Dementia_llm2.ipynb

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        "!pip install bertopic datasets accelerate bitsandbytes xformers adjustText"
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      "execution_count": 3,
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.24.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu118)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.19.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c66be10f-bacd-404b-8ba9-81d6ff622668 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_text = df['processed'].tolist()"
      ],
      "metadata": {
        "id": "XQLNRZBKrIfg"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "28e8be5a4bdf442dbda84acc5fc803ff",
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            "4a6ad33063334403b651cfa08f6e74fa",
            "d4f9de5ccc294f74bfd3e0ecda6075aa"
          ]
        },
        "id": "kAMSXYMTM8lD",
        "outputId": "90446a31-ebd5-496e-a8ee-2feee3b05d7b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "28e8be5a4bdf442dbda84acc5fc803ff"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import cuda\n",
        "\n",
        "model_id = 'meta-llama/Llama-2-13b-chat-hf'\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ox5PCcBDpnJk",
        "outputId": "2fa4848f-86ba-4952-9978-6e762862f9df"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import bfloat16\n",
        "import transformers\n",
        "\n",
        "# set quantization configuration to load large model with less GPU memory\n",
        "# this requires the `bitsandbytes` library\n",
        "\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,  # 4-bit quantization\n",
        "    bnb_4bit_quant_type='nf4',  # Normalized float 4\n",
        "    bnb_4bit_use_double_quant=True,  # Second quantization after the first\n",
        "    bnb_4bit_compute_dtype=bfloat16  # Computation type\n",
        ")"
      ],
      "metadata": {
        "id": "s2Bjqt8ApqKC"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Llama 2 Tokenizer\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "# Llama 2 Model\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto',\n",
        ")\n",
        "model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852,
          "referenced_widgets": [
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        "id": "AYYWVhelpsr3",
        "outputId": "c897cb0d-3a8d-4f68-a7d2-11b7bc2d39ba"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ea10a9e5d8a0483abc0eee7c3a8ca46d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3900d18b4434b75875f86e8ac2243a0"
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          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "a2ea50ef11ef451d85da993c7f589b4b"
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        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
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            ],
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              "model_id": "e04098898d8a4405924978d70ef1c929"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c83c4fe35bad4695ba0f854573225f92"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9eea6c1994d4fc88b014380f47f7bfd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(32000, 5120)\n",
              "    (layers): ModuleList(\n",
              "      (0-39): 40 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaAttention(\n",
              "          (q_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
              "          (k_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
              "          (v_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
