{"cells":[{"cell_type":"markdown","metadata":{"id":"DQPOXpeiqlXN"},"source":["## 加载模型网页版\n","安装所需的库以及下载模型仓库"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":83006,"status":"ok","timestamp":1714278049910,"user":{"displayName":"Jerome Valeska","userId":"16033697576553206766"},"user_tz":-480},"id":"LwAqBoBHZWks","outputId":"62e84d9c-6cdb-4c57-e6b1-90cd2bcea2ab"},"outputs":[{"name":"stdout","output_type":"stream","text":["Git LFS initialized.\n","Cloning into 'glm-emotion'...\n","remote: Enumerating objects: 68, done.\u001b[K\n","remote: Counting objects: 100% (65/65), done.\u001b[K\n","remote: Compressing objects: 100% (65/65), done.\u001b[K\n","remote: Total 68 (delta 26), reused 0 (delta 0), pack-reused 3\u001b[K\n","Unpacking objects: 100% (68/68), 30.83 KiB | 1.71 MiB/s, done.\n","Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from -r 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smmap-5.0.1-py3-none-any.whl (24 kB)\n","Requirement already satisfied: jsonschema-specifications\u003e=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema\u003e=3.0-\u003ealtair\u003c6.0,\u003e=4.2.0-\u003egradio==3.40.0-\u003e-r /content/glm-emotion/requirements.txt (line 5)) (2023.12.1)\n","Requirement already satisfied: referencing\u003e=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema\u003e=3.0-\u003ealtair\u003c6.0,\u003e=4.2.0-\u003egradio==3.40.0-\u003e-r /content/glm-emotion/requirements.txt (line 5)) (0.34.0)\n","Requirement already satisfied: rpds-py\u003e=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema\u003e=3.0-\u003ealtair\u003c6.0,\u003e=4.2.0-\u003egradio==3.40.0-\u003e-r /content/glm-emotion/requirements.txt (line 5)) (0.18.0)\n","Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.10/dist-packages (from linkify-it-py\u003c3,\u003e=1-\u003emarkdown-it-py[linkify]\u003e=2.0.0-\u003egradio==3.40.0-\u003e-r /content/glm-emotion/requirements.txt (line 5)) (1.0.3)\n","Requirement already satisfied: six\u003e=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil\u003e=2.7-\u003ematplotlib~=3.0-\u003egradio==3.40.0-\u003e-r /content/glm-emotion/requirements.txt (line 5)) (1.16.0)\n","Building wheels for collected packages: ffmpy\n"," Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=f247e55d8b9cc380a86df60827b00cb1dde4ea8bc8455ae270ef3dd9b96b9398\n"," Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n","Successfully built ffmpy\n","Installing collected packages: