FinGPT_Inference_Llama2_13B_falcon_7B_for_Beginners.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinGPT/blob/master/FinGPT_Inference_Llama2_13B_falcon_7B_for_Beginners.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTV-l4nMGmtU",
        "outputId": "84a4ebc5-5f8d-4b12-d867-1d39bcd79f95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.41.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.32.0 peft==0.5.0\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": [
        "#for the `load_in_8bit=True` error\n",
        "!pip install protobuf transformers==4.32.0 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate"
      ],
      "metadata": {
        "id": "Mtui5C82IwR-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference with Single Task: Sentiment"
      ],
      "metadata": {
        "id": "uTyL2Ct70bmo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast\n",
        "from peft import PeftModel  # 0.5.0"
      ],
      "metadata": {
        "id": "UFzsvG4ZbXVO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Models\n",
        "base_model = \"NousResearch/Llama-2-13b-hf\"\n",
        "peft_model = \"FinGPT/fingpt-sentiment_llama2-13b_lora\"\n",
        "tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = \"cuda:0\", load_in_8bit = True,)\n",
        "model = PeftModel.from_pretrained(model, peft_model)\n",
        "model = model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542,
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        },
        "id": "ZMxTm7wFYrla",
        "outputId": "baaf81ce-d002-4694-f5f9-0ffc0279d1f8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)3b-hf/resolve/main/tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d91e6c58289b4a009ec0e7a7afe39749"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b11cbcc9fe1f4f2da4804ca859947050"
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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": "4b71a814dad54344a3613cd865973d02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
          ]
        },
        {
          "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": "ddf0a6e53dd140968504a5893b5ebdc8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)esolve/main/model.safetensors.index.json:   0%|          | 0.00/33.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1459f6a388c4e249825ed388a5bac61"
            }
          },
          "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": "2d82dc28178f4b189ffbf6a238ebc087"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00001-of-00003.safetensors:   0%|          | 0.00/9.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2ed4f5871a8342e7924ee2946ffa7e83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8969abbfd504d098bbe043dde93a8b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c263604b7774db2879d5bdb7a973c29"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b55185e5b62449faa453918e1b3caa7c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "(…)b-hf/resolve/main/generation_config.json:   0%|          | 0.00/175 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "906ee9d532214de380579a6e8bee08c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)3b_lora/resolve/main/adapter_config.json:   0%|          | 0.00/462 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36fd83bef2bb48b4aeb16b4e083e8f4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/19.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b853f40d8ab4dbab3bb7ba46f7eeb19"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make prompts\n",
        "prompt = [\n",
        "'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}\n",
        "Input: FINANCING OF ASPOCOMP 'S GROWTH Aspocomp is aggressively pursuing its growth strategy by increasingly focusing on technologically more demanding HDI printed circuit boards PCBs .\n",
        "Answer: ''',\n",
        "'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}\n",
        "Input: According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .\n",
        "Answer: ''',\n",
        "'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}\n",
        "Input: A tinyurl link takes users to a scamming site promising that users can earn thousands of dollars by becoming a Google ( NASDAQ : GOOG ) Cash advertiser .\n",
        "Answer: ''',\n",
        "]\n",
        "\n",
        "# Generate results\n",
        "tokens = tokenizer(prompt, return_tensors='pt', padding=True, max_length=512)\n",