              "          (o_proj): Linear4bit(in_features=5120, out_features=5120, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear4bit(in_features=5120, out_features=13824, bias=False)\n",
              "          (up_proj): Linear4bit(in_features=5120, out_features=13824, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=13824, out_features=5120, bias=False)\n",
              "          (act_fn): SiLUActivation()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm()\n",
              "        (post_attention_layernorm): LlamaRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=5120, out_features=32000, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Our text generator\n",
        "generator = transformers.pipeline(\n",
        "    model=model, tokenizer=tokenizer,\n",
        "    task='text-generation',\n",
        "    temperature=0.1,\n",
        "    max_new_tokens=500,\n",
        "    repetition_penalty=1.1\n",
        ")"
      ],
      "metadata": {
        "id": "yvX5nZ1lp3ww"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_text[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwRX41Fo2hg2",
        "outputId": "71e11a62-fc24-4ccc-e840-fa6cb106adc9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['dementia is tough to deal with see a doctor he can give you a note and you can go home back to your tower and your old life',\n",
              " 'if he ever did paranoia narcissism and dementia have eaten it all up',\n",
              " 'or donald trump vs hillary clinton for effects of syphilis dementia insomnia fantasy irritability weak risk assessment',\n",
              " 'he must be suffering from dementia the last guy lied like it was his job',\n",
              " 'should you visit your loved one every day via thx amp dementia amwriting']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "non_strings = [i for i, item in enumerate(processed_text) if not isinstance(item, str)]\n",
        "print(\"Non-string indices:\", non_strings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gZcCTN320qF",
        "outputId": "1cdd078f-d98a-4531-afe3-ad9632d3aebb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Non-string indices: [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 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19283, 19284, 19285, 19286, 19287, 19288, 19289, 19290, 19291, 19292, 19293, 19294, 19295, 19296, 19297, 19298, 19299, 19300, 19301, 19302, 19303, 19304, 19305, 19306, 19307, 19308, 19309, 19310, 19311, 19312, 19313, 19314, 19315, 19316, 19317, 19318, 19319, 19320, 19321, 19322, 19323, 19324, 19325, 19326, 19327, 19328, 19329, 19330, 19331, 19332, 19333, 19334, 19335, 19336, 19337, 19338, 19339, 19340, 19341, 19342, 19343, 19344, 19345, 19346, 19347, 19348, 19349, 19350, 19351, 19352, 19353, 19354, 19355, 19356, 19357, 19358, 19359, 19360, 19361, 19362, 19363, 19364, 19365, 19366, 19367, 19368, 19369, 19370, 19371, 19372, 19373, 19374, 19375, 19376, 19377, 19378, 19379, 19380, 19381, 19382, 19383, 19384, 19385, 19386, 19387, 19388, 19389, 19390, 19391, 19392, 19393, 19394, 19395, 19396, 19397, 19398, 19399, 19400, 19401, 19402, 19403, 19404, 19405, 19406, 19407, 19408, 19409, 19410, 19411, 19412, 19413, 19414, 19415, 19416, 19417, 19418, 19419, 19420, 19421, 19422, 19423, 19424, 19425, 19426, 19427, 19428, 19429, 19430, 19431, 19432, 19433, 19434, 19435, 19436, 19437, 19438, 19439, 19440, 19441, 19442, 19443, 19444, 19445, 19446, 19447, 19448, 19449, 19450, 19451, 19452, 19453, 19454, 19455, 19456, 19457, 19458, 19459, 19460, 19461, 19462, 19463, 19464, 19465, 19466, 19467, 19468, 19469, 19470, 19471, 19472, 19473, 19474, 19475, 19476, 19477, 19478, 19479, 19480, 19481, 19482, 19483, 19484, 19485, 19486, 19487, 19488, 19489, 19490, 19491, 19492, 19493, 19494, 19495, 19496, 19497, 19498, 19499, 19500, 19501, 19502, 19503, 19504, 19505, 19506, 19507, 19508, 19509, 