tokenizers, pydub, ffmpy, cpm_kernels, websockets, watchdog, smmap, semantic-version, python-multipart, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, markdown-it-py, latex2mathml, h11, aiofiles, uvicorn, starlette, pydeck, nvidia-cusparse-cu12, nvidia-cudnn-cu12, mdtex2html, mdit-py-plugins, httpcore, gitdb, transformers, sse-starlette, nvidia-cusolver-cu12, httpx, gitpython, fastapi, gradio-client, streamlit, gradio, accelerate\n"," Attempting uninstall: tokenizers\n"," Found existing installation: tokenizers 0.19.1\n"," Uninstalling tokenizers-0.19.1:\n"," Successfully uninstalled tokenizers-0.19.1\n"," Attempting uninstall: markdown-it-py\n"," Found existing installation: markdown-it-py 3.0.0\n"," Uninstalling markdown-it-py-3.0.0:\n"," Successfully uninstalled markdown-it-py-3.0.0\n"," Attempting uninstall: mdit-py-plugins\n"," Found existing installation: mdit-py-plugins 0.4.0\n"," Uninstalling mdit-py-plugins-0.4.0:\n"," Successfully uninstalled mdit-py-plugins-0.4.0\n"," Attempting uninstall: transformers\n"," Found existing installation: transformers 4.40.0\n"," Uninstalling transformers-4.40.0:\n"," Successfully uninstalled transformers-4.40.0\n","Successfully installed accelerate-0.29.3 aiofiles-23.2.1 cpm_kernels-1.0.11 fastapi-0.110.2 ffmpy-0.3.2 gitdb-4.0.11 gitpython-3.1.43 gradio-3.40.0 gradio-client-0.16.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 latex2mathml-3.77.0 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 mdtex2html-1.3.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 orjson-3.10.1 pydeck-0.9.0b1 pydub-0.25.1 python-multipart-0.0.9 semantic-version-2.10.0 smmap-5.0.1 sse-starlette-2.1.0 starlette-0.37.2 streamlit-1.33.0 tokenizers-0.13.3 transformers-4.30.2 uvicorn-0.29.0 watchdog-4.0.0 websockets-11.0.3\n"]}],"source":["!git lfs install\n","!git clone https://huggingface.co/Jerome2046/glm-emotion\n","!pip install -r /content/glm-emotion/requirements.txt"]},{"cell_type":"markdown","metadata":{"id":"023N9L1g0Bqf"},"source":["启动脚本"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"mR75YbuWu7yH"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/glm-emotion\n","/usr/local/lib/python3.10/dist-packages/gradio_client/documentation.py:103: UserWarning: Could not get documentation group for \u003cclass 'gradio.mix.Parallel'\u003e: No known documentation group for module 'gradio.mix'\n"," warnings.warn(f\"Could not get documentation group for {cls}: {exc}\")\n","/usr/local/lib/python3.10/dist-packages/gradio_client/documentation.py:103: UserWarning: Could not get documentation group for \u003cclass 'gradio.mix.Series'\u003e: No known documentation group for module 'gradio.mix'\n"," warnings.warn(f\"Could not get documentation group for {cls}: {exc}\")\n","2024-04-28 04:20:07.748302: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2024-04-28 