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      "execution_count": null,
      "metadata": {
        "id": "9pCmyyIg2jnJ"
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      "source": [
        "!pip install transformers==4.30.2 peft==0.4.0\n",
        "!pip install sentencepiece\n",
        "!pip install accelerate\n",
        "!pip install torch\n",
        "!pip install peft\n",
        "!pip install datasets\n",
        "!pip install bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/AI4Finance-Foundation/FinNLP.git\n",
        "\n",
        "import sys\n",
        "sys.path.append('/content/FinNLP/')"
      ],
      "metadata": {
        "id": "5Pgw0C3f2mod",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bee160b-b9be-4b10-ed95-3d674758b97c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'FinNLP'...\n",
            "remote: Enumerating objects: 1310, done.\u001b[K\n",
            "remote: Counting objects: 100% (369/369), done.\u001b[K\n",
            "remote: Compressing objects: 100% (166/166), done.\u001b[K\n",
            "remote: Total 1310 (delta 214), reused 296 (delta 169), pack-reused 941\u001b[K\n",
            "Receiving objects: 100% (1310/1310), 4.21 MiB | 8.19 MiB/s, done.\n",
            "Resolving deltas: 100% (587/587), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "from finnlp.benchmarks.fpb import test_fpb\n",
        "from finnlp.benchmarks.fiqa import test_fiqa , add_instructions\n",
        "from finnlp.benchmarks.tfns import test_tfns\n",
        "from finnlp.benchmarks.nwgi import test_nwgi\n"
      ],
      "metadata": {
        "id": "7lnOBCzH2mpz"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Load Model\n",
        "base_model = \"THUDM/chatglm2-6b\"\n",
        "peft_model = \"oliverwang15/FinGPT_v31_ChatGLM2_Sentiment_Instruction_LoRA_FT\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(base_model, trust_remote_code=True, load_in_8bit = True, device_map = \"auto\")\n",
        "model = PeftModel.from_pretrained(model, peft_model)\n",
        "model = model.eval()"
      ],
      "metadata": {
        "id": "_uz6iTwt2mr9"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "batch_size = 8"
      ],
      "metadata": {
        "id": "Xltv4y3z2mt2"
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      "execution_count": 5,
      "outputs": []
    },
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      "source": [
        "# FPB\n",
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        "outputId": "c69ba1c2-d849-4262-a4ec-a6b9c98fd6e0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.04k [00:00<?, ?B/s]"
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          "metadata": {}
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          "data": {
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              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.88k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/682k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/4846 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f11395fce804fd59c4c7a9a3f614d1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: L&T has also made a commitment to redeem the remaining shares by the end of 2011 .\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 1212. Batchsize: 8. Total steps: 152\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 152/152 [01:30<00:00,  1.67it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.8531353135313532. F1 macro: 0.8355311905054061. F1 micro: 0.8531353135313532. F1 weighted (BloombergGPT): 0.8518766219402919. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "# FiQA\n",
        "res = test_fiqa(model, tokenizer, prompt_fun = add_instructions, batch_size = batch_size)"
      ],
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        "id": "Os6gavsw2mz2",
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        "outputId": "01ecaeb9-9b81-414b-e6f1-5985f5e4a007"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
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              "Downloading data:   0%|          | 0.00/161k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/16.7k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/25.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "302719501d3a43ea904f065193207da4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e0c790d9c7646b391aa79cae51e4938"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d43e78445954e749e80cbcca98d2531"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb540a98879f46baac86f4135c7812f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: This $BBBY stock options trade would have more than doubled your money https://t.co/Oa0loiRIJL via @TheStreet\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 275. Batchsize: 8. Total steps: 35\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 35/35 [00:18<00:00,  1.93it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.8327272727272728. F1 macro: 0.7457078313253013. F1 micro: 0.8327272727272728. F1 weighted (BloombergGPT): 0.8475613362541076. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TFNS\n",
        "res = test_tfns(model, tokenizer, batch_size = batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448,
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        "id": "iFP1GnsB5JhA",
        "outputId": "2e433c65-4ea6-4600-a452-518452ab7bab"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ddbe0e2796e467fbe7b1e87f4b3a5d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/859k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97548f1ae93840578d2d1896d77adb83"
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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,
              "model_id": "f9ef99c9811247f39312510b2d6258ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19cf177dea0f4f3a898ba80e6f5ef3ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79d64661b96f4d8da315c169b7ad9136"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0524d15d64a43cba4d01bf05d0f8234"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: $ALLY - Ally Financial pulls outlook https://t.co/G9Zdi1boy5\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 2388. Batchsize: 8. Total steps: 299\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 299/299 [02:33<00:00,  1.95it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.876465661641541. F1 macro: 0.8422150651552788. F1 micro: 0.876465661641541. F1 weighted (BloombergGPT): 0.8760116165894634. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NWGI\n",
        "res = test_nwgi(model, tokenizer, batch_size = batch_size)"
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          "height": 448,
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        "id": "DyDAnAtA5Jjo",
        "outputId": "b35e95f6-86a5-4bbe-e763-7618e4f08346"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/682 [00:00<?, ?B/s]"
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              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/10.8M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4212a40d31834a4697c61814988dd142"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8dcf6e3ed78148c2a2275408f7b4db27"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/16184 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86ff2e1ae471413cb4d96d930a13b71e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/4047 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a25107e13f3453999f6a371c4e2f3e3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: In the latest trading session, Adobe Systems (ADBE) closed at $535.98, marking a +0.31% move from the previous day.\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 4047. Batchsize: 8. Total steps: 506\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 506/506 [05:31<00:00,  1.53it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.6375092661230541. F1 macro: 0.6449413175330677. F1 micro: 0.6375092661230541. F1 weighted (BloombergGPT): 0.6368355381269861. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NBkF36c26GM-"
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      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
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      "metadata": {
        "id": "s5hq6C9E6GRH"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Qey_cDxA5JoC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rfAI1k2I5Jp6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}