19510, 19511, 19512, 19513, 19514, 19515, 19516, 19517, 19518, 19519, 19520, 19521, 19522, 19523, 19524, 19525, 19526, 19527, 19528, 19529, 19530, 19531, 19532, 19533, 19534, 19535, 19536, 19537, 19538, 19539, 19540, 19541, 19542, 19543, 19544, 19545, 19546, 19547, 19548, 19549, 19550, 19551, 19552, 19553, 19554, 19555, 19556, 19557, 19558, 19559, 19560, 19561, 19562, 19563, 19564, 19565, 19566, 19567, 19568, 19569, 19570, 19571, 19572, 19573, 19574, 19575, 19576, 19577, 19578, 19579, 19580, 19581, 19582, 19583, 19584, 19585, 19586, 19587, 19588, 19589, 19590, 19591, 19592, 19593, 19594, 19595, 19596, 19597, 19598, 19599, 19600, 19601, 19602, 19603, 19604, 19605, 19606, 19607, 19608, 19609, 19610, 19611, 19612, 19613, 19614, 19615, 19616, 19617, 19618, 19619, 19620, 19621, 19622, 19623, 19624, 19625, 19626, 19627, 19628, 19629, 19630, 19631, 19632, 19633, 19634, 19635, 19636, 19637, 19638, 19639, 19640, 19641, 19642, 19643, 19644, 19645, 19646, 19647, 19648, 19649, 19650, 19651, 19652, 19653, 19654, 19655, 19656, 19657, 19658, 19659, 19660, 19661, 19662, 19663, 19664, 19665, 19666, 19667, 19668, 19669, 19670, 19671, 19672, 19673, 19674, 19675, 19676, 19677, 19678, 19679, 19680, 19681, 19682, 19683, 19684, 19685, 19686, 19687, 19688, 19689, 19690, 19691, 19692, 19693, 19694, 19695, 19696, 19697, 19698, 19699, 19700, 19701, 19702, 19703, 19704, 19705, 19706, 19707, 19708, 19709, 19710, 19711, 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40590, 40591, 40592, 40593, 40594, 40595, 40596, 40597, 40598, 40599, 40600, 40601, 40602, 40603, 40604, 40605, 40606, 40607, 40608, 40609, 40610, 40611, 40612, 40613, 40614, 40615, 40616, 40617, 40618, 40619, 40620, 40621, 40622, 40623, 40624, 40625, 40626, 40627, 40628, 40629, 40630, 40631, 40632, 40633, 40634, 40635, 40636, 40637, 40638, 40639, 40640, 40641, 40642, 40643, 40644, 40645, 40646, 40647, 40648, 40649, 40650, 40651, 40652, 40653, 40654, 40655, 40656, 40657, 40658, 40659, 40660, 40661, 40662, 40663, 40664, 40665, 40666, 40667, 40668, 40669, 40670, 40671, 40672, 40673, 40674, 40675, 40676, 40677, 40678, 40679, 40680, 40681, 40682, 40683, 40684, 40685, 40686, 40687, 40688, 40689, 40690, 40691, 40692, 40693, 40694, 40695, 40696, 40697, 40698, 40699, 40700, 40701, 40702, 40703, 40704, 40705, 40706, 40707, 40708, 40709, 40710, 40711, 40712, 40713, 40714, 40715, 40716, 40717, 40718, 40719, 40720, 40721, 40722, 40723, 40724, 40725, 40726, 40727, 40728, 40729, 40730, 40731, 40732, 40733, 40734, 40735, 40736, 40737, 40738, 40739, 40740, 40741, 40742, 40743, 40744, 40745, 40746, 40747, 40748, 40749, 40750, 40751, 40752, 40753, 40754, 40755, 40756, 40757, 40758, 40759, 40760, 40761, 40762, 40763, 40764, 40765, 40766, 40767, 40768, 40769, 40770, 40771, 40772, 40773, 40774, 40775, 40776, 40777, 40778, 40779, 40780, 40781, 40782, 40783, 40784, 40785, 40786, 40787, 40788, 40789, 40790, 40791, 40792, 40793, 40794, 40795, 40796, 40797, 40798, 40799, 40800, 40801, 40802, 40803, 40804, 40805, 40806, 40807, 40808, 40809, 40810, 40811, 40812, 40813, 40814, 40815, 40816, 40817, 40818, 40819, 40820, 40821, 40822, 40823, 40824, 40825, 40826, 40827, 40828, 40829, 40830, 40831, 40832, 40833, 40834, 40835, 40836, 40837, 40838, 40839, 40840, 40841, 40842, 40843, 40844, 40845, 40846, 40847, 40848, 40849, 40850, 40851, 40852, 40853, 40854, 40855, 40856, 40857, 40858, 40859, 40860, 40861, 40862, 40863, 40864, 40865, 40866, 40867, 40868, 40869, 40870, 40871, 40872, 40873, 40874, 40875, 40876, 40877, 40878, 40879, 40880, 40881, 40882, 40883, 40884, 40885, 40886, 40887, 40888, 40889, 40890, 40891, 40892, 40893, 40894, 40895, 40896, 40897, 40898, 40899, 40900, 40901, 40902, 40903, 40904, 40905, 40906, 40907, 40908, 40909, 40910, 40911, 40912, 40913, 40914, 40915, 40916, 40917, 40918, 40919, 40920, 40921, 40922, 