        "res = model.generate(**tokens, max_length=512)\n",
        "res_sentences = [tokenizer.decode(i) for i in res]\n",
        "out_text = [o.split(\"Answer: \")[1] for o in res_sentences]\n",
        "\n",
        "# show results\n",
        "for sentiment in out_text:\n",
        "    print(sentiment)\n",
        "\n",
        "# Output:\n",
        "# positive\n",
        "# neutral\n",
        "# negative\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tTwD4E9Yroi",
        "outputId": "c1fe1451-1ff7-42a8-8f9c-336c7b537dbd"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1411: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1529: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " positive</s>\n",
            " neutral</s>\n",
            " negative</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference with Multi-Task\n",
        "* Please Restart runtime, the memory is not enough to run two tasks"
      ],
      "metadata": {
        "id": "07-KagX6YtM8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast\n",
        "from peft import PeftModel  # 0.5.0"
      ],
      "metadata": {
        "id": "QJN4tCASbZxu"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import datasets\n",
        "\n",
        "\n",
        "template_dict = {\n",
        "    'default': 'Instruction: {instruction}\\nInput: {input}\\nAnswer: '\n",
        "}\n",
        "\n",
        "lora_module_dict = {\n",
        "    'chatglm2': ['query_key_value'],\n",
        "    'falcon': ['query_key_value'],\n",
        "    'bloom': ['query_key_value'],\n",
        "    'internlm': ['q_proj', 'k_proj', 'v_proj'],\n",
        "    'llama2': ['q_proj', 'k_proj', 'v_proj'],\n",
        "    'qwen': [\"c_attn\"],\n",
        "    'mpt': ['Wqkv'],\n",
        "}\n",
        "\n",
        "\n",
        "def get_prompt(template, instruction, input):\n",
        "\n",
        "    if instruction:\n",
        "        return template_dict[template].format(instruction=instruction, input=input)\n",
        "    else:\n",
        "        return input\n",
        "\n",
        "\n",
        "def test_mapping(args, feature):\n",
        "\n",
        "    prompt = get_prompt(\n",
        "        args.instruct_template,\n",
        "        feature['instruction'],\n",
        "        feature['input']\n",
        "    )\n",
        "    return {\n",
        "        \"prompt\": prompt,\n",
        "    }\n",
        "\n",
        "\n",
        "def tokenize(args, tokenizer, feature):\n",
        "\n",
        "    prompt = get_prompt(\n",
        "        args.instruct_template,\n",
        "        feature['instruction'],\n",
        "        feature['input']\n",
        "    )\n",
        "    prompt_ids = tokenizer(\n",
        "        prompt, padding=False,\n",
        "        max_length=args.max_length, truncation=True\n",
        "    )['input_ids']\n",
        "    target_ids = tokenizer(\n",
        "        feature['output'].strip(), padding=False,\n",
        "        max_length=args.max_length, truncation=True,\n",
        "        add_special_tokens=False\n",
        "    )['input_ids']\n",
        "\n",
        "    input_ids = prompt_ids + target_ids\n",
        "    exceed_max_length = len(input_ids) >= args.max_length\n",
        "\n",
        "    # Add EOS Token\n",
        "    if input_ids[-1] != tokenizer.eos_token_id and not exceed_max_length:\n",
        "        input_ids.append(tokenizer.eos_token_id)\n",
        "\n",
        "    label_ids = [tokenizer.pad_token_id] * len(prompt_ids) + input_ids[len(prompt_ids):]\n",
        "\n",
        "    return {\n",
        "        \"input_ids\": input_ids,\n",
        "        \"labels\": label_ids,\n",
        "        \"exceed_max_length\": exceed_max_length\n",
        "    }\n",
        "\n",
        "\n",
        "def parse_model_name(name, from_remote=False):\n",
        "\n",
        "    if name == 'chatglm2':\n",
        "        return 'THUDM/chatglm2-6b' if from_remote else 'base_models/chatglm2-6b'\n",
        "    elif name == 'llama2':\n",
        "        return 'meta-llama/Llama-2-7b-hf' if from_remote else 'base_models/Llama-2-7b-hf'\n",
        "        # return 'NousResearch/Llama-2-7b-hf' if from_remote else 'base_models/Llama-2-7b-hf-nous'\n",
        "    elif name == 'falcon':\n",
        "        return 'tiiuae/falcon-7b' if from_remote else 'base_models/falcon-7b'\n",
        "    elif name == 'internlm':\n",