04:20:07.748366: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2024-04-28 04:20:07.750130: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","2024-04-28 04:20:08.980266: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n","/content/glm-emotion/web_demo.py:101: GradioDeprecationWarning: The `style` method is deprecated. Please set these arguments in the constructor instead.\n"," user_input = gr.Textbox(show_label=False, placeholder=\"Input...\", lines=10).style(\n","tokenizer_config.json: 100% 244/244 [00:00\u003c00:00, 1.13MB/s]\n","tokenization_chatglm.py: 100% 10.1k/10.1k [00:00\u003c00:00, 35.4MB/s]\n","A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- tokenization_chatglm.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n","tokenizer.model: 100% 1.02M/1.02M [00:00\u003c00:00, 52.2MB/s]\n","config.json: 100% 1.32k/1.32k [00:00\u003c00:00, 6.93MB/s]\n","configuration_chatglm.py: 100% 2.33k/2.33k [00:00\u003c00:00, 11.2MB/s]\n","A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- configuration_chatglm.py\n",". Make sure to double-check they do not contain any added malicious code. 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initialized: ['transformer.prefix_encoder.embedding.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","Running on local URL: http://127.0.0.1:7860\n","Running on public URL: https://06fa039979e0b08b79.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"]}],"source":["%cd glm-emotion/\n","!bash web_demo.sh"]},{"cell_type":"markdown","metadata":{"id":"AvRZYv_2_q9Z"},"source":["例子\n","```\n","分析下面文段的情感类型：价格便宜、配置很足、什么双核啊、内存大啊、硬盘大啊、无线啊都有，打开一看，完美屏\n","分析下面文段的情感类型：新年第一天，好难受....这两天太累了，没休息好，脾气变暴躁，不小心间接伤害了我最亲的人......[泪]\n","```\n","## 加载模型使用\n","下载相关的模型依赖以及模型"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2214,"status":"ok","timestamp":1714231553348,"user":{"displayName":"Jerome Valeska","userId":"16033697576553206766"},"user_tz":-480},"id":"5AS7BONJ_aM7","outputId":"d8997aa0-7b12-4757-a41c-806f90e0425f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Git LFS initialized.\n","Cloning into 'glm-emotion'...\n","remote: Enumerating objects: 68, done.\u001b[K\n","remote: Counting objects: 100% (65/65), done.\u001b[K\n","remote: Compressing objects: 100% (65/65), done.\u001b[K\n","remote: Total 68 (delta 26), reused 0 (delta 0), pack-reused 3\u001b[K\n","Unpacking objects: 100% (68/68), 30.83 KiB | 1.54 MiB/s, done.