40923, 40924, 40925, 40926, 40927, 40928, 40929, 40930, 40931, 40932, 40933, 40934, 40935, 40936, 40937, 40938, 40939, 40940, 40941, 40942, 40943, 40944, 40945, 40946, 40947, 40948, 40949, 40950, 40951, 40952, 40953, 40954, 40955, 40956, 40957, 40958, 40959, 40960, 40961, 40962, 40963, 40964, 40965, 40966, 40967, 40968, 40969, 40970, 40971, 40972, 40973, 40974, 40975, 40976, 40977, 40978, 40979, 40980, 40981, 40982, 40983, 40984, 40985, 40986, 40987, 40988, 40989, 40990, 40991, 40992, 40993, 40994, 40995, 40996, 40997, 40998, 40999, 41000, 41001, 41002, 41003, 41004, 41005, 41006, 41007, 41008, 41009, 41010, 41011, 41012, 41013, 41014, 41015, 41016, 41017, 41018, 41019, 41020, 41021, 41022, 41023, 41024, 41025, 41026, 41027, 41028, 41029, 41030, 41031, 41032, 41033, 41034, 41035, 41036, 41037, 41038, 41039, 41040, 41041, 41042, 41043, 41044, 41045, 41046, 41047, 41048, 41049, 41050, 41051, 41052, 41053, 41054, 41055, 41056, 41057, 41058, 41059, 41060, 41061, 41062, 41063, 41064, 41065, 41066, 41067, 41068, 41069, 41070, 41071, 41072, 41073, 41074, 41075, 41076, 41077, 41078, 41079, 41080, 41081, 41082, 41083, 41084, 41085, 41086, 41087, 41088, 41089, 41090, 41091, 41092, 41093, 41094, 41095, 41096, 41097, 41098, 41099, 41100, 41101, 41102, 41103, 41104, 41105, 41106, 41107, 41108, 41109, 41110, 41111, 41112, 41113, 41114, 41115, 41116, 41117, 41118, 41119, 41120, 41121, 41122, 41123, 41124, 41125, 41126, 41127, 41128, 41129, 41130, 41131, 41132, 41133, 41134, 41135, 41136, 41137, 41138, 41139, 41140, 41141, 41142, 41143, 41144, 41145, 41146, 41147, 41148, 41149, 41150, 41151, 41152, 41153, 41154, 41155, 41156, 41157, 41158, 41159, 41160, 41161, 41162, 41163, 41164, 41165, 41166, 41167, 41168, 41169, 41170, 41171, 41172, 41173, 41174, 41175, 41176, 41177, 41178, 41179, 41180, 41181, 41182, 41183, 41184, 41185, 41186, 41187, 41188, 41189, 41190, 41191, 41192, 41193, 41194, 41195, 41196, 41197, 41198, 41199, 41200, 41201, 41202, 41203, 41204, 41205, 41206, 41207, 41208, 41209, 41210, 41211, 41212, 41213, 41214, 41215, 41216, 41217, 41218, 41219, 41220, 41221, 41222, 41223, 41224, 41225, 41226, 41227, 41228, 41229, 41230, 41231, 41232, 41233, 41234, 41235, 41236, 41237, 41238, 41239, 41240, 41241, 41242, 41243, 41244, 41245, 41246, 41247, 41248, 41249, 41250, 41251, 41252, 41253, 41254, 41255, 41256, 41257, 41258, 41259, 41260, 41261, 41262, 41263, 41264, 41265, 41266, 41267, 41268, 41269, 41270, 41271, 41272, 41273, 41274, 41275, 41276, 41277, 41278, 41279, 41280, 41281, 41282, 41283, 41284, 41285, 41286, 41287, 41288, 41289, 41290, 41291, 41292, 41293, 41294, 41295, 41296, 41297, 41298, 41299, 41300, 41301, 41302, 41303, 41304, 41305, 41306, 41307, 41308, 41309, 41310, 41311, 41312, 41313, 41314, 41315, 41316, 41317, 41318, 41319, 41320, 41321, 41322, 41323, 41324, 41325, 41326, 41327, 41328, 41329, 41330, 41331, 41332, 41333, 41334, 41335, 41336, 41337, 41338, 41339, 41340, 41341, 41342, 41343, 41344, 41345, 41346, 41347, 41348, 41349, 41350, 41351, 41352, 41353, 41354, 41355, 41356, 41357, 41358, 41359, 41360, 41361, 41362, 41363, 41364, 41365, 41366, 41367, 41368, 41369, 41370, 41371, 41372, 41373, 41374, 41375, 41376, 41377, 41378, 41379, 41380, 41381, 41382, 41383, 41384, 41385, 41386, 41387, 41388, 41389, 41390, 41391, 41392, 41393, 41394, 41395, 41396, 41397, 41398, 41399, 41400, 41401, 41402, 41403, 41404, 41405, 41406, 41407, 41408, 41409, 41410, 41411, 41412, 41413, 41414, 41415, 41416, 41417, 41418, 41419, 41420, 41421, 41422, 41423, 41424, 41425, 41426, 41427, 41428, 41429, 41430, 41431, 41432, 41433, 41434, 41435, 41436, 41437, 41438, 41439, 41440, 41441, 41442, 41443, 41444, 41445, 41446, 41447, 41448, 41449, 41450, 41451, 41452, 41453, 41454, 41455, 41456, 41457, 41458, 41459, 41460, 41461, 41462, 41463, 41464, 41465, 