighted_m1 = m1 * probability_weights\n        combined_feature = weighted_m1 + m1\n        global_f = self.cross_mamba(combined_feature, extra_emb=m2)\n        return global_f\u64cd\u4f5c"},{"assistant_initiates":false,"asst_text":"\u5728\u65b0\u7684\u8bbe\u8ba1\u4e2d\uff0c\u4f60\u5bf9 `CrossMamba` \u7684\u524d\u5411\u4f20\u64ad\u6d41\u7a0b\u8fdb\u884c\u4e86\u8c03\u6574\uff0c\u5e76\u5f15\u5165\u4e86\u4e24\u6b21 `Mamba` \u878d\u5408\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u6539\u8fdb\u7248\u8bbe\u8ba1\u7684\u8be6\u7ec6\u5206\u6790\uff1a\n\n### \u65b0\u8bbe\u8ba1\u5206\u6790\n\n1. **\u521d\u59cb\u5316\u7ec4\u4ef6**\uff1a\n   - \u4e0e\u4e4b\u524d\u7248\u672c\u76f8\u540c\uff0c\u5305\u62ec `Mamba` \u5b9e\u4f8b\u3001\u5c42\u5f52\u4e00\u5316\u3001\u6df1\u5ea6\u5377\u79ef\u548c `Tanh` \u6fc0\u6d3b\u51fd\u6570\u3002\n\n2. **\u524d\u5411\u4f20\u64ad\u6d41\u7a0b**\uff1a\n   - **\u6b8b\u5dee\u8fde\u63a5**\uff1a`m1_resi = m1 + m1_resi`\uff0c\u7528\u6765\u5728\u8ba1\u7b97\u4e2d\u4fdd\u7559\u539f\u59cb\u8f93\u5165\u7684\u5f71\u54cd\u3002\n   \n   - **\u5dee\u5f02\u5f52\u4e00\u5316\u4e0e\u6743\u91cd\u8ba1\u7b97**\uff1a\n     - \u8ba1\u7b97 `m1` \u4e0e `m2` \u4e4b\u95f4\u7684\u5dee\u5f02\uff0c\u5229\u7528 `norm1` \u8fdb\u884c\u5c42\u5f52\u4e00\u5316\uff0c\u89c4\u8303\u5316\u8f93\u5165\u7279\u5f81\u7684\u5206\u5e03\u3002\n     - \u901a\u8fc7 `Tanh` \u6fc0\u6d3b\u8fd9\u4e00\u6b65\u8ba1\u7b97 `probability_weights`\uff0c\u5b9e\u73b0\u4e00\u79cd\u5e73\u6ed1\u5316\u7684\u6743\u91cd\u8c03\u6574\u3002\n\n   - **\u7279\u5f81\u52a0\u6743\u4e0e\u878d\u5408**\uff1a\n     - \u5c06 `probability_weights` \u5e94\u7528\u4e8e `m1`\uff0c\u751f\u6210\u52a0\u6743\u540e\u7684\u7279\u5f81 `weighted_m1`\u3002\n     - \u8ba1\u7b97\u878d\u5408\u7279\u5f81 `combined_feature = weighted_m1 + m1`\u3002\n\n   - **\u53cc\u901a\u9053\u6a21\u6001\u4ea4\u4e92**\uff1a\n     - \u4f7f\u7528 `cross_mamba` \u9488\u5bf9 `combined_feature` \u548c `m1` \u5206\u522b\u4e0e `m2` \u8fdb\u884c\u7279\u5f81\u4ea4\u4e92\uff0c\u751f\u6210 `global_f1` \u548c `global_f2`\u3002\n     - \u5c06\u4e0a\u8ff0\u4e24\u4e2a\u5168\u5c40\u7279\u5f81\u76f8\u52a0\u5f97\u51fa\u6700\u7ec8\u7684 `global_f`\uff0c\u63d0\u4f9b\u4e00\u4e2a\u591a\u8def\u5f84\u878d\u5408\u7684\u7279\u5f81\u8f93\u51fa\u3002\n\n### \u8bbe\u8ba1\u610f\u56fe\u4e0e\u53ef\u80fd\u7684\u4f18\u70b9\n\n- **\u591a\u8def\u5f84\u878d\u5408\u673a\u5236**\uff1a\n  - \u901a\u8fc7\u4e24\u6b21\u8c03\u7528 `Mamba` \u5c42\u5b9e\u73b0\u591a\u8def\u5f84\u7684\u7279\u5f81\u878d\u5408\u3002`global_f1` \u4ece\u7ecf\u8fc7\u52a0\u6743\u548c\u5f52\u4e00\u5316\u5904\u7406\u7684\u7279\u5f81\u89d2\u5ea6\u878d\u5165\u66f4\u591a\u4ea4\u4e92\uff0c\u800c `global_f2` \u76f4\u63a5\u4ece\u539f\u59cb `m1` \u51fa\u53d1\u4ee5\u4fdd\u8bc1\u57fa\u7840\u7279\u5f81\u7684\u4e00\u81f4\u6027\u3002\n  - \u878d\u5408\u4e24\u79cd\u98ce\u683c\u7684\u7279\u5f81\u63d0\u53d6\u4e0e\u5904\u7406\uff0c\u53ef\u4ee5\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u51cf\u8f7b\u5355\u4e00\u5904\u7406\u8def\u5f84\u53ef\u80fd\u5bfc\u81f4\u7684\u4fe1\u606f\u635f\u5931\u6216\u504f\u5dee\u3002\n\n- **\u7279\u5f81\u5197\u4f59\u5229\u7528**\uff1a\n  - \u76f8\u6bd4\u4e8e\u4ec5\u57fa\u4e8e\u52a0\u6743\u7279\u5f81\u8fdb\u884c\u5904\u7406\uff0c\u8fd9\u79cd\u8bbe\u8ba1\u5e2e\u52a9\u6a21\u578b\u5728\u5b58\u5728\u7279\u5f81\u6743\u91cd\u4e0d\u5f53\u60c5\u51b5\u65f6\u4fdd\u7559\u6a21\u578b\u7684\u6b63\u5e38\u5de5\u4f5c\u6a21\u5f0f\u3002\n\n### \u6f5c\u5728\u95ee\u9898\u4e0e\u4f18\u5316\u5efa\u8bae\n\n- **\u590d\u6742\u5ea6\u4e0e\u8ba1\u7b97\u6210\u672c**\uff1a\n  - \u8fd9\u79cd\u8bbe\u8ba1\u5f15\u5165\u4e86\u66f4\u591a\u7684\u8ba1\u7b97\u5f00\u9500\uff0c\u5c24\u5176\u662f\u5728\u7279\u5f81\u91cf\u8f83\u5927\u7684\u60c5\u51b5\u4e0b\uff0c\u4e24\u6b21 `cross_mamba` \u8ba1\u7b97\u53ef\u80fd\u589e\u52a0\u5ef6\u8fdf\u3002\n  - \u9700\u8981\u6743\u8861\u5b9e\u9645\u6027\u80fd\u63d0\u5347\u4e0e\u8ba1\u7b97\u6210\u672c\u3002\n\n- **\u53c2\u6570\u4e0e\u8c03\u4f18**\uff1a\n  - \u8bbe\u7f6e\u5408\u9002\u7684\u53c2\u6570\u548c\u521d\u59cb\u6743\u91cd\u6765\u786e\u4fdd\u4e24\u4e2a\u878d\u5408\u8def\u5f84\u7684\u5e73\u8861\u6027\uff0c\u907f\u514d\u5176\u4e2d\u4e00\u8def\u5bf9\u6700\u7ec8\u7279\u5f81\u6709\u8fc7\u5927\u6216\u8fc7\u5c0f\u7684\u5f71\u54cd\u3002\n\n### \u603b\u7ed3\n\n\u901a\u8fc7\u5f15\u5165\u8fd9\u4e9b\u66f4\u590d\u6742\u7684\u7279\u5f81\u8ba1\u7b97\u548c\u591a\u8def\u5f84\u878d\u5408\u65b9\u5f0f\uff0c\u8bbe\u8ba1\u610f\u5728\u66f4\u597d\u5730\u878d\u5408\u591a\u6a21\u6001\u4fe1\u606f\uff0c\u5176\u6f5c\u5728\u7684\u4f18\u52bf\u5728\u4e8e\u6355\u6349\u548c\u878d\u5408\u591a\u79cd\u6a21\u5f0f\u7279\u5f81\u3002\u4f46\u5b9e\u73b0\u4e0a\u4ecd\u9700\u8981\u5728\u5177\u4f53\u573a\u666f\u4e0b\u5bf9\u6027\u80fd\u548c\u590d\u6742\u5ea6\u8fdb\u884c\u5b9e\u9645\u9a8c\u8bc1\u548c\u8c03\u6574\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":2021,"n_turns_in_conversation":28,"n_words":179,"source_row_offset":30732,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"21e59b3e66eb8f5be4ef76ad","turn_index":9,"user_text":"\u90a3\u4e48\u8fd9\u6837\u8bbe\u8ba1\u5462class CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.norm2 = LayerNorm(dim, 'with_bias')\n        self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)\n        self.tanh = nn.Tanh()\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.tanh(diff_feature)\n        weighted_m1 = m1 * probability_weights\n        combined_feature = weighted_m1 + m1\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        global_f = global_f1 + global_f2\n        return global_f"},{"assistant_initiates":false,"asst_text":"\u5c06 `combined_feature = weighted_m1 + m1` \u6539\u4e3a `combined_feature = weighted_m1` \u662f\u4e00\u4e2a\u5173\u952e\u7684\u8bbe\u8ba1\u51b3\u7b56\uff0c\u8fd9\u5c06\u5f71\u54cd\u6a21\u578b\u5982\u4f55\u878d\u5408\u7279\u5f81\u3002\u4ee5\u4e0b\u662f\u5bf9\u8fd9\u4e00\u6539\u53d8\u7684\u8be6\u7ec6\u5206\u6790\uff1a\n\n### \u4fee\u6539\u5206\u6790\n\n1. **\u5f53\u524d\u5b9e\u73b0**\uff1a\n   - `combined_feature = weighted_m1 + m1` \u610f\u5473\u7740 `combined_feature` \u5305\u542b\u4e86\u52a0\u6743\u540e\u7684\u7279\u5f81\u548c\u539f\u59cb\u7279\u5f81\u7684\u7b80\u5355\u7d2f\u52a0\u3002\n   - \u8fd9\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u53ef\u4ee5\u786e\u4fdd\u65e0\u8bba `probability_weights` \u5982\u4f55\u8c03\u6574\uff0c`m1` \u7684\u539f\u59cb\u4fe1\u606f\u90fd\u80fd\u6709\u6240\u4fdd\u7559\uff0c\u4f46\u53ef\u80fd\u5bfc\u81f4\u52a0\u6743\u673a\u5236\u7684\u4f5c\u7528\u88ab\u7a00\u91ca\u3002\n\n2. **\u4fee\u6539\u4e3a `combined_feature = weighted_m1` \u610f\u4e49**\uff1a\n   - **\u786e\u4fdd\u7279\u5f81\u9009\u62e9\u7684\u91cd\u8981\u6027**\uff1a\u8fd9\u6837\u5904\u7406\u4f1a\u66f4\u52a0\u5f3a\u8c03\u4f7f\u7528 `probability_weights` \u6240\u9009\u62e9\u7684\u91cd\u8981\u7279\u5f81\uff0c\u5f3a\u5316\u6a21\u578b\u5728\u878d\u5408\u9636\u6bb5\u7684\u7279\u5f81\u5f3a\u8c03\u80fd\u529b\u3002\n   - **\u51cf\u5c11\u539f\u59cb\u7279\u5f81\u5f71\u54cd**\uff1a\u5254\u9664\u5bf9 `m1` \u7684\u76f4\u63a5\u7d2f\u52a0\u53ef\u4ee5\u907f\u514d\u539f\u59cb\u7279\u5f81\u5bf9\u524d\u9762\u6a21\u6001\u4ea4\u4e92\u4fe1\u606f\u7684\u53ef\u80fd\u5e72\u6270\uff0c\u786e\u4fdd\u5728\u7279\u5f81\u4ea4\u4e92\u4e2d\u66f4\u5173\u6ce8\u4e8e\u7ecf\u8fc7\u52a0\u6743\u8c03\u6574\u540e\u7684\u7279\u5f81\u8868\u73b0\u3002\n   - **\u7b80\u5316\u4fe1\u606f\u6d41\u52a8**\uff1a\u53ef\u4ee5\u7b80\u5316\u7279\u5f81\u7684\u6d41\u52a8\u548c\u5904\u7406\u8def\u5f84\uff0c\u660e\u786e\u52a0\u6743\u673a\u5236\u5bf9\u7279\u5f81\u878d\u5408\u7684\u5f71\u54cd\u3002\n\n### \u6f5c\u5728\u5f71\u54cd\n\n- **\u6b63\u9762\u5f71\u54cd**\uff1a\n  - \u6a21\u578b\u53ef\u80fd\u66f4\u7cbe\u51c6\u5730\u638c\u63e1\u548c\u5f3a\u8c03\u4e24\u4e2a\u6a21\u6001\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5c24\u5176\u5728 `probability_weights` \u63d0\u4f9b\u6709\u610f\u4e49\u7684\u8c03\u6574\u65f6\u3002\n  - \u53ef\u4ee5\u5728\u67d0\u79cd\u7a0b\u5ea6\u4e0a\u589e\u5f3a\u6a21\u578b\u5bf9\u4e8e\u4e0d\u540c\u8f93\u5165\u4e2d\u663e\u8457\u4fe1\u606f\u6a21\u5f0f\u7684\u5206\u8fa8\u80fd\u529b\u3002\n\n- **\u8d1f\u9762\u5f71\u54cd**\uff1a\n  - \u82e5 `probability_weights` \u7684\u6743\u91cd\u5206\u5e03\u4e0d\u5747\u6216\u8005\u5b66\u4e60\u4e0d\u7406\u60f3\uff0c\u53ef\u80fd\u4e22\u5931\u91cd\u8981\u7684\u539f\u59cb\u7279\u5f81\u4fe1\u606f\u3002\n  - \u9700\u8981\u6a21\u578b\u80fd\u591f\u66f4\u52a0\u51c6\u786e\u5730\u4ea7\u751f `probability_weights`\uff0c\u4ee5\u907f\u514d\u5bf9\u7279\u5f81\u7684\u4e0d\u5f53\u6291\u5236\u3002\n\n### \u8c03\u6574\u5efa\u8bae\n\n\u5728\u505a\u51fa\u8fd9\u6837\u7684\u8f6c\u6362\u540e\uff0c\u53ef\u4ee5\u91c7\u53d6\u4ee5\u4e0b\u63aa\u65bd\u4ee5\u786e\u4fdd\u4fee\u6539\u5e26\u6765\u9884\u60f3\u4e2d\u7684\u6548\u679c\uff1a\n\n- **\u6743\u91cd\u521d\u59cb\u5316\u548c\u8bad\u7ec3\u76d1\u63a7**\uff1a\n  - \u6ce8\u91cd `probability_weights` \u7684\u751f\u6210\u548c\u5e94\u7528\uff0c\u9700\u8981\u786e\u4fdd\u5176\u53cd\u6620\u8f93\u5165\u7279\u5f81\u95f4\u7684\u79d1\u5b66\u6bd4\u91cd\u3002\n  - \u5b9a\u4e49\u9002\u5f53\u7684\u635f\u5931\u51fd\u6570\u6216\u8f85\u52a9\u4efb\u52a1\u4ee5\u786e\u4fdd\u52a0\u6743\u540e\u7684\u7279\u5f81\u66f4\u52a0\u6709\u610f\u4e49\u3002\n\n- **\u5b9e\u9a8c\u9a8c\u8bc1**\uff1a\n  - \u901a\u8fc7\u5b9e\u9a8c\u5bf9\u6bd4 `combined_feature = weighted_m1` \u548c `combined_feature = weighted_m1 + m1` \u7684\u6548\u679c\uff0c\u5b9e\u9645\u6d4b\u8bd5\u5728\u7279\u5f81\u878d\u5408\u4e2d\u7684\u4f18\u7f3a\u70b9\u3002\n\n- **\u52a8\u6001\u6743\u91cd\u5b66\u4e60**\uff1a\n  - \u8003\u8651\u52a0\u5f3a `probability_weights` \u7684\u5b66\u4e60\u80fd\u529b\uff0c\u901a\u8fc7\u5f3a\u5316\u5b66\u4e60\u3001\u5bf9\u6bd4\u5b66\u4e60\u7b49\u673a\u5236\u63d0\u9ad8\u7279\u5f81\u52a0\u6743\u7b56\u7565\u7684\u5408\u7406\u6027\u3002\n\n\u6839\u636e\u6a21\u578b\u5728\u771f\u5b9e\u6570\u636e\u4e0a\u7684\u8868\u73b0\u8fdb\u4e00\u6b65\u51b3\u5b9a\u8be5\u8c03\u6574\u662f\u5426\u5408\u9002\u3002\u8fd9\u6837\u7684\u8bbe\u8ba1\u8c03\u6574\u4e3b\u8981\u662f\u4e3a\u4e86\u786e\u4fdd\u5728\u7279\u5f81\u878d\u5408\u65f6\uff0c\u6a21\u578b\u80fd\u66f4\u52a0\u6709\u6548\u5730\u5229\u7528\u6a21\u6001\u7279\u5f81\u7684\u4e92\u8865\u6027\u548c\u5dee\u5f02\u6027\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1316,"n_turns_in_conversation":28,"n_words":103,"source_row_offset":30733,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"6a4e458bbd26f5e35d5ad7fa","turn_index":10,"user_text":"\u589e\u52a0\u4e86\u539f\u59cb\u8def\u5f84\u540e\uff0c\u90a3\u4e48combined_feature = weighted_m1 + m1\u662f\u5426\u6539\u6210combined_feature = weighted_m1\u66f4\u5408\u9002\uff1f"},{"assistant_initiates":false,"asst_text":"\u5c06 `combined_feature = weighted_m1 + m1` \u6539\u4e3a `combined_feature = weighted_m1` \u7684\u4fee\u6539\u4e3b\u8981\u96c6\u4e2d\u5728\u5f3a\u8c03\u901a\u8fc7 `probability_weights` \u9009\u62e9\u7684\u7279\u5f81\uff0c\u4ece\u800c\u5bf9\u7279\u5f81\u878d\u5408\u7684\u91cd\u8981\u6027\u548c\u7279\u5f81\u9009\u62e9\u8fdb\u884c\u4e86\u8c03\u6574\u3002\u8fd9\u4e00\u6539\u53d8\u7684\u6548\u679c\u548c\u7406\u7531\u7efc\u5408\u5982\u4e0b\uff1a\n\n### \u6539\u52a8\u540e\u7684\u5206\u6790\n\n1. **\u5f3a\u5316\u7279\u5f81\u9009\u62e9**\uff1a\n   - \u901a\u8fc7\u4ec5\u4f7f\u7528 `weighted_m1`\uff0c\u4f60\u5728 `combined_feature` \u4e2d\u4ec5\u4fdd\u7559\u4e86\u7ecf\u8fc7\u6743\u91cd\u8c03\u6574\u540e\u7684\u7279\u5f81\u3002\u8fd9\u79cd\u5904\u7406\u5f3a\u8c03\u4e86\u7531 `probability_weights` \u786e\u5b9a\u7684\u91cd\u8981\u7279\u5f81\uff0c\u7a81\u51fa\u4e86\u81ea\u9002\u5e94\u52a0\u6743\u5728\u7279\u5f81\u9009\u62e9\u4e2d\u7684\u4f5c\u7528\u3002\n\n2. **\u7b80\u5316\u7279\u5f81\u5904\u7406\u6d41\u7a0b**\uff1a\n   - \u53bb\u9664\u4e0e\u539f\u59cb `m1` \u7684\u76f4\u63a5\u76f8\u52a0\uff0c\u6709\u52a9\u4e8e\u51cf\u5c11\u5197\u4f59\u4fe1\u606f\u7684\u53c2\u4e0e\uff0c\u4f7f\u5f97\u7279\u5f81\u4ea4\u4e92\u66f4\u52a0\u900f\u660e\u548c\u7b80\u5316\u3002\u8fd9\u6837\u53ef\u4ee5\u8ba9\u6a21\u578b\u66f4\u76f4\u63a5\u5730\u4f9d\u8d56\u4e8e\u5b66\u4e60\u5230\u7684\u6743\u91cd\u4ee5\u786e\u5b9a\u54ea\u4e9b\u7279\u5f81\u66f4\u91cd\u8981\u3002\n\n3. **\u589e\u52a0\u5dee\u5f02\u663e\u8457\u6027**\uff1a\n   - \u5f3a\u5316\u56e0\u8f93\u5165\u6a21\u6001\u5dee\u5f02\u800c\u52a0\u6743\u7684\u7279\u5f81\uff0c\u5e2e\u52a9\u6a21\u578b\u66f4\u6e05\u695a\u5730\u4f53\u73b0\u6a21\u6001\u4e4b\u95f4\u7684\u533a\u522b\u3002\u5bf9\u7279\u5f81\u52a0\u6743\u7b56\u7565\u66f4\u52a0\u654f\u611f\uff0c\u4ece\u800c\u53ef\u80fd\u66f4\u597d\u5730\u533a\u5206\u548c\u5229\u7528\u4e0d\u540c\u6a21\u6001\u7684\u4fe1\u606f\u3002\n\n### \u6f5c\u5728\u5f71\u54cd\n\n- **\u4f18\u52bf**\uff1a\n  - \u66f4\u6e05\u6670\u5730\u5212\u5206\u7279\u5f81\u6743\u91cd\uff0c\u7a81\u51fa\u6709\u4ef7\u503c\u7684\u4fe1\u606f\uff0c\u6f5c\u5728\u63d0\u9ad8\u53cc\u6a21\u6001\u878d\u5408\u7684\u7075\u6d3b\u6027\u548c\u6548\u7387\u3002\n  - \u5bf9\u6743\u91cd\u5b66\u4e60\u7684\u8d28\u91cf\u66f4\u654f\u611f\uff0c\u53ef\u80fd\u63d0\u9ad8\u6a21\u578b\u5bf9\u4e8e\u826f\u597d\u6743\u91cd\u7b56\u7565\u7684\u4f9d\u8d56\u6027\uff0c\u5982\u679c\u6743\u91cd\u80fd\u591f\u51c6\u786e\u53cd\u6620\u7279\u5f81\u7684\u91cd\u8981\u6027\uff0c\u8fd9\u79cd\u7b56\u7565\u4f1a\u66f4\u6709\u6548\u3002\n\n- **\u6311\u6218**\uff1a\n  - \u5982\u679c `probability_weights` \u6ca1\u6709\u88ab\u8bad\u7ec3\u597d\u6216\u8005\u5728\u7279\u5b9a\u7279\u5f81\u96c6\u4e0a\u8868\u73b0\u4e0d\u4f73\uff0c\u53ef\u80fd\u5bfc\u81f4\u4fe1\u606f\uff08\u5c24\u5176\u662f\u539f\u59cb\u7279\u5f81\u4fe1\u606f\uff09\u7684\u4e22\u5931\u3002\n  - \u53ef\u80fd\u9700\u8981\u66f4\u590d\u6742\u7684\u7b56\u7565\u6765\u521d\u59cb\u5316\u548c\u8bad\u7ec3\u5f97\u5230\u6709\u6548\u7684 `probability_weights`\u3002\n\n### \u8c03\u6574\u4e0e\u4f18\u5316\u5efa\u8bae\n\n1. **\u6743\u91cd\u751f\u6210\u7b56\u7565**\uff1a\u786e\u4fdd `probability_weights` \u7684\u6b63\u786e\u5b66\u4e60\u3002\u53ef\u4ee5\u901a\u8fc7\u589e\u52a0\u6b63\u5219\u5316\u3001\u4f7f\u7528\u8f85\u52a9\u635f\u5931\u7b49\u65b9\u6cd5\u6765\u5e2e\u52a9\u6a21\u578b\u5b66\u4e60\u66f4\u51c6\u786e\u7684\u6743\u91cd\u3002\n\n2. **\u5b9e\u9a8c\u5bf9\u6bd4\u4e0e\u9a8c\u8bc1**\uff1a\u901a\u8fc7\u76f4\u63a5\u5b9e\u9a8c\u5bf9\u6bd4\u4e0d\u540c\u8bbe\u8ba1\u65b9\u6848\u7684\u6027\u80fd\u5dee\u5f02\uff0c\u5728\u4e0d\u540c\u6570\u636e\u96c6\u548c\u4efb\u52a1\u4e0a\u9a8c\u8bc1\u8bbe\u8ba1\u7684\u901a\u7528\u6027\u3002\n\n3. **\u52a8\u6001\u8c03\u6574**\uff1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u52a8\u6001\u89c2\u5bdf `probability_weights` \u7684\u5206\u5e03\u4e0e\u53d8\u5316\uff0c\u8ba9\u6a21\u578b\u5728\u8bad\u7ec3\u7684\u540c\u65f6\u80fd\u591f\u9002\u5e94\u4e0d\u540c\u6a21\u6001\u7279\u5f81\u7684\u52a8\u6001\u53d8\u5316\u3002\n\n\u603b\u4e4b\uff0c\u8fd9\u79cd\u8c03\u6574\u66f4\u51c6\u786e\u5730\u805a\u7126\u4e8e\u7279\u5f81\u9009\u62e9\u4e0e\u6743\u91cd\u7684\u5e94\u7528\uff0c\u5e0c\u671b\u901a\u8fc7\u8c03\u6574\u80fd\u591f\u5728\u6293\u4f4f\u4e0d\u540c\u6a21\u6001\u7279\u5f81\u533a\u522b\u7684\u540c\u65f6\uff0c\u5b9e\u73b0\u66f4\u597d\u7684\u878d\u5408\u7ed3\u679c\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1854,"n_turns_in_conversation":28,"n_words":134,"source_row_offset":30734,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"e13ef96b0484e8009fe4e0b3","turn_index":11,"user_text":"\u6211\u7684\u610f\u601d\u662f\u6539\u6210\uff1aclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.norm2 = LayerNorm(dim, 'with_bias')\n        self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)\n        self.tanh = nn.Tanh()\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.tanh(diff_feature)\n        weighted_m1 = m1 * probability_weights\n        combined_feature = weighted_m1\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        global_f = global_f1 + global_f2\n        return global_f"},{"assistant_initiates":false,"asst_text":"\u5728\u76ee\u524d\u7684 `CrossMamba` \u8bbe\u8ba1\u4e2d\uff0c\u5df2\u7ecf\u5b9e\u73b0\u4e86\u6bd4\u8f83\u6709\u9488\u5bf9\u6027\u7684\u7279\u5f81\u52a0\u6743\u548c\u878d\u5408\u7b56\u7565\u3002\u4f46\u4ecd\u7136\u6709\u4e00\u4e9b\u90e8\u5206\u53ef\u4ee5\u8fdb\u4e00\u6b65\u4f18\u5316\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u9c81\u68d2\u6027\u3001\u53ef\u89e3\u91ca\u6027\u53ca\u6027\u80fd\u8868\u73b0\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5177\u4f53\u5efa\u8bae\uff1a\n\n### 1. **\u6743\u91cd\u751f\u6210\u4e0e\u521d\u59cb\u5316**\n\n#### \u4f18\u5316\u5efa\u8bae\uff1a\n- **\u6743\u91cd\u521d\u59cb\u5316**\uff1a\u8003\u8651\u4f7f\u7528\u66f4\u590d\u6742\u7684\u521d\u59cb\u5316\u7b56\u7565\uff0c\u5982\u57fa\u4e8e\u7edf\u8ba1\u5206\u5e03\uff08Xavier \u6216 He \u521d\u59cb\u5316\uff09\uff0c\u4ee5\u4fbf\u8ba9\u521d\u59cb\u7684 `probability_weights` \u66f4\u6709\u610f\u4e49\uff0c\u907f\u514d\u8fc7\u5927\u6216\u8fc7\u5c0f\u7684\u6743\u91cd\u503c\u3002\n- **\u81ea\u9002\u5e94\u6b63\u5219\u5316**\uff1a\u5f15\u5165 L2 \u6b63\u5219\u5316\u6216\u8005\u5176\u4ed6\u624b\u6bb5\u9650\u5236 `probability_weights` \u7684\u6743\u91cd\u8303\u56f4\uff0c\u786e\u4fdd\u6a21\u578b\u4e0d\u4f9d\u8d56\u5355\u4e00\u7279\u5f81\u3002\n\n### 