\n"]}],"source":["!git lfs install\n","!git clone https://huggingface.co/Jerome2046/glm-emotion"]},{"cell_type":"markdown","metadata":{"id":"DvlKHPPsAVZr"},"source":["加载模型"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":58256,"status":"ok","timestamp":1714231616107,"user":{"displayName":"Jerome Valeska","userId":"16033697576553206766"},"user_tz":-480},"id":"0Efsrn6qUeEC","outputId":"240731e5-83cb-44fe-f7f9-5453e714e9f1"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"51cc21083f0942b196dbee886b37bd3b","version_major":2,"version_minor":0},"text/plain":["tokenizer_config.json: 0%| | 0.00/244 [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f285398671334ba2900e049567454482","version_major":2,"version_minor":0},"text/plain":["tokenization_chatglm.py: 0%| | 0.00/10.1k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- tokenization_chatglm.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"76a6054035f345c6817227cc94629206","version_major":2,"version_minor":0},"text/plain":["tokenizer.model: 0%| | 0.00/1.02M [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4a5493d2869d4d7a9fbc1192a888f1fd","version_major":2,"version_minor":0},"text/plain":["config.json: 0%| | 0.00/1.32k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2eaf86638d59469288279d9f50d27445","version_major":2,"version_minor":0},"text/plain":["configuration_chatglm.py: 0%| | 0.00/2.33k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- configuration_chatglm.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"40bf016a938f41d3bf7a1087df93201b","version_major":2,"version_minor":0},"text/plain":["modeling_chatglm.py: 0%| | 0.00/54.9k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"82114ed8ab1043dc8e0ce789096095fd","version_major":2,"version_minor":0},"text/plain":["quantization.py: 0%| | 0.00/14.7k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- quantization.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n","A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n","- modeling_chatglm.py\n","- quantization.py\n",". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"241e9dffdb7f49b2a25f7c07fc9d9644","version_major":2,"version_minor":0},"text/plain":["pytorch_model.bin.index.json: 0%| | 0.00/20.4k [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5255aefcc4a74608ab395bc7c34ff8b4","version_major":2,"version_minor":0},"text/plain":["Downloading shards: 0%| | 0/7 [00:00\u003c?