41466, 41467, 41468, 41469, 41470, 41471, 41472, 41473, 41474, 41475, 41476, 41477, 41478, 41479, 41480, 41481, 41482, 41483, 41484, 41485, 41486, 41487, 41488, 41489, 41490, 41491, 41492, 41493, 41494, 41495, 41496, 41497, 41498, 41499, 41500, 41501, 41502, 41503, 41504, 41505, 41506, 41507, 41508, 41509, 41510, 41511, 41512, 41513, 41514, 41515, 41516, 41517, 41518, 41519, 41520, 41521, 41522, 41523, 41524, 41525, 41526, 41527, 41528, 41529, 41530, 41531, 41532, 41533, 41534, 41535, 41536, 41537, 41538, 41539, 41540, 41541, 41542, 41543, 41544, 41545, 41546, 41547, 41548, 41549, 41550, 41551, 41552, 41553, 41554, 41555, 41556, 41557, 41558, 41559, 41560, 41561, 41562, 41563, 41564, 41565, 41566, 41567, 41568, 41569, 41570, 41571, 41572, 41573, 41574, 41575, 41576, 41577, 41578, 41579, 41580, 41581, 41582, 41583, 41584, 41585, 41586, 41587, 41588, 41589, 41590, 41591, 41592, 41593, 41594, 41595, 41596, 41597, 41598, 41599, 41600, 41601, 41602, 41603, 41604, 41605, 41606, 41607, 41608, 41609, 41610, 41611, 41612, 41613, 41614, 41615, 41616, 41617, 41618, 41619, 41620, 41621, 41622, 41623, 41624, 41625, 41626, 41627, 41628, 41629, 41630, 41631, 41632, 41633, 41634, 41635, 41636, 41637, 41638, 41639, 41640, 41641, 41642, 41643, 41644, 41645, 41646, 41647, 41648, 41649, 41650, 41651, 41652, 41653, 41654, 41655, 41656, 41657, 41658, 41659, 41660, 41661, 41662, 41663, 41664, 41665, 41666, 41667, 41668, 41669, 41670, 41671, 41672, 41673, 41674, 41675, 41676, 41677, 41678, 41679, 41680, 41681, 41682, 41683, 41684, 41685, 41686, 41687, 41688, 41689, 41690, 41691, 41692, 41693, 41694, 41695, 41696, 41697, 41698, 41699, 41700, 41701, 41702, 41703, 41704, 41705, 41706, 41707, 41708, 41709, 41710, 41711, 41712, 41713, 41714, 41715, 41716, 41717, 41718, 41719, 41720, 41721, 41722, 41723, 41724, 41725, 41726, 41727, 41728, 41729, 41730, 41731, 41732, 41733, 41734, 41735, 41736, 41737, 41738, 41739, 41740, 41741, 41742, 41743, 41744, 41745, 41746, 41747, 41748, 41749, 41750, 41751, 41752, 41753, 41754, 41755, 41756, 41757, 41758, 41759, 41760, 41761, 41762, 41763, 41764, 41765, 41766, 41767, 41768, 41769, 41770, 41771, 41772, 41773, 41774, 41775, 41776, 41777, 41778, 41779, 41780, 41781, 41782, 41783, 41784, 41785, 41786, 41787, 41788, 41789, 41790, 41791, 41792, 41793, 41794, 41795, 41796, 41797, 41798, 41799, 41800, 41801, 41802, 41803, 41804, 41805, 41806, 41807, 41808, 41809, 41810, 41811, 41812, 41813, 41814, 41815, 41816, 41817, 41818, 41819, 41820, 41821, 41822, 41823, 41824, 41825, 41826, 41827, 41828, 41829, 41830, 41831, 41832, 41833, 41834, 41835, 41836, 41837, 41838, 41839, 41840, 41841, 41842, 41843, 41844, 41845, 41846, 41847, 41848, 41849, 41850, 41851, 41852, 41853, 41854, 41855, 41856, 41857, 41858, 41859, 41860, 41861, 41862, 41863, 41864, 41865, 41866, 41867, 41868, 41869, 41870, 41871, 41872, 41873, 41874, 41875, 41876, 41877, 41878, 41879, 41880, 41881, 41882, 41883, 41884, 41885, 41886, 41887, 41888, 41889, 41890, 41891, 41892, 41893, 41894, 41895, 41896, 41897, 41898, 41899, 41900, 41901, 41902, 41903, 41904, 41905, 41906, 41907, 41908, 41909, 41910, 41911, 41912, 41913, 41914, 41915, 41916, 41917, 41918, 41919, 41920, 41921, 41922, 41923, 41924, 41925, 41926, 41927, 41928, 41929, 41930, 41931, 41932, 41933, 41934, 41935, 41936, 41937, 41938, 41939, 41940, 41941, 41942, 41943, 41944, 41945, 41946, 41947, 41948, 41949, 41950, 41951, 41952, 41953, 41954, 41955, 41956, 41957, 41958, 41959, 41960, 41961, 41962, 41963, 41964, 41965, 41966, 41967, 41968, 41969, 41970, 41971, 41972, 41973, 41974, 41975, 41976, 41977, 41978, 41979, 41980, 41981, 41982, 41983, 41984, 41985, 41986, 41987, 41988, 41989, 41990, 41991, 41992, 41993, 41994, 41995, 41996, 41997, 41998, 41999, 42000, 42001, 42002, 42003, 42004, 42005, 42006, 42007, 42008, 42009, 