        "        return 'internlm/internlm-7b' if from_remote else 'base_models/internlm-7b'\n",
        "    elif name == 'qwen':\n",
        "        return 'Qwen/Qwen-7B' if from_remote else 'base_models/Qwen-7B'\n",
        "    elif name == 'mpt':\n",
        "        return 'cekal/mpt-7b-peft-compatible' if from_remote else 'base_models/mpt-7b-peft-compatible'\n",
        "        # return 'mosaicml/mpt-7b' if from_remote else 'base_models/mpt-7b'\n",
        "    elif name == 'bloom':\n",
        "        return 'bigscience/bloom-7b1' if from_remote else 'base_models/bloom-7b1'\n",
        "    else:\n",
        "        raise ValueError(f\"Undefined base model {name}\")\n",
        "\n",
        "\n",
        "def load_dataset(names, from_remote=False):\n",
        "    dataset_names = [d for d in names.split(',')]\n",
        "    dataset_list = []\n",
        "    for name in dataset_names:\n",
        "        rep = 1\n",
        "        if not os.path.exists(name):\n",
        "            rep = int(name.split('*')[1]) if '*' in name else 1\n",
        "            name = ('FinGPT/fingpt-' if from_remote else 'data/fingpt-') + name.split('*')[0]\n",
        "        tmp_dataset = datasets.load_from_disk(name)\n",
        "        if 'test' not in tmp_dataset:\n",
        "            tmp_dataset = tmp_dataset.train_test_split(0.2, shuffle=True, seed=42)\n",
        "\n",
        "        dataset_list.extend([tmp_dataset] * rep)\n",
        "    return dataset_list\n",
        "\n"
      ],
      "metadata": {
        "id": "Wpf57NyVbuVs"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model(base_model, peft_model, from_remote=True):\n",
        "\n",
        "    model_name = parse_model_name(base_model, from_remote)\n",
        "\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        model_name, trust_remote_code=True,\n",
        "        device_map=\"auto\",\n",
        "    )\n",
        "    model.model_parallel = True\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "\n",
        "    tokenizer.padding_side = \"left\"\n",
        "    if base_model == 'qwen':\n",
        "        tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids('<|endoftext|>')\n",
        "        tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids('<|extra_0|>')\n",
        "    if not tokenizer.pad_token or tokenizer.pad_token_id == tokenizer.eos_token_id:\n",
        "        tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "        model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "    model = PeftModel.from_pretrained(model, peft_model)\n",
        "    model = model.eval()\n",
        "    return model, tokenizer\n",
        "\n",
        "\n",
        "def test_demo(model, tokenizer):\n",
        "\n",
        "    for task_name, input, instruction in zip(demo_tasks, demo_inputs, demo_instructions):\n",
        "        prompt = 'Instruction: {instruction}\\nInput: {input}\\nAnswer: '.format(\n",
        "            input=input,\n",
        "            instruction=instruction\n",
        "        )\n",
        "        inputs = tokenizer(\n",
        "            prompt, return_tensors='pt',\n",
        "            padding=True, max_length=512,\n",
        "            return_token_type_ids=False\n",
        "        )\n",
        "        inputs = {key: value.to(model.device) for key, value in inputs.items()}\n",
        "        res = model.generate(\n",
        "            **inputs, max_length=512, do_sample=False,\n",
        "            eos_token_id=tokenizer.eos_token_id\n",
        "        )\n",
        "        output = tokenizer.decode(res[0], skip_special_tokens=True)\n",
        "        print(f\"\\n==== {task_name} ====\\n\")\n",
        "        print(output)"
      ],
      "metadata": {
        "id": "yGj4oCaDZkjA"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "FROM_REMOTE=True\n",
        "\n",
        "base_model = 'falcon'\n",
        "peft_model = 'FinGPT/fingpt-mt_falcon-7b_lora' if FROM_REMOTE else 'finetuned_models/MT-falcon-linear_202309210126'\n",
        "\n",
        "model, tokenizer = load_model(base_model, peft_model, FROM_REMOTE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
            "1efeecbb93cb44a7925566249f32e53b",
            "767a01ad674d4328bc003889459340d2",
            "1f4e64b66dc442af89384c83cb213d97",
            "4b67b4b929064817835a2608edb8c924",
            "a5ae601634b74be38713ba0fc76562db",
            "d93428f90f9c45bb97f3634de33c9c49",
            "d6e455ed742f4b098801cf052edb3e5f",