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"bf7ef84ae05e4d0f89c5ba6f5ea188f3","version_major":2,"version_minor":0},"text/plain":["pytorch_model-00001-of-00007.bin: 0%| | 0.00/1.83G [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"94016e899f3040a68bafdd4a93934beb","version_major":2,"version_minor":0},"text/plain":["pytorch_model-00002-of-00007.bin: 0%| | 0.00/1.97G [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"78c4f297032143b5972ebd0c5a385f01","version_major":2,"version_minor":0},"text/plain":["pytorch_model-00003-of-00007.bin: 0%| | 0.00/1.93G [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8e2ca16cd8f64c11a131e2c189ec22cd","version_major":2,"version_minor":0},"text/plain":["pytorch_model-00004-of-00007.bin: 0%| | 0.00/1.82G [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"06755b0f4d544d76b86fa1c328ec72ae","version_major":2,"version_minor":0},"text/plain":["pytorch_model-00005-of-00007.bin: 0%| | 0.00/1.97G [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d82b8e45386343d1848356863370f668","version_major":2,"version_minor":0},"text/plain":["pytorch_model-00006-of-00007.bin: 0%| | 0.00/1.93G [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e1f47f3009d149168457a6a80d6928ef","version_major":2,"version_minor":0},"text/plain":["pytorch_model-00007-of-00007.bin: 0%| | 0.00/1.05G [00:00\u003c?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fc043a5e201945c4a3ceb882d4e89064","version_major":2,"version_minor":0},"text/plain":["Loading checkpoint shards: 0%| | 0/7 [00:00\u003c?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Some weights of ChatGLMForConditionalGeneration were not initialized from the model checkpoint at THUDM/chatglm2-6b and are newly initialized: ['transformer.prefix_encoder.embedding.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"data":{"text/plain":["ChatGLMForConditionalGeneration(\n"," (transformer): ChatGLMModel(\n"," (embedding): Embedding(\n"," (word_embeddings): Embedding(65024, 4096)\n"," )\n"," (rotary_pos_emb): RotaryEmbedding()\n"," (encoder): GLMTransformer(\n"," (layers): ModuleList(\n"," (0-27): 28 x GLMBlock(\n"," (input_layernorm): RMSNorm()\n"," (self_attention): SelfAttention(\n"," (query_key_value): Linear(in_features=4096, out_features=4608, bias=True)\n"," (core_attention): CoreAttention(\n"," (attention_dropout): Dropout(p=0.0, inplace=False)\n"," )\n"," (dense): Linear(in_features=4096, out_features=4096, bias=False)\n"," )\n"," (post_attention_layernorm): RMSNorm()\n"," (mlp): MLP(\n"," (dense_h_to_4h): Linear(in_features=4096, out_features=27392, bias=False)\n"," (dense_4h_to_h): Linear(in_features=13696, out_features=4096, bias=False)\n"," )\n"," )\n"," )\n"," (final_layernorm): RMSNorm()\n"," )\n"," (output_layer): Linear(in_features=4096, out_features=65024, bias=False)\n"," (prefix_encoder): PrefixEncoder(\n"," (embedding): Embedding(128, 14336)\n"," )\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n",")"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["import