42010, 42011, 42012, 42013, 42014, 42015, 42016, 42017, 42018, 42019, 42020, 42021, 42022, 42023, 42024, 42025, 42026, 42027, 42028, 42029, 42030, 42031, 42032, 42033, 42034, 42035, 42036, 42037, 42038, 42039, 42040, 42041, 42042, 42043, 42044, 42045, 42046, 42047, 42048, 42049, 42050, 42051, 42052, 42053, 42054, 42055, 42056, 42057, 42058, 42059, 42060, 42061, 42062, 42063, 42064, 42065, 42066, 42067, 42068, 42069, 42070, 42071, 42072, 42073, 42074, 42075, 42076, 42077, 42078, 42079, 42080, 42081, 42082, 42083, 42084, 42085, 42086, 42087, 42088, 42089, 42090, 42091, 42092, 42093, 42094, 42095, 42096, 42097, 42098, 42099, 42100, 42101, 42102, 42103, 42104, 42105, 42106, 42107, 42108, 42109, 42110, 42111, 42112, 42113, 42114, 42115, 42116, 42117, 42118, 42119, 42120, 42121, 42122, 42123, 42124, 42125, 42126, 42127, 42128, 42129, 42130, 42131, 42132, 42133, 42134, 42135, 42136, 42137, 42138, 42139, 42140, 42141, 42142, 42143, 42144, 42145, 42146, 42147, 42148, 42149, 42150, 42151, 42152, 42153, 42154, 42155, 42156, 42157, 42158, 42159, 42160, 42161, 42162, 42163, 42164, 42165, 42166, 42167, 42168, 42169, 42170, 42171, 42172, 42173, 42174, 42175, 42176, 42177, 42178, 42179, 42180, 42181, 42182, 42183, 42184, 42185, 42186, 42187, 42188, 42189, 42190, 42191, 42192, 42193, 42194, 42195, 42196, 42197, 42198, 42199, 42200, 42201, 42202, 42203, 42204, 42205, 42206, 42207, 42208, 42209, 42210, 42211, 42212, 42213, 42214, 42215, 42216, 42217, 42218, 42219, 42220, 42221, 42222, 42223, 42224, 42225, 42226, 42227, 42228, 42229, 42230, 42231, 42232, 42233, 42234, 42235, 42236, 42237, 42238, 42239, 42240, 42241, 42242, 42243, 42244, 42245, 42246, 42247, 42248, 42249, 42250, 42251, 42252, 42253, 42254, 42255, 42256, 42257, 42258, 42259, 42260, 42261, 42262, 42263, 42264, 42265, 42266, 42267, 42268, 42269, 42270, 42271, 42272, 42273, 42274, 42275, 42276, 42277, 42278, 42279, 42280, 42281, 42282, 42283, 42284, 42285, 42286, 42287, 42288, 42289, 42290, 42291, 42292, 42293, 42294, 42295, 42296, 42297, 42298, 42299, 42300, 42301, 42302, 42303, 42304, 42305, 42306, 42307, 42308, 42309, 42310, 42311, 42312, 42313, 42314, 42315, 42316, 42317, 42318, 42319, 42320, 42321, 42322, 42323, 42324, 42325, 42326, 42327, 42328, 42329, 42330, 42331, 42332, 42333, 42334, 42335, 42336, 42337, 42338, 42339, 42340, 42341, 42342, 42343, 42344, 42345, 42346, 42347, 42348, 42349, 42350, 42351, 42352, 42353, 42354, 42355, 42356, 42357, 42358, 42359, 42360, 42361, 42362, 42363, 42364, 42365, 42366, 42367, 42368, 42369, 42370, 42371, 42372, 42373, 42374, 42375, 42376, 42377, 42378, 42379, 42380, 42381, 42382, 42383, 42384, 42385, 42386, 42387, 42388, 42389, 42390, 42391, 42392, 42393, 42394, 42395, 42396, 42397, 42398, 42399, 42400, 42401, 42402, 42403, 42404, 42405, 42406, 42407, 42408, 42409, 42410, 42411, 42412, 42413, 42414, 42415, 42416, 42417, 42418, 42419, 42420, 42421, 42422, 42423, 42424, 42425, 42426, 42427, 42428, 42429, 42430, 42431, 42432, 42433, 42434, 42435, 42436, 42437, 42438, 42439, 42440, 42441, 42442, 42443, 42444, 42445, 42446, 42447, 42448, 42449, 42450, 42451, 42452, 42453, 42454, 42455, 42456, 42457, 42458, 42459, 42460, 42461, 42462, 42463, 42464, 42465, 42466, 42467, 42468, 42469, 42470, 42471, 42472, 42473, 42474, 42475, 42476, 42477, 42478, 42479, 42480, 42481, 42482, 42483, 42484, 42485, 42486, 42487, 42488, 42489, 42490, 42491, 42492, 42493, 42494, 42495, 42496, 42497, 42498, 42499, 42500, 42501, 42502, 42503, 42504, 42505, 42506, 42507, 42508, 42509, 42510, 42511, 42512, 42513, 42514, 42515, 42516, 42517, 42518, 42519, 42520, 42521, 42522, 42523, 42524, 42525, 42526, 42527, 42528, 42529, 42530, 42531, 42532, 42533, 42534, 42535, 42536, 42537, 42538, 42539, 42540, 42541, 42542, 42543, 42544, 42545, 42546, 42547, 42548, 42549, 42550, 42551, 42552, 42553, 42554, 42555, 42556, 42557, 42558, 42559, 42560, 42561, 42562, 42563, 42564, 42565, 42566, 42567, 42568, 42569, 42570, 42571, 42572, 42573, 42574, 42575, 42576, 42577, 42578, 42579, 42580, 42581, 42582, 42583, 42584, 42585, 42586, 42587, 42588, 42589, 42590, 42591, 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "processed_text = [item for item in processed_text if isinstance(item, str)]"