            "17fbf4edd72a4deb80c8849f8b42a9c8",
            "ac0d9717279c45ef9d1cbafa95a1e3d2",
            "6529c8dcfe4047d7909affadae0b0575",
            "d1f917fd527442edbe68b3be9658532c"
          ]
        },
        "id": "24-Nh82zaWuC",
        "outputId": "1a035faf-a3e9-4bfa-acdd-e6729d334c9b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.tiiuae.falcon-7b.898df1396f35e447d5fe44e0a3ccaaaa69f30d36.configuration_falcon:\n",
            "WARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model without the trust_remote_code=True argument.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1efeecbb93cb44a7925566249f32e53b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using pad_token, but it is not set yet.\n",
            "You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embeding dimension will be 65025. This might induce some performance reduction as *Tensor Cores* will not be available. For more details  about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "demo_tasks = [\n",
        "    'Financial Sentiment Analysis',\n",
        "    'Financial Relation Extraction',\n",
        "    'Financial Headline Classification',\n",
        "    'Financial Named Entity Recognition',\n",
        "]\n",
        "demo_inputs = [\n",
        "    \"Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano\",\n",
        "    \"Apple Inc Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.\",\n",
        "    'gold trades in red in early trade; eyes near-term range at rs 28,300-28,600',\n",
        "    'This LOAN AND SECURITY AGREEMENT dated January 27 , 1999 , between SILICON VALLEY BANK (\" Bank \"), a California - chartered bank with its principal place of business at 3003 Tasman Drive , Santa Clara , California 95054 with a loan production office located at 40 William St ., Ste .',\n",
        "]\n",
        "demo_instructions = [\n",
        "    'What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.',\n",
        "    'Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be \"relation1: word1, word2; relation2: word3, word4\". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.',\n",
        "    'Does the news headline talk about price going up? Please choose an answer from {Yes/No}.',\n",
        "    'Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.',\n",
        "]"
      ],
      "metadata": {
        "id": "kTsoodGCHwDi"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_demo(model, tokenizer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ckn6fnNFL7Si",
        "outputId": "3b16c5c8-51a9-40ff-bb54-73badac97b8b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==== Financial Sentiment Analysis ====\n",
            "\n",
            "Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano\n",
            "Answer: positive\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==== Financial Relation Extraction ====\n",
            "\n",
            "Instruction: Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be \"relation1: word1, word2; relation2: word3, word4\". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.\n",
            "Input: Apple Inc Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.\n",
            "Answer: employer: Steve Jobs, Apple Inc\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==== Financial Headline Classification ====\n",
            "\n",
            "Instruction: Does the news headline talk about price going up? Please choose an answer from {Yes/No}.\n",
            "Input: gold trades in red in early trade; eyes near-term range at rs 28,300-28,600\n",
            "Answer: No\n",
            "\n",
            "==== Financial Named Entity Recognition ====\n",
            "\n",
            "Instruction: Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.\n",
            "Input: This LOAN AND SECURITY AGREEMENT dated January 27, 1999, between SILICON VALLEY BANK (\" Bank \"), a California - chartered bank with its principal place of business at 3003 Tasman Drive, Santa Clara, California 95054 with a loan production office located at 40 William St., Ste.\n",
            "Answer: SILICON VALLEY BANK is an organization, Bank is an organization, California is a location, bank is an organization, 3003 Tasman Drive is a location, Santa Clara is a location, California is a location, 40 William St is a location.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hsO0Wg9y0h_E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}