2. **\u5dee\u5f02\u8ba1\u7b97\u7684\u6539\u8fdb**\n\n#### \u4f18\u5316\u5efa\u8bae\uff1a\n- **\u591a\u5c3a\u5ea6\u7279\u5f81\u5dee\u5f02**\uff1a\u76f4\u63a5\u4f7f\u7528 `m1 - m2` \u7684\u5f52\u4e00\u5316\u53ef\u80fd\u4e0d\u80fd\u6355\u6349\u5230\u590d\u6742\u7684\u7279\u5f81\u5173\u7cfb\u3002\u53ef\u4ee5\u8003\u8651\u5f15\u5165\u4e0d\u540c\u5c3a\u5ea6\u7684\u5dee\u5f02\u7279\u5f81\uff0c\u4f8b\u5982\uff1a\n  - \u4f7f\u7528\u989d\u5916\u7684\u5377\u79ef\u64cd\u4f5c\uff08`dwconv`\uff09\u751f\u6210\u4e0d\u540c\u5c42\u7ea7/\u5c3a\u5ea6\u7684\u7279\u5f81\u8868\u793a\u518d\u8ba1\u7b97\u5dee\u5f02\u3002\n  - \u8003\u8651\u7528\u5176\u4ed6\u5ea6\u91cf\u65b9\u6cd5\uff0c\u8bf8\u5982\u4f59\u5f26\u76f8\u4f3c\u5ea6\u6216\u76f4\u63a5\u4f7f\u7528\u6ce8\u610f\u529b\u673a\u5236\u4ee3\u66ff\u7b80\u5355\u7684\u7279\u5f81\u5dee\u3002\n\n### 3. **\u591a\u8def\u5f84\u878d\u5408\u673a\u5236**\n\n#### \u4f18\u5316\u5efa\u8bae\uff1a\n- **\u663e\u5f0f\u7279\u5f81\u9009\u62e9**\uff1a\u5229\u7528 mask \u6216 attention \u6743\u91cd\u6765\u52a8\u6001\u9009\u62e9\u548c\u52a0\u6743\u878d\u5408\u7279\u5f81\uff0c\u907f\u514d\u6240\u6709\u7279\u5f81\u90fd\u88ab\u76f2\u76ee\u7d2f\u52a0\u3002\n- **\u8def\u5f84\u4e2d dropout**\uff1a\u5f15\u5165 dropout \u7b80\u5355\u800c\u6709\u6548\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u5c24\u5176\u5728 `global_f1` \u548c `global_f2` \u7684\u52a0\u6cd5\u4e2d\u751f\u6548\u3002\n\n### 4. **\u5b9e\u65bd\u9ad8\u7ea7\u7279\u5f81\u5904\u7406**\n\n#### \u4f18\u5316\u5efa\u8bae\uff1a\n- **\u5377\u79ef\u4e0e\u975e\u7ebf\u6027\u8f6c\u6362\u7684\u589e\u5f3a**\uff1a\u8003\u8651\u4f7f\u7528\u66f4\u590d\u6742\u7684\u5377\u79ef\u673a\u5236\uff08\u5982\u5e26\u6709\u975e\u7ebf\u6027\u7684\u6df1\u5ea6\u5377\u79ef\uff09\u6765\u66ff\u4ee3\u7b80\u5355\u7684 `dwconv`\uff0c\u7ed3\u5408 `ReLU` \u6216 `GELU` \u7b49\u975e\u7ebf\u6027\u51fd\u6570\uff0c\u4ee5\u63d0\u9ad8\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u3002\n- **\u52a8\u6001\u878d\u5408\u6743\u91cd**\uff1a\u5c1d\u8bd5\u5c06 `global_f1` \u548c `global_f2` \u7684\u878d\u5408\u6539\u4e3a\u52a0\u6743\u5f0f\uff0c\u53ef\u4ee5\u5f15\u5165\u52a8\u6001\u6743\u91cd\u6765\u786e\u5b9a `global_f1` \u548c `global_f2` \u76f8\u5bf9\u7684\u91cd\u8981\u6027\u3002\n\n### 5. **\u6027\u80fd\u76d1\u63a7\u4e0e\u8c03\u8bd5**\n\n#### \u4f18\u5316\u5efa\u8bae\uff1a\n- **\u957f\u671f\u6027\u80fd\u76d1\u63a7**\uff1a\u8bbe\u7f6e\u76d1\u63a7\u6307\u6807\uff08\u5982\u9a8c\u8bc1\u635f\u5931\uff09\u4ee5\u52a8\u6001\u8c03\u8282\u5b66\u4e60\u7387\u6216\u8005\u5c42\u7ea7\u7b56\u7565\u3002\n- **\u8d85\u53c2\u6570\u9a8c\u8bc1**\uff1a\u4ece\u4e00\u4e2a\u5c0f\u7684\u5f00\u53d1\u96c6\u4e0a\u8fdb\u884c\u8d85\u53c2\u6570\u9a8c\u8bc1\uff08\u5982\u5377\u79ef\u6838\u5927\u5c0f\u3001\u5b66\u4e60\u7387\u3001\u6b63\u5219\u5316\u5f3a\u5ea6\uff09\uff0c\u786e\u4fdd\u4e0d\u540c\u7ec4\u4ef6\u80fd\u591f\u5f62\u6210\u534f\u540c\u6548\u5e94\u800c\u975e\u51b2\u7a81\u3002\n\n\u901a\u8fc7\u4e0a\u8ff0\u4f18\u5316\u65b9\u6848\uff0c\u4f60\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u9ad8 `CrossMamba` \u5728\u591a\u6a21\u6001\u878d\u5408\u4efb\u52a1\u4e2d\u7684\u80fd\u529b\u548c\u8868\u73b0\uff0c\u540c\u65f6\u4e5f\u4f7f\u8bf8\u5982\u7279\u5f81\u9009\u62e9\u3001\u878d\u5408\u8def\u5f84\u7b49\u673a\u5236\u66f4\u52a0\u7a33\u5065\u548c\u7075\u6d3b\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1924,"n_turns_in_conversation":28,"n_words":160,"source_row_offset":30735,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"f4595b552fef305f00ead29b","turn_index":12,"user_text":"\u7efc\u4e0a\u6240\u6709\uff0c\u7ed9\u51faclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.norm2 = LayerNorm(dim, 'with_bias')\n        self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)\n        self.tanh = nn.Tanh()\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.tanh(diff_feature)\n        weighted_m1 = m1 * probability_weights\n        combined_feature = weighted_m1\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        global_f = global_f1 + global_f2\n        return global_f\u4e2d\u8fd8\u9700\u4f18\u5316\u7684\u90e8\u5206\u3002"},{"assistant_initiates":false,"asst_text":"\u4ee5\u4e0b\u662f\u9488\u5bf9 `CrossMamba` \u7c7b\u4e2d\u6240\u63d0\u5230\u7684\u5404\u4e2a\u4f18\u5316\u5efa\u8bae\u7684\u4ee3\u7801\u5b9e\u73b0\u3002\u8fd9\u4e9b\u5efa\u8bae\u5305\u62ec\u6743\u91cd\u751f\u6210\u4e0e\u521d\u59cb\u5316\u3001\u591a\u5c3a\u5ea6\u7279\u5f81\u5dee\u5f02\u3001\u591a\u8def\u5f84\u878d\u5408\u673a\u5236\u3001\u9ad8\u7ea7\u7279\u5f81\u5904\u7406\u4ee5\u53ca\u6027\u80fd\u76d1\u63a7\u4e0e\u8c03\u8bd5\u3002\n\n### 1. \u6743\u91cd\u751f\u6210\u4e0e\u521d\u59cb\u5316\n\n**\u4f7f\u7528 Xavier \u6216 He \u521d\u59cb\u5316**\uff1a\n```python\nimport torch.nn.init as init\n\nclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.norm2 = LayerNorm(dim, 'with_bias')\n        self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)\n        self.tanh = nn.Tanh()\n\n        # \u521d\u59cb\u5316\u7ebf\u6027\u6743\u91cd\uff08\u4f8b\u5982 probability_weights \u7684\u751f\u6210\uff09\n        self.probability_weights_layer = nn.Linear(dim, dim)\n        init.xavier_uniform_(self.probability_weights_layer.weight)\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.tanh(self.probability_weights_layer(diff_feature))\n        weighted_m1 = m1 * probability_weights\n        combined_feature = weighted_m1\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        global_f = global_f1 + global_f2\n        return global_f\n```\n\n### 2. \u591a\u5c3a\u5ea6\u7279\u5f81\u5dee\u5f02\n\n**\u5f15\u5165\u591a\u5c3a\u5ea6\u5377\u79ef\u6765\u6355\u6349\u590d\u6742\u7279\u5f81\u5173\u7cfb**\uff1a\n```python\nclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        \n        # \u589e\u52a0\u591a\u5c3a\u5ea6\u5377\u79ef\n        self.multi_scale_conv = nn.Conv2d(dim, dim, kernel_size=(3, 5), padding=(1, 2), groups=dim)\n        self.tanh = nn.Tanh()\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        \n        # \u4f7f\u7528\u591a\u5c3a\u5ea6\u5377\u79ef\u8ba1\u7b97\u7279\u5f81\u5dee\u5f02\n        m1_diff = self.multi_scale_conv(m1)\n        m2_diff = self.multi_scale_conv(m2)\n        diff_feature = self.norm1(m1_diff - m2_diff)\n        \n        probability_weights = self.tanh(diff_feature)\n        weighted_m1 = m1 * probability_weights\n        combined_feature = weighted_m1\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        global_f = global_f1 + global_f2\n        return global_f\n```\n\n### 3. \u591a\u8def\u5f84\u878d\u5408\u673a\u5236\n\n**\u5f15\u5165\u663e\u5f0f\u7279\u5f81\u9009\u62e9\u4e0e dropout**\uff1a\n```python\nclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.dropout = nn.Dropout(p=0.1)  # \u5f15\u5165dropout\n        self.tanh = nn.Tanh()\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.tanh(diff_feature)\n        weighted_m1 = m1 * probability_weights\n        combined_feature = self.dropout(weighted_m1)  # \u4f7f\u7528 dropout\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        global_f = global_f1 + global_f2\n        return global_f\n```\n\n### 4. \u9ad8\u7ea7\u7279\u5f81\u5904\u7406\n\n**\u7ed3\u5408\u9ad8\u7ea7\u975e\u7ebf\u6027\u548c\u52a8\u6001\u6743\u91cd\u878d\u5408**\uff1a\n```python\nclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.dwconv = nn.Conv2d(dim, dim, kernel_size=3, padding=1, groups=dim)\n        self.gelu = nn.GELU()  # \u4f7f\u7528GELU\u975e\u7ebf\u6027\n        self.global_weight = nn.Parameter(torch.tensor(0.5))  # \u521d\u59cb\u5316\u52a8\u6001\u878d\u5408\u6743\u91cd\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.gelu(diff_feature)  # \u4f7f\u7528GELU\u66ff\u4ee3Tanh\n        weighted_m1 = m1 * probability_weights\n        combined_feature = weighted_m1\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        \n        # \u52a8\u6001\u878d\u5408\u6743\u91cd\n        global_f = self.global_weight * global_f1 + (1 - self.global_weight) * global_f2\n        return global_f\n```\n\n### 5. \u6027\u80fd\u76d1\u63a7\u4e0e\u8c03\u8bd5\n\n**\u96c6\u6210\u65e9\u505c\u548c\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\uff08\u4f2a\u4ee3\u7801\u793a\u610f\uff09**\uff1a\n```python\n# \u5728\u4e3b\u8bad\u7ec3\u5faa\u73af\u5916\u8bbe\u7f6e\nimport torch.optim as optim\nscheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=5, factor=0.5)\n\n# \u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\nfor epoch in range(num_epochs):\n    train_loss = train(...)\n    val_loss = validate(...)\n    \n    scheduler.step(val_loss)  # \u6839\u636e\u9a8c\u8bc1\u635f\u5931\u52a8\u6001\u8c03\u6574\u5b66\u4e60\u7387\n    \n    if early_stopping(val_loss):\n        break  # \u5b9e\u73b0\u65e9\u505c\u673a\u5236\n```\n\n\u5728\u5b9e\u9645\u5b9e\u73b0\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u4f60\u7684\u5177\u4f53\u4efb\u52a1\u548c\u6a21\u578b\u7ed3\u6784\u8fdb\u4e00\u6b65\u7ec6\u5316\u8fd9\u4e9b\u4ee3\u7801\u4f18\u5316\u5efa\u8bae\u3002\u901a\u8fc7\u8fd9\u4e9b\u4f18\u5316\uff0c\u671f\u671b\u80fd\u591f\u66f4\u597d\u5730\u589e\u5f3a\u591a\u6a21\u6001\u7279\u5f81\u7684\u878d\u5408\u4e0e\u5904\u7406\u80fd\u529b\uff0c\u5e76\u63d0\u9ad8\u6574\u4f53\u6a21\u578b\u7684\u9c81\u68d2\u6027\u548c\u8868\u73b0\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":4485,"n_turns_in_conversation":28,"n_words":383,"source_row_offset":30736,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"cd1c947d2f6bcb1dea4b305e","turn_index":13,"user_text":"\u5206\u522b\u7ed9\u51fa\u5efa\u8bae\u7684\u4ee3\u7801"},{"assistant_initiates":false,"asst_text":"\u5728\u8fd9\u4e2a\u7248\u672c\u7684 `CrossMamba` \u7c7b\u4e2d\uff0c\u5df2\u7ecf\u96c6\u6210\u4e86\u591a\u9879\u4f18\u5316\u63aa\u65bd\uff0c\u5305\u62ec\u6743\u91cd\u521d\u59cb\u5316\u3001dropout \u589e\u5f3a\u6cdb\u5316\u80fd\u529b\u4ee5\u53ca\u53cc\u8def\u5f84\u878d\u5408\u7b56\u7565\u3002\u8fd9\u662f\u4e00\u79cd\u5e73\u8861\u7279\u5f81\u9009\u62e9\u4e0e\u878d\u5408\u5197\u4f59\u4fdd\u62a4\u7684\u8bbe\u8ba1\u3002\n\n### \u4ee3\u7801\u5206\u6790\u4e0e\u4f18\u5316\u70b9\n\n1. **\u6743\u91cd\u521d\u59cb\u5316**\uff1a\n   - \u4f7f\u7528 Xavier \u521d\u59cb\u5316 `probability_weights_layer`\uff0c\u786e\u4fdd\u5728\u5f00\u59cb\u65f6\u6709\u4e00\u4e2a\u597d\u7684\u7279\u5f81\u9009\u62e9\u6743\u91cd\u5206\u5e03\u3002\n   \n2. **\u5dee\u5f02\u5f52\u4e00\u5316\u4e0e\u6743\u91cd\u751f\u6210**\uff1a\n   - \u901a\u8fc7 `norm1` \u5bf9 `m1` \u548c `m2` \u7684\u5dee\u5f02\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\uff0c\u4f7f\u540e\u7eed `probability_weights` \u8ba1\u7b97\u66f4\u7a33\u5b9a\u3002\n   - `probability_weights` \u901a\u8fc7\u7ebf\u6027\u5c42\u548c `Tanh` \u51fd\u6570\u8fdb\u884c\u8ba1\u7b97\uff0c\u5e73\u6ed1\u5316\u7279\u5f81\u6bd4\u91cd\u3002\n\n3. **\u7279\u5f81\u52a0\u6743\u4e0e\u878d\u5408**\uff1a\n   - `weighted_m1` \u8d4b\u4e88 `m1` \u7279\u5f81\u4ee5 `probability_weights` \u6743\u91cd\uff0c\u5728 `dropout` \u540e\u8fdb\u4e00\u6b65\u5e73\u6ed1\uff0c\u9632\u6b62\u8fc7\u62df\u5408\u3002\n   - `cross_mamba` \u5206\u522b\u5e94\u7528\u4e8e\u52a0\u6743\u7279\u5f81\u4e0e\u539f\u7279\u5f81\uff0c\u786e\u4fdd\u5728\u6743\u91cd\u8ba1\u7b97\u4e0d\u7406\u60f3\u60c5\u51b5\u4e0b\u8fd8\u80fd\u4fdd\u6301\u57fa\u7840\u4fe1\u606f\u3002\n\n4. **\u53cc\u8def\u5f84\u5168\u7403\u7279\u5f81\u878d\u5408**\uff1a\n   - \u901a\u8fc7 `global_f1` \u548c `global_f2` \u7684\u7b80\u5355\u76f8\u52a0\u5b9e\u73b0\u591a\u8def\u5f84\u878d\u5408\uff0c\u4fdd\u62a4\u91cd\u8981\u7279\u5f81\u7684\u540c\u65f6\uff0c\u5de9\u56fa\u57fa\u7840\u7279\u5f81\u7ed3\u6784\u3002\n\n### \u8fdb\u4e00\u6b65\u4f18\u5316\u5efa\u8bae\n\n- **\u8c03\u8282 `dropout` \u5f3a\u5ea6**\uff1a\n  - \u76ee\u524d `p=0.1` \u662f\u4e00\u4e2a\u8f83\u4f4e\u503c\uff0c\u53ef\u4ee5\u6839\u636e\u4efb\u52a1\u9700\u8981\u8c03\u6574\u8be5\u53c2\u6570\u4ee5\u6d4b\u8bd5\u4e0d\u540c\u7684\u6cdb\u5316\u6548\u679c\u3002\n\n- **\u52a8\u6001\u6743\u91cd\u8c03\u6574**\uff1a\n  - \u53ef\u4ee5\u5728\u540e\u7eed\u8fdb\u4e00\u6b65\u6539\u8fdb\u4e2d\u5f15\u5165\u5b66\u4e60\u7684\u52a8\u6001\u878d\u5408\u6743\u91cd\uff0c\u4ee5\u4fbf\u5728\u5408\u9002\u7684\u60c5\u51b5\u4e0b\u6539\u53d8 `global_f1` \u548c `global_f2` \u7684\u5f71\u54cd\u529b\u3002\n\n- **\u975e\u7ebf\u6027\u6539\u8fdb**\uff1a\n  - \u7ee7\u7eed\u63a2\u7d22\u5176\u4ed6\u975e\u7ebf\u6027\u6fc0\u6d3b\u51fd\u6570\u53ef\u80fd\u7ed9 `probability_weights` \u5e26\u6765\u7684\u5f71\u54cd\uff0c\u5982 `ReLU`, `GELU` \u7b49\uff0c\u4ee5\u8c03\u8282\u7279\u5f81\u52a0\u6743\u66f2\u7ebf\u3002\n\n- **\u8be6\u7ec6\u76d1\u63a7\u4e0e\u8c03\u8bd5**\uff1a\n  - \u5728\u4f7f\u7528\u8fc7\u7a0b\u4e2d\u901a\u8fc7\u4e13\u95e8\u7684\u9a8c\u8bc1\u548c\u6d4b\u8bd5\u96c6\uff0c\u7ec6\u81f4\u76d1\u63a7 `global_f` \u5bf9\u8f93\u5165\u7279\u5f81\u53d8\u5316\u7684\u54cd\u5e94\uff0c\u4fdd\u969c\u6a21\u578b\u5728\u5e94\u5bf9\u5f02\u5e38\u8f93\u5165\u6709\u9002\u5f53\u7684\u9002\u5e94\u80fd\u529b\u3002\n\n\u4ee5\u4e0a\u4f18\u5316\u5e94\u6839\u636e\u5b9e\u9645\u6570\u636e\u96c6\u4e0e\u4efb\u52a1\u76ee\u6807\u5177\u4f53\u8c03\u6574\uff0c\u68c0\u9a8c\u5728\u771f\u5b9e\u573a\u666f\u4e2d\u7684\u8868\u73b0\u3002\u7ecf\u8fc7\u6301\u7eed\u7684\u6d4b\u8bd5\u4e0e\u9a8c\u8bc1\uff0c\u671f\u671b\u8be5\u878d\u5408\u7b56\u7565\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u6a21\u578b\u5728\u591a\u6a21\u6001\u4efb\u52a1\u4e2d\u7684\u8868\u73b0\u80fd\u529b\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1921,"n_turns_in_conversation":28,"n_words":155,"source_row_offset":30737,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"d4b0574141101f368e673f2e","turn_index":14,"user_text":"\u6211\u4eec\u8fd9\u6837\u4fee\u6539\uff1aclass CrossMamba(nn.Module):\n    def __init__(self, dim):\n        super(CrossMamba, self).__init__()\n        self.cross_mamba = Mamba(dim, bimamba_type=\"v3\")\n        self.norm1 = LayerNorm(dim, 'with_bias')\n        self.dropout = nn.Dropout(p=0.1)  # \u5f15\u5165dropout\n        self.tanh = nn.Tanh()\n        self.probability_weights_layer = nn.Linear(dim, dim)\n        init.xavier_uniform_(self.probability_weights_layer.weight)\n\n    def forward(self, m1, m1_resi, m2):\n        m1_resi = m1 + m1_resi\n        diff_feature = self.norm1(m1 - m2)\n        probability_weights = self.tanh(self.probability_weights_layer(diff_feature))\n        weighted_m1 = m1 * probability_weights\n        combined_feature = self.dropout(weighted_m1)\n        global_f1 = self.cross_mamba(combined_feature, extra_emb=m2)\n        global_f2 = self.cross_mamba(m1, extra_emb=m2)\n        global_f = global_f1 + global_f2\n        return global_f"},{"assistant_initiates":false,"asst_text":"\u5982\u679c\u4f60\u5728\u8bad\u7ec3\u4e2d\u89c2\u5bdf\u5230\u4e86\u8f7b\u5fae\u7684\u8fc7\u62df\u5408\u73b0\u8c61\uff0c\u8fd9\u901a\u5e38\u610f\u5473\u7740\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u8868\u73b0\u4f18\u5f02\uff0c\u4f46\u5728\u9a8c\u8bc1\u96c6\u6216\u6d4b\u8bd5\u96c6\u4e0a\u8868\u73b0\u8f83\u5dee\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u53ef\u4ee5\u8003\u8651\u4ee5\u4e0b\u51e0\u79cd\u7b56\u7565\uff1a\n\n### 1. **\u589e\u5f3a\u6570\u636e**\n\n- **\u6570\u636e\u589e\u5f3a**\uff1a\u589e\u52a0\u6570\u636e\u7684\u591a\u6837\u6027\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u6cdb\u5316\u3002\u5bf9\u56fe\u50cf\u6765\u8bf4\uff0c\u8fd9\u53ef\u4ee5\u5305\u62ec\u7ffb\u8f6c\u3001\u65cb\u8f6c\u3001\u7f29\u653e\u3001\u5e73\u79fb\u3001\u989c\u8272\u6270\u52a8\u7b49\u64cd\u4f5c\u3002\n- **\u5408\u6210\u6570\u636e**\uff1a\u5982\u679c\u53ef\u80fd\uff0c\u751f\u6210\u4e00\u4e9b\u5408\u6210\u6570\u636e\u4ee5\u4fbf\u589e\u52a0\u8bad\u7ec3\u6570\u636e\u7684\u591a\u6837\u6027\u3002\n\n### 2. **\u8c03\u6574\u6a21\u578b\u590d\u6742\u5ea6**\n\n- **\u51cf\u5c0f\u6a21\u578b\u590d\u6742\u5ea6**\uff1a\u5982\u679c\u6a21\u578b\u8fc7\u4e8e\u590d\u6742\uff0c\u53ef\u4ee5\u8003\u8651\u964d\u4f4e\u5c42\u6570\u6216\u6bcf\u5c42\u7684\u795e\u7ecf\u5143\u6570\u91cf\u3002\n- **\u53c2\u6570\u526a\u679d**\uff1a\u53bb\u9664\u4e0d\u91cd\u8981\u7684\u53c2\u6570\u6216\u8005\u795e\u7ecf\u5143\uff0c\u4ee5\u7b80\u5316\u6a21\u578b\u7ed3\u6784\u3002\n\n### 3. **\u6b63\u5219\u5316\u6280\u672f**\n\n- **L1/L2 \u6b63\u5219\u5316**\uff1a\u5728\u635f\u5931\u51fd\u6570\u4e2d\u6dfb\u52a0\u6b63\u5219\u5316\u9879\uff0c\u9650\u5236\u6a21\u578b\u6743\u91cd\u7684\u5927\u5c0f\u3002\n  \n  ```python\n  # PyTorch \u4f8b\u5b50\n  optimizer = torch.optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)  # L2 \u6b63\u5219\u5316\n  ```\n\n- **Batch Normalization**\uff1a\u5728\u7f51\u7edc\u4e2d\u95f4\u5c42\u4f7f\u7528\u6279\u91cf\u5f52\u4e00\u5316\u6765\u7a33\u5b9a\u8bad\u7ec3\u8fc7\u7a0b\uff0c\u51cf\u5c11\u5bf9\u521d\u59cb\u6743\u91cd\u548c\u5b66\u4e60\u7387\u7684\u654f\u611f\u6027\u3002\n\n- **Dropout**\uff1a\u589e\u52a0 dropout \u7684\u5e94\u7528\u8303\u56f4\u548c\u5f3a\u5ea6\uff0c\u8fdb\u4e00\u6b65\u9632\u6b62\u8fc7\u62df\u5408\u3002\n  \n  ```python\n  self.dropout = nn.Dropout(p=0.3)  # \u589e\u52a0 dropout \u6982\u7387\n  ```\n\n### 