      ],
      "metadata": {
        "id": "NuCV204725dN"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# Pre-calculate embeddings\n",
        "embedding_model = SentenceTransformer(\"BAAI/bge-small-en\")\n",
        "#using processed\n",
        "embeddings = embedding_model.encode(processed_text, show_progress_bar=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# Pre-calculate embeddings\n",
        "embedding_model = SentenceTransformer(\"BAAI/bge-small-en\")\n",
        "embeddings = embedding_model.encode(processed_text, show_progress_bar=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "e89eeca7a92d4ae68852b1ab0d8b360c"
          ]
        },
        "id": "dlX7qvSh0DD_",
        "outputId": "9eeac6e0-f3fd-4461-fd13-c9b2d1a09c66"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from umap import UMAP\n",
        "from hdbscan import HDBSCAN\n",
        "\n",
        "#change min_cluster from 150 to 2\n",
        "umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', random_state=42)\n",
        "hdbscan_model = HDBSCAN(min_cluster_size=2, metric='euclidean', cluster_selection_method='eom', prediction_data=True)"
      ],
      "metadata": {
        "id": "lFPkIn5j0FIh"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pre-reduce embeddings for visualization purposes\n",
        "reduced_embeddings = UMAP(n_neighbors=15, n_components=2, min_dist=0.0, metric='cosine', random_state=42).fit_transform(embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "isjCWzjq0KKO",
        "outputId": "eff177e3-5265-4ce2-8b73-8d09d6955fef"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1943: UserWarning: n_jobs value -1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# System prompt describes information given to all conversations\n",
        "system_prompt = \"\"\"\n",
        "<s>[INST] <<SYS>>\n",
        "You are a helpful, respectful and honest assistant for labeling topics.\n",
        "<</SYS>>\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "vsHQZAeh4Bv1"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Example prompt demonstrating the output we are looking for\n",
        "example_prompt = \"\"\"\n",
        "I have a topic that contains the following documents:\n",
        "- Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food.\n",
        "- Meat, but especially beef, is the word food in terms of emissions.\n",
        "- Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one.\n",
        "\n",
        "The topic is described by the following keywords: 'meat, beef, eat, eating, emissions, steak, food, health, processed, chicken'.\n",
        "\n",
        "Based on the information about the topic above, please create a short label of this topic. Make sure you to only return the label and nothing more.\n",
        "\n",
        "[/INST] Environmental impacts of eating meat\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "BhKiUzHB4CBB"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Our main prompt with documents ([DOCUMENTS]) and keywords ([KEYWORDS]) tags\n",
        "main_prompt = \"\"\"\n",
        "[INST]\n",
        "I have a topic that contains the following documents:\n",
        "[DOCUMENTS]\n",
        "\n",
        "The topic is described by the following keywords: '[KEYWORDS]'.\n",
        "\n",