torch\n","import os\n","from transformers import AutoConfig, AutoModel, AutoTokenizer\n","\n","# 载入Tokenizer与模型\n","tokenizer = AutoTokenizer.from_pretrained(\"THUDM/chatglm2-6b\", trust_remote_code=True)\n","config = AutoConfig.from_pretrained(\"THUDM/chatglm2-6b\", trust_remote_code=True, pre_seq_len=128)\n","model = AutoModel.from_pretrained(\"THUDM/chatglm2-6b\", config=config, trust_remote_code=True)\n","# 载入预训练参数\n","prefix_state_dict = torch.load(os.path.join(\"glm-emotion\", \"pytorch_model.bin\"))\n","new_prefix_state_dict = {}\n","for k, v in prefix_state_dict.items():\n"," if k.startswith(\"transformer.prefix_encoder.\"):\n"," new_prefix_state_dict[k[len(\"transformer.prefix_encoder.\"):]] = v\n","model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)\n","model.half().cuda().eval()"]},{"cell_type":"markdown","metadata":{"id":"6Jg1SQqZAZPR"},"source":["直接使用"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7202,"status":"ok","timestamp":1714231625417,"user":{"displayName":"Jerome Valeska","userId":"16033697576553206766"},"user_tz":-480},"id":"W7xsBn_L_zRP","outputId":"3c4ee712-6aba-41c4-c0df-6886cb7f21b3"},"outputs":[{"name":"stdout","output_type":"stream","text":["这段文段的情感类型是正面的。\n"]}],"source":["response, history = model.chat(tokenizer, '分析下面文段的情感类型：价格便宜、配置很足、什么双核啊、内存大啊、硬盘大啊、无线啊都有，打开一看，完美屏', history=[])\n","print(response)"]},{"cell_type":"markdown","metadata":{"id":"SYtr3Xl5AgRp"},"source":["或者根据文件内容进行分析"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7615,"status":"ok","timestamp":1714231653490,"user":{"displayName":"Jerome Valeska","userId":"16033697576553206766"},"user_tz":-480},"id":"rpW--mxfAPWf","outputId":"6896b365-b259-4039-e1d0-3a233c71a5e0"},"outputs":[{"name":"stdout","output_type":"stream","text":["0 {'response': '这篇文段表达的情感类别是批评（负面）。', 'content': '请下列文段的情感类别是什么：女权本质上是平权，但是不应改名叫平权。 因为，如果社会公认只能说平权，那么“女权”这两个字，就会被污名化。未来再说“女权”，就跟现在说“女拳”一样，变成一种嘲讽。 进一步说，这个思路，是非常隐秘的杯葛。 举个例子你就明白了。最近有个新闻，西班牙在推一个谴责针对女性暴力的宣言，极右的呼声党拒绝签字，理由是：也有女的打男的呀！所以为什么我们要发出一个专门反对男的家暴女性的宣言呢？ 是不是听起来很耳熟？这种事情真是没有文化隔阂，大家思路都差不多。它跟“没有女权只有平权”这个说法的逻辑是一模一样的：既然也有女性针对男性的暴力，为什么要专门谴责男性针对女性的暴力呢？既然女权的本质是平权，为什么要专门说“女”权呢？ 对此，但凡你的脑子还有转动的能力，就会回答说：你谴责你的，我谴责我的呗！本来应该是盟友，至少是井水不犯河水，现在居然变成了你不谴责你想谴责的，就规定我也不准谴责我想谴责的，凭什么啊？！ 而如果你想得再深一步，就会开始琢磨：这家伙，平时不谴责家暴，现在以“也有女性对男性的家暴”为名，反对谴责“男性对女性的家暴”，他的初衷，到底是觉得家暴应该谴责，还是觉得家暴这种事情，根本就无所谓呢？ 现在你明白这个思路的阴险之处了吧？女权本质上当然是平权，这是一个理论问题；然而关键是，然后呢？然后你是用平权的精神来发出女权的声音，还是用平权的名义来限制女权的表达？这是一个操作问题。 而操作方向，就是看出鸡贼的地方了。转发理由://@ieaber:明白人。改了“平权”掩盖了女性权力被剥夺，被压迫的事实，是非常鸡贼的话术。人家说“lgbt平权”，你一异性恋上去高高兴兴拉着手说说“所有性向平权哈”，掩盖了人家没平权是因为你丫异性恋专权的打压。否认压迫，淡化矛盾，不想解决问题'}\n","1 {'response': '这段文段表达的情感类别是愤怒和批评。', 'content': '请下列文段的情感类别是什么：\"#论坛说事[超话]#【“9岁袭胸，13岁性侵”，母子不分床种下的恶因，终结出“恶果”！】 