4. **\u8c03\u6574\u8bad\u7ec3\u7b56\u7565**\n\n- **\u65e9\u505c**\uff1a\u76d1\u63a7\u9a8c\u8bc1\u635f\u5931\uff0c\u5982\u679c\u53d1\u73b0\u6301\u7eed\u51e0\u4e2a epoch \u65e0\u663e\u8457\u6539\u5584\uff0c\u5219\u505c\u6b62\u8bad\u7ec3\u3002\n  \n  ```python\n  # \u4f2a\u4ee3\u7801\n  if val_loss > best_loss:\n      counter += 1\n      if counter > patience:\n          early_stop = True\n  else:\n      best_loss = val_loss\n      counter = 0\n  ```\n\n- **\u51cf\u5c11\u5b66\u4e60\u7387**\uff1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u9047\u5230\u74f6\u9888\u65f6\uff0c\u964d\u4f4e\u5b66\u4e60\u7387\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u4ece\u4e0d\u4f73\u7684\u5c40\u90e8\u6700\u4f18\u8d70\u51fa\u3002\n\n  ```python\n  # \u51cf\u5c11\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\n  scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=5, factor=0.5)\n  ```\n\n- **\u6539\u8fdb\u8bad\u7ec3\u6279\u5927\u5c0f**\uff1a\u5bf9\u4e8e\u67d0\u4e9b\u95ee\u9898\uff0c\u8c03\u6574\u6279\u5904\u7406\u5927\u5c0f\u53ef\u80fd\u4f1a\u6539\u5584\u6cdb\u5316\u80fd\u529b\u3002\n\n### 5. **\u4f7f\u7528\u66f4\u591a\u6570\u636e\u6216\u4ea4\u53c9\u9a8c\u8bc1**\n\n- **\u4ea4\u53c9\u9a8c\u8bc1**\uff1a\u4f7f\u7528 k \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u786e\u4fdd\u6a21\u578b\u6cdb\u5316\u5230\u4e0d\u540c\u7684\u6570\u636e\u5207\u5206\u3002\n\n\u901a\u8fc7\u7ec4\u5408\u8fd9\u4e9b\u6280\u672f\uff0c\u7279\u522b\u662f\u4f7f\u7528\u9002\u5408\u4f60\u6570\u636e\u7684\u6b63\u5219\u5316\u3001\u6570\u636e\u589e\u5f3a\u7b56\u7565\uff0c\u901a\u5e38\u80fd\u591f\u51cf\u8f7b\u6216\u89e3\u51b3\u8f7b\u5fae\u8fc7\u62df\u5408\u7684\u95ee\u9898\u3002\u53ef\u4ee5\u4ece\u7ec4\u5408\u5e94\u7528\u8fd9\u4e9b\u65b9\u6cd5\u4e2d\u627e\u5230\u6700\u4f73\u65b9\u6848\u4ee5\u63d0\u9ad8\u6cdb\u5316\u6027\u80fd\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1284,"n_turns_in_conversation":28,"n_words":105,"source_row_offset":30738,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"938cb1947b074717ffca1499","turn_index":15,"user_text":"\u56e0\u4e3a\u6211\u4eec\u5728\u8bad\u7ec3\u4e2d\u5176\u5b9e\u51fa\u73b0\u4e86\u8f7b\u5fae\u8fc7\u62df\u5408\u73b0\u8c61"},{"assistant_initiates":false,"asst_text":"\u5728\u4f18\u5316 `CrossMamba` \u4e4b\u540e\uff0c\u8fdb\u884c\u9002\u5f53\u7684\u6570\u636e\u589e\u5f3a\u8c03\u6574\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u6cdb\u5316\uff0c\u51cf\u5c11\u8fc7\u62df\u5408\u3002\u5728\u8fdb\u884c\u6570\u636e\u589e\u5f3a\u4fee\u6539\u65f6\uff0c\u76ee\u6807\u662f\u589e\u52a0\u6570\u636e\u7684\u591a\u6837\u6027\uff0c\u540c\u65f6\u8003\u8651\u4e0d\u5bfc\u81f4\u8f93\u5165\u6570\u636e\u7684\u8fc7\u5ea6\u6270\u52a8\u3002\u5982\u4e0b\u662f\u5bf9\u6570\u636e\u589e\u5f3a\u7684\u4e00\u4e9b\u5efa\u8bae\u548c\u53ef\u80fd\u7684\u914d\u7f6e\uff1a\n\n### \u6269\u5c55\u6570\u636e\u589e\u5f3a\u7b56\u7565\n\n1. **\u51e0\u4f55\u53d8\u6362**\uff1a\n   - **\u65cb\u8f6c**\uff1a\u589e\u52a0\u65cb\u8f6c\u89d2\u5ea6\u7684\u8303\u56f4\uff0c\u4ee5\u6355\u6349\u66f4\u591a\u7684\u59ff\u6001\u53d8\u5316\u3002\n     ```python\n     degrees = 10\n     ```\n   - **\u5e73\u79fb**\uff1a\u5f15\u5165\u66f4\u5927\u8303\u56f4\u7684\u5e73\u79fb\uff0c\u6a21\u62df\u76ee\u6807\u7684\u4f4d\u79fb\u3002\n     ```python\n     translate = (0.1, 0.1)\n     ```\n   - **\u7f29\u653e**\uff1a\u589e\u52a0\u7f29\u653e\u7684\u8303\u56f4\uff0c\u9002\u5e94\u5927\u5c0f\u53d8\u5316\u3002\n     ```python\n     scale = (0.9, 1.1)\n     ```\n\n2. **\u989c\u8272\u6270\u52a8**\uff1a\n   - \u5982\u56fe\u50cf\u6570\u636e\u4e2d\u5305\u542b\u989c\u8272\u4fe1\u606f\uff0c\u53ef\u4ee5\u589e\u52a0\u4eae\u5ea6\u3001\u5bf9\u6bd4\u5ea6\u3001\u9971\u548c\u5ea6\u7b49\u53c2\u6570\u7684\u8c03\u6574\u3002\n   - **\u989c\u8272\u6296\u52a8**\uff1a\u5bf9\u4e8e RGB \u56fe\u50cf\uff0c\u53ef\u4ee5\u9002\u5f53\u5f15\u5165\u989c\u8272\u53d8\u5316\u3002\n     ```python\n     color_jitter = tfm.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1)\n     ```\n\n3. **\u4eff\u5c04\u53d8\u6362**\uff1a\n   - **\u526a\u5207**\uff1a\u589e\u52a0\u526a\u5207\u8303\u56f4\u4ee5\u5904\u7406\u66f4\u590d\u6742\u7684\u5f62\u53d8\u3002\n     ```python\n     shear = 5\n     ```\n\n4. **\u629b\u5f00\u56fe\u50cf\u7684\u5176\u4ed6\u64cd\u4f5c\uff08\u5982\u566a\u58f0\u6ce8\u5165\uff09**\uff1a\n   - **\u968f\u673a\u566a\u97f3**\uff1a\u5728\u56fe\u50cf\u6570\u636e\u4e2d\u5f15\u5165\u968f\u673a\u566a\u58f0\uff0c\u4ee5\u63d0\u9ad8\u9c81\u68d2\u6027\u3002\n\n### \u7efc\u5408\u6570\u636e\u589e\u5f3a\u914d\u7f6e\n\n\u7efc\u5408\u8003\u8651\u4ee5\u4e0a\u5efa\u8bae\uff0c\u53ef\u4ee5\u5982\u4e0b\u66f4\u65b0\u4f60\u7684\u6570\u636e\u589e\u5f3a\u914d\u7f6e\uff1a\n\n```python\nimport torchvision.transforms as tfm\n\n# \u7efc\u5408\u6570\u636e\u589e\u5f3a\u914d\u7f6e\ntransform = tfm.Compose([\n    tfm.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=5),\n    tfm.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n    tfm.RandomHorizontalFlip(),  # \u5f15\u5165\u6c34\u5e73\u7ffb\u8f6c\n])\n\n# \u5e94\u7528\u6570\u636e\u589e\u5f3a\naugmented_data = transform(original_data)\n```\n\n### \u4f7f\u7528\u5efa\u8bae\n\n- **\u5b9e\u9a8c\u4e0e\u9a8c\u8bc1**\uff1a\u5efa\u8bae\u5728\u5f00\u53d1\u96c6\u4e0a\u8bd5\u9a8c\u65b0\u7684\u6570\u636e\u589e\u5f3a\u65b9\u6848\u4ee5\u8bc4\u4f30\u5176\u5f71\u54cd\uff0c\u786e\u4fdd\u4e0d\u7834\u574f\u539f\u59cb\u6570\u636e\u7684\u57fa\u672c\u7279\u5f81\u3002\n- **\u9010\u6b65\u8c03\u8bd5**\uff1a\u9010\u6b65\u6dfb\u52a0\u6216\u8c03\u6574\u589e\u5f3a\u7b56\u7565\uff0c\u76d1\u63a7\u4e0d\u540c\u7b56\u7565\u7ec4\u5408\u5bf9\u7ed3\u679c\u5e26\u6765\u7684\u5f71\u54cd\u3002\n\n\u901a\u8fc7\u5c55\u5f00\u6570\u636e\u589e\u5f3a\u7b56\u7565\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u7684\u591a\u6837\u6027\u548c\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u8fd8\u53ef\u4ee5\u66f4\u597d\u5730\u5e94\u5bf9\u4e0d\u540c\u6a21\u6001\u4e0e\u7279\u5f81\u7684\u53d8\u5316\u3002\u5728\u4f18\u5316\u7f51\u7edc\u7ed3\u6784\u7684\u540c\u65f6\uff0c\u8f85\u52a9\u4ee5\u66f4\u5177\u5f39\u6027\u7684\u589e\u5f3a\u7b56\u7565\uff0c\u5c06\u6709\u52a9\u4e8e\u51cf\u8f7b\u8fc7\u62df\u5408\u73b0\u8c61\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1449,"n_turns_in_conversation":28,"n_words":105,"source_row_offset":30739,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"30d1be05feebdd382a7dd212","turn_index":16,"user_text":"\u5728\u4fee\u6539crossMamba\u4e4b\u524d\uff0c\u91c7\u7528\u7684\u6570\u636e\u589e\u5f3a\u662f\uff1atfm.RandomAffineTransform(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05), shear=2.5))\uff0c\u90a3\u4e48\u73b0\u5728\u5c06crossMamba\u4f18\u5316\u4e4b\u540e\uff0c\u6570\u636e\u589e\u5f3a\u5e94\u8be5\u5982\u4f55\u4fee\u6539\uff1f"},{"assistant_initiates":false,"asst_text":"\u786e\u5b9e\uff0c\u6570\u636e\u589e\u5f3a\u7b56\u7565\u7684\u9009\u62e9\u9700\u8981\u5728\u589e\u5f3a\u6570\u636e\u591a\u6837\u6027\u4e0e\u4fdd\u6301\u6570\u636e\u5408\u7406\u6027\u53ca\u771f\u5b9e\u6027\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002\u8fc7\u4e8e\u6fc0\u8fdb\u7684\u6570\u636e\u589e\u5f3a\u53ef\u80fd\u5bfc\u81f4\u751f\u6210\u7684\u56fe\u50cf\u4e0e\u771f\u5b9e\u5206\u5e03\u5dee\u5f02\u8fc7\u5927\uff0c\u8fdb\u800c\u59a8\u788d\u6a21\u578b\u7684\u7406\u89e3\u548c\u5b66\u4e60\u3002\u8ba9\u6211\u4eec\u5177\u4f53\u5206\u6790\u4f60\u6240\u63d0\u4f9b\u7684\u589e\u5f3a\u53c2\u6570\uff1a\n\n### \u5206\u6790\u5f53\u524d\u589e\u5f3a\u8bbe\u7f6e\n\n1. **\u65cb\u8f6c (`degrees=10`)**\uff1a\n   - \u589e\u52a0\u4e86\u65cb\u8f6c\u89d2\u5ea6\u7684\u8303\u56f4\uff0c\u4ece 5 \u5230 10 \u5ea6\uff0c\u8fd9\u53ef\u80fd\u5bf9\u67d0\u4e9b\u4efb\u52a1\u662f\u5408\u7406\u7684\uff0c\u4f46\u5982\u679c\u56fe\u50cf\u7684\u4e3b\u8981\u7279\u5f81\u65b9\u5411\u6027\u5f88\u5f3a\uff0c\u5219\u53ef\u80fd\u8fc7\u5927\u3002\n   \n2. **\u5e73\u79fb (`translate=(0.1, 0.1)`)**\uff1a\n   - \u5141\u8bb8 10% \u7684\u5e73\u79fb\uff0c\u8fd9\u4f1a\u5bfc\u81f4\u8f83\u5927\u7684\u4f4d\u79fb\u3002\u5bf9\u4e8e\u67d0\u4e9b\u68c0\u6d4b\u4efb\u52a1\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4 \u4e0d\u4e00\u81f4\u7684\u80cc\u666f\u6216\u8005\u7279\u5f81\u5207\u5272\u3002\n   \n3. **\u7f29\u653e (`scale=(0.9, 1.1)`)**\uff1a\n   - \u5141\u8bb8 10% \u7684\u653e\u5927\u6216\u7f29\u5c0f\uff0c\u4e00\u822c\u662f\u53ef\u4ee5\u63a5\u53d7\u7684\uff0c\u4f46\u8981\u770b\u5177\u4f53\u4efb\u52a1\u7ec6\u8282\uff1a\u5982\u679c\u76f8\u5bf9\u5c3a\u5ea6\u53d8\u5316\u5f71\u54cd\u5927\uff0c\u5219\u53ef\u8003\u8651\u7f29\u5c0f\u8303\u56f4\u3002\n\n4. **\u526a\u5207 (`shear=5`)**\uff1a\n   - 5 \u5ea6\u7684\u526a\u5207\u5bf9\u4e8e\u67d0\u4e9b\u5f62\u5f0f\u56fa\u5b9a\u7684\u7269\u4f53\u53ef\u80fd\u5bfc\u81f4\u8fc7\u5ea6\u626d\u66f2\u3002\n\n### \u5efa\u8bae\u7684\u8c03\u6574\n\n1. **\u964d\u4f4e\u65cb\u8f6c\u89d2\u5ea6\u548c\u526a\u5207**\uff1a\n   - \u5982\u679c\u6570\u636e\u53d8\u6362\u540e\u5bb9\u6613\u5f97\u51fa\u6781\u5927\u51e0\u4f55\u53d8\u52a8\uff0c\u53ef\u4ee5\u9002\u5f53\u51cf\u5c0f\u8fd9\u4e9b\u53c2\u6570\u3002\u4f8b\u5982\uff0c\u5c1d\u8bd5\uff1a`degrees=5` \u548c `shear=2`;\n   \n2. **\u51cf\u5c11\u5e73\u79fb\u5e45\u5ea6**\uff1a\n   - \u53ef\u4ee5\u5c06 `translate` \u7f29\u5c0f\u81f3 `(0.05, 0.05)`\uff0c\u4ee5\u51cf\u5c11\u4f4d\u79fb\u5e26\u6765\u7684\u80cc\u666f\u53d8\u5316\u3002\n   \n3. **\u4fdd\u6301\u6216\u9002\u5ea6\u8c03\u8282\u7f29\u653e\u8303\u56f4**\uff1a\n   - \u5982\u679c\u7f29\u653e\u5f71\u54cd\u4e0d\u5927\uff0c\u4fdd\u6301\u5f53\u524d 10% \u7684\u6bd4\u4f8b\u662f\u5408\u7406\u7684\u3002\u5982\u9700\u7cbe\u7ec6\u8c03\u6574\uff0c\u53ef\u4ee5\u8003\u8651\u7a0d\u5fae\u7f29\u5c0f\u8303\u56f4\u81f3 `(0.95, 1.05)`\u3002\n\n4. **\u9010\u6b65\u5b9e\u9a8c**\uff1a\n   - \u53ef\u4ee5\u901a\u8fc7\u591a\u6b21\u5c0f\u5e45\u5ea6\u8c03\u6574\uff0c\u501f\u52a9\u9a8c\u8bc1\u96c6\u6765\u786e\u8ba4\u6700\u4f73\u7684\u589e\u5f3a\u7b56\u7565\u3002\n\n### \u793a\u4f8b\u8c03\u6574\n\n```python\nimport torchvision.transforms as tfm\n\n# \u8c03\u6574\u540e\u7684\u6570\u636e\u589e\u5f3a\u914d\u7f6e\ntransform = tfm.Compose([\n    tfm.RandomAffine(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05), shear=2),  # \u8c03\u6574\u7684\u589e\u5f3a\u53c2\u6570\n    tfm.RandomHorizontalFlip(),  # \u4fdd\u6301\u6c34\u5e73\u7ffb\u8f6c\u4ee5\u589e\u52a0\u53d8\u5316\n])\n\n# \u5e94\u7528\u6570\u636e\u589e\u5f3a\naugmented_data = transform(original_data)\n```\n\n\u901a\u8fc7\u5b9e\u9a8c\u517c\u5177\u76f8\u5bf9\u4fdd\u5b88\u4e0e\u9002\u5ea6\u7684\u589e\u5f3a\u7b56\u7565\uff0c\u671f\u671b\u53ef\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u800c\u4e0d\u727a\u7272\u5bf9\u539f\u59cb\u6570\u636e\u5206\u5e03\u7684\u5c0a\u91cd\u3002\u5728\u6570\u636e\u591a\u6837\u6027\u548c\u6a21\u578b\u7a33\u5b9a\u6027\u4e4b\u95f4\u627e\u5230\u5e73\u8861\u70b9\u662f\u5b9e\u73b0\u6709\u6548\u6570\u636e\u589e\u5f3a\u7684\u5173\u952e\u3002","behavior":"degenerate_repetition","conversation_hash":"0ec794a2fdc1e75d2ec54be5ee8efec9","conversation_redacted":false,"conversation_toxic":false,"country":"Singapore","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4o-2024-08-06","n_chars":1226,"n_turns_in_conversation":28,"n_words":100,"source_row_offset":30740,"source_shard":"turns-00041.parquet","timestamp":"Wed, 30 Oct 2024 12:25:35 GMT","turn_id":"4468e19f1fa82b7d54bfc524","turn_index":17,"user_text":"tfm.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=5)\u662f\u4e0d\u662f\u8fc7\u4e8e\u589e\u5f3a\u4e86\uff1f"}],"shard":{"index":41,"name":"turns-00041.parquet","rows":42515}}