        "Based on the information about the topic above, please create a short label of this topic. Make sure you to only return the label and nothing more.\n",
        "[/INST]\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "jWgVK3Az4CNu"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = system_prompt + example_prompt + main_prompt"
      ],
      "metadata": {
        "id": "kPzi4twd39ZQ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from bertopic.representation import KeyBERTInspired, MaximalMarginalRelevance, TextGeneration\n",
        "\n",
        "# KeyBERT\n",
        "keybert = KeyBERTInspired()\n",
        "\n",
        "# MMR\n",
        "mmr = MaximalMarginalRelevance(diversity=0.3)\n",
        "\n",
        "# Text generation with Llama 2\n",
        "llama2 = TextGeneration(generator, prompt=prompt)\n",
        "\n",
        "# All representation models\n",
        "representation_model = {\n",
        "    \"KeyBERT\": keybert,\n",
        "    \"Llama2\": llama2,\n",
        "    \"MMR\": mmr,\n",
        "}"
      ],
      "metadata": {
        "id": "Il6rrvBe0QKS"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from bertopic import BERTopic\n",
        "\n",
        "topic_model = BERTopic(\n",
        "\n",
        "  # Sub-models\n",
        "  embedding_model=embedding_model,\n",
        "  umap_model=umap_model,\n",
        "  hdbscan_model=hdbscan_model,\n",
        "  representation_model=representation_model,\n",
        "\n",
        "  # Hyperparameters\n",
        "  top_n_words=10,\n",
        "  verbose=True\n",
        ")\n",
        "\n",
        "# Train model\n",
        "topics, probs = topic_model.fit_transform(processed_text, embeddings)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BsnSrUrd0TP6",
        "outputId": "81fec469-0a6d-44ee-8aee-9e1a449fa27f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-11-23 11:34:07,615 - BERTopic - Reduced dimensionality\n",
            "2023-11-23 11:34:07,623 - BERTopic - Clustered reduced embeddings\n",
            "100%|██████████| 4/4 [00:24<00:00,  6.02s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show topics\n",
        "topic_model.get_topic_info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "qLDqbHb10Yb7",
        "outputId": "a4d3172d-ba2a-4603-b282-44c150ab8414"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Topic  Count                     Name  \\\n",
              "0     -1      4  -1_the_zone_oh_dementia   \n",
              "1      0      6  0_he_his_think_dementia   \n",
              "2      1      6     1_people_in_are_love   \n",
              "3      2      2       2_you_and_can_your   \n",
              "\n",
              "                                      Representation  \\\n",
              "0  [the, zone, oh, dementia, or, for, love, irrit...   \n",
              "1  [he, his, think, dementia, it, was, the, over,...   \n",
              "2  [people, in, are, love, for, to, dementia, amp...   \n",
              "3  [you, and, can, your, to, home, had, note, no,...   \n",
              "\n",
              "                                             KeyBERT  \\\n",
              "0  [dementia, insomnia, living, irritability, tru...   \n",
              "1                       [dementia, , , , , , , , , ]   \n",
              "2  [dementia, memorycare, care, autism, love, , ,...   \n",
              "3              [dementia, brain, your, , , , , , , ]   \n",
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_info_dementia = topic_model.get_topic_info()"
      ],
      "metadata": {
        "id": "-LQFv-IvCq5w"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "topic_info_dementia.to_csv('topic_info_dementia.csv', index=False)"
      ],
      "metadata": {
        "id": "16p6ut5yCh16"
      },
      "execution_count": 36,
      "outputs": []
    }
  ]
}