父母是孩子最亲近的人，孩子做的很多事都是在父母的指导下完成的，比如，吃饭、睡觉都是一起的，也没有什么性别方面的顾忌的，但是，随着孩子年龄的逐渐增长，孩子对于性别方面的意识也清晰明了，作为家长，这时候就要停止这种行为了，不然造成的后果谁也无法想象。 案例！英国的一个小男孩 9岁袭胸，13 岁性侵，这个小男孩在家里和外面表现出的就是乖乖仔的那种形象，装得什么都不懂，外人就是认为他是\"\"妈宝男\"\"，而当没有大人在的时候就开始谈那些不堪入目的话题，也会随意地调戏女孩子，甚至还被他袭胸过的女孩子的父母投诉他，他的父母并不以为然，觉得这是孩子之间的正常交往。 可谁知道，四年后，这个男孩居然做出了性侵女孩子的事，调查才发现，原来这个男孩父母一直和孩子同床睡，也没有对孩子进行关于性方面的教育，才会酿成这样的\"\"恶果\"\"。 分析！孩子到了一定的年龄，家长不和孩子分床睡，会给孩子带来的影响。 1、促使孩子太早性早熟 孩子会随着年龄慢慢地长大，可是父母却还是把孩子当做是孩子，一直在手心上捧着，还不愿意和孩子分床睡，可是这样做造成的后果就是促使孩子过早性成熟，比其他的同龄孩子成熟得多，家长在孩子三岁的时候就应该和孩子分床睡，孩子三岁时就逐渐有了关于性别方面的认识。 2、对性别方面的知识模棱两可 父母不和孩子分床睡，就会导致孩子孩子对性别知识模棱两可，不会懂得男女之间的区别是什么。但是，孩子会慢慢地成长，心理生理都会有变化，而父母的能做的就是教导孩子认识关于性别方面的知识，孩子就会明白男女有别。 3、不会独立 孩子学会自己一个人睡，是独立的最好证明，对孩子的成长有很大的帮助。家长舍不得和孩子分床睡，会促使孩子过于依赖家长，做什么事都离不开家长的帮助，很容易就使孩子成为\"\"妈宝孩\"\"，因此，家长可以培养孩子独立性，从分床做起，要是家长不和孩子分床，会使孩子变得胆小懦弱。 家长应该如何和孩子分床睡 1、教导关于性别方面的区别 孩子的成长是需要父母的引导，父母也是孩子的榜样，关于性知识的方面是需要父母教导孩子，其实孩子的生活就是一片白纸，很多事情都是不懂不明白的，作为父母，应该教育孩子有关性的知识，而不是让孩子懵懂无知，对性方面却是一片空白，不然会毁了孩子。 2、给孩子足够的安全感 父母总是认为孩子永远长不大，这样的孩子是很难成长起来，孩子本身就是对性知识是懵懂无知的，要想和孩子分床睡，父母应该给孩子做好心理准备，别让孩子被动接受，而有时候孩子离不开父母就是缺少安全感的最好证明。在和孩子分床睡之前，应该给足够的安全感孩子。 总结！孩子终有一天会长大的，总会脱离父母的庇护而高飞的，这时候父母就要给予孩子足够的空间，让孩子学会独立，并给他们正确的指导 ，应该教会孩子什么应该做什么不应该做，别因为自己对孩子的宠溺而毁了孩子。#论坛说事#转发理由:转发微博\"'}\n","2 {'response': '这段文段表达的情感类别是愤怒。', 'content': '请下列文段的情感类别是什么：武汉不明原因肺炎不能断定是SARS@手机浏览器 武汉不明原因肺炎不能断定是SARS 上海 显示地图'}\n","3 {'response': '这段文段的情感类别是正面的。', 'content': '请下列文段的情感类别是什么：伊斯坦布尔 Dr.B 抽脂＋脂肪丰臀面诊 案例太惊艳，看到立刻定了机票飞过来面诊。 第一次来穆斯林国家还是有点害怕，定了机票开始后悔，想着应该还是会在美国做。不过来了以后想法完全变了。 首先就是，土耳其太好玩了！文化和自然景观都很棒！土耳其历史悠久，文化遗产之多堪比我大中华，地中海风景优美。其次就是土耳其居然挺发达的，伊斯坦布尔不比上海和首尔差吧，交通方便，城市现代化。 重点要说面诊咯。医生太勤奋了，工作时间从早上8点到晚上8点，体力惊人啊（话说这边都是看起来很行的壮汉。。。）面诊地点在伊斯坦布尔最高档的购物中心里面，他有属于自己的做手术的医院在附近，他说医院比面诊处还高级，因为他的客人都是欧洲来的贵族贵妇。 面诊很快，没有像美国医生那么详细，我脱完他看了一下就说，知道你身材啥样了，做吧，会有radical change，然后就让我穿衣服。出来以后我自己主动问了几个问题。 1.抽脂使用的仪器 和美国医生一样，用的是power-assisted，不用威塑。美国真的很少很少医生用威塑，大多数用的都是power-assisted的，Dr.B也说power-assisted最好，不过看他英语不是很好的亚子我没问原因，在美国医生那边已经问过很多次。 2.Dr.B很多案例术后的皮肤比术前还紧致，我问他怎么做到的。 他说这是个big work（依然英语不是很好的亚子），靠他神奇的双手[允悲] 另外他和很多美国医生一样，抽脂后会用J-plasma收紧皮肤。有些医生说J-plasma没用，但也有不少医生说有用，意见不统一，不过他的案例确实很紧致了。 3.术后护理 脂肪丰臀手术最麻烦的就是术后好久不能坐，美国一般至少2周，多的甚至6周，Dr.B说只要一周，这个太心动了，不然术后很难熬。 4.报价 抽腰腹＋背，填充到臀部，一共是1.3万欧元。 5.图一是整形保险，两年内需要修复的话，有保险cover，适用于加入了ISAPS这个组织的所有医生。这个保险第一次听说。 6.图三是Dr.B说跟我接近的身材，我能达到的效果[允悲]图四是修复抽脂后凹凸不平皮肤松弛的案例。 7.他说我不需要抽手臂，但是所有的美国医生都认为我应该抽，并且抽的效果会很好。顶尖的医生也存在审美和技术差异，这个医生我认为是更擅长腰腹臀，不太擅长手臂或大腿的。抽脂最好还是分部为找不同的医生做。 8.Dr.B说抽脂前应该再减个三四公斤，这样塑形效果更好[允悲][允悲][允悲]美国医生都吃胖点再抽[doge][doge][doge]也不知道是美国和欧洲观念差异。这里美女比美国多很多，我在路上经常目不转睛地盯着美女看。 9.面诊完，问了我一下需不需要他们后面再联系我，然后就直接让我走了，没有任何推手术的行为。付了面诊费100欧元。 http://t.cn/RU1y3lA转发理由:土耳其抽脂'}\n","4 {'response': '这段文段落表达的情感类别是探讨和反思。', 'content': '请下列文段的情感类别是什么：父母常说，我们一定要给孩子最好的。但是如何定义“最好的”？ 有共识的就是最好的教育，即最好的学校，最好的居住环境，最好的体验比如旅游，最好的课外活动比如各种兴趣的培养等等。我认为这都不是从孩子切身角度考虑的最好，那么对于孩子而言，什么是最好的？ 是无条件的爱。这个最好是无论种族社会地位的每个父母都能给孩子的。可惜的是，很多的父母，无论亚裔还是欧裔，始终不能明白一个最简单的道理，就是他们认为最好的不一定是别人最想要的。今天最强烈地感受到这点。朋友刚刚告诉我，她女儿的两位大学好友，一个开学前自杀了，另一位打算退学。两位都是私校毕业的优等生，上的是英国顶尖大学的医学系。一个亚裔，一个英裔。没有人给出顶尖大学优等生因心理问题辍学的数字，但我可以肯定地说，这个比例高到我们无法想象。在我认识的几家人里，就有优等生长期抑郁的两位，还有两位有自闭倾向，还有几位不同程度地承受着心理问题。我不能保证我的孩子此生不被心理问题困扰，但我可以保证给他们最好的，就是无条件的爱和尊重。一次考试不好，天塌不下来； 一个专业没选好，天塌不下来； 工作暂时无着落，天塌不下来； 大学没能进入心仪的，天塌不下来... 还是那句话，除了生命，一切都可以从头再来！转发理由:是这样的。'}\n","5 {'response': '这段文段的情感类别是新闻报道。', 'content': '请下列文段的情感类别是什么：【[话筒]#兰州兽医研究所布鲁氏菌阳性人数增至96人# 目前均为隐性感染】记者从兰州市疾控中心了解到，截至12月7日中午12点，已经对兰州兽医研究所317名师生进行布鲁氏菌检测，其中96人呈血清学阳性，目前均为隐性感染，无明显症状。专家提示，布鲁氏菌病人与人传染几率极低，目前没有过人间传染病例。（央视）转发理由:转发微博'}\n","6 {'response': '这段文段表现出一种兴奋和激动的情感,因为它包含了多个感叹号和祝福。它似乎在欢迎某个事件或产品,并描述了多个不同的东西。', 'content': '请下列文段的情感类别是什么：\"来了！！！双十二来了！18张图！！！ 九号晚上8点是法代和日代开始，十号晚上8点是玫瑰店开始！法代指路圣诞巧克力/黑巧，kiko圣诞限量，Filorga的高端十倍NCEF系列，雅诗敦A醇系列，生物肽顶级系列，当然还有svr，sesderma，天露芬的白菜清仓'}\n","7 {'response': '这段文段落表现出了日环食的壮观和美丽,描述了人们观测到的日环食景象,以及不同地区的日环食发生时间和持续时间。文段没有表达情感色彩或态度。', 'content': '请下列文段的情感类别是什么：【#金环日食奇观26日上演# 我国全境可观日偏食】本月26日天宇将上演壮观的日环食天象。天文专家表示，本次日环食发生期间，我国全境都可以观测到日偏食，且越往南食分（月亮遮住太阳直径的比例）越大。 当太阳、月球和地球排成一条直线，月球位于两者之间时就会发生日食天象。日食又分全食、环食和偏食，如果当天月球距离地球较远，而太阳距离地球较近，月球的视圆面较小而不能全部遮掩太阳，就会发生日环食。日环食发生时，天空中的太阳变成了一个圆环，金光灿灿犹如一只‘金指环’。 中国天文学会会员、天津市天文学会理事史志成介绍说，此次日环食将于北京时间26日10时29分50秒开始，至16时05分43秒终止，历经5个多小时。环食带从中东地区开始，经印度南端、斯里兰卡，进入东南亚的印度尼西亚、新加坡、马来西亚等国，最后结束于西太平洋上。这次的环食带平均宽度在115千米左右，见食地最长时间可以看到3分40秒左右的环食。 “10时56分左右月亮半影进入我国西藏阿里地区，而后月亮半影向东北上扫过中国全境，我国均可看到不同程度的日偏食，其中，海南食分最大，为0.499；漠河食分最小，为0.021。15时37分后月亮半影由海南省离开我国，对我国来说，可见日食时间历时4个多小时。”史志成说。 天文专家表示，虽然不及日全食和日环食般壮观，但偏食亦精彩，届时，太阳缺了一个角，好似真的被传说中的天狗咬掉了。（记者周润健）转发理由:期待[挤眼]'}\n","8 {'response': '这段文段的感情类别是医疗。', 'content': '请下列文段的情感类别是什么：皮肤科小医生讲科普 皮肤科小医生的病例分享 扁平疣是由人乳头状瘤病毒（HPV）感染引起的病毒性疾病。临床表现为皮色或粉红色的扁平丘疹，[钱][钱][钱][钱]KBS导播多见于面部和手背，无明显的自觉症状，病程慢性。可通过直接或间接的接触传染。好发于青少年，可突'}\n","9 {'response': '这段文段表达的情感类别是感慨和励志。它通过一个美国小女孩和一个中国小女孩的不同人生目标引起了作者的感慨，并强调了人们应该关注自己的初心和真正的爱情，而不是关注外表和财富。', 'content': '请下列文段的情感类别是什么：这是一个美国小女孩与一个中国小女孩不同的人生目标，看完让人心痛！我相信这绝不是个例。 片片腊味情的微博视频 转发理由://@花猫王子://@伊人5200:中国小女孩油腻是油腻，但也是大人教的。关键是好多女孩长大后怕都是“忘了初心”，找的人可不是“帅的，有钱，有学问，住别墅的”，而是为了“甜甜的爱情”大把大把牺牲奉献，甚至把命都搭上的。还谈什么个人理想和梦想啊。'}\n"]}],"source":["import json\n","with open('/content/drive/MyDrive/dataset/cov.json', 'r') as file:\n"," data = json.load(file)[:10]\n"," new_data = []\n"," cnt = 0\n"," for item in data:\n"," response, history = model.chat(tokenizer, item['content'], history=[])\n"," tmp = 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