phi_2_classification_fine_tune.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mit1280/fined-tuning/blob/main/phi_2_classification_fine_tune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPy8ZcoKlQ1U",
        "outputId": "7b3b1a8b-a294-4ce9-9724-c2a7efb26200"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m279.7/279.7 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q -U accelerate peft bitsandbytes transformers einops datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "y6J4FoKFPbKw"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import bitsandbytes as bnb\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import transformers\n",
        "from datasets import Dataset\n",
        "from peft import LoraConfig, PeftConfig\n",
        "from transformers import (AutoModelForCausalLM,\n",
        "                          AutoTokenizer,\n",
        "                          BitsAndBytesConfig,\n",
        "                          TrainingArguments,\n",
        "                          pipeline,\n",
        "                          TrainingArguments,\n",
        "                          Trainer)\n",
        "from sklearn.metrics import (accuracy_score,\n",
        "                             classification_report,\n",
        "                             confusion_matrix)\n",
        "from sklearn.model_selection import train_test_split\n",
        "from peft import (\n",
        "    LoraConfig,\n",
        "    prepare_model_for_kbit_training,\n",
        "    get_peft_model\n",
        ")\n",
        "\n",
        "from datasets import load_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load train data"
      ],
      "metadata": {
        "id": "zmsXUbEzIIe9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208,
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            "ece027bd97a548ffa6e4966c9a45ad17",
            "3d171f3df081437282b82b47cf768604"
          ]
        },
        "id": "GN78oIV-HT3_",
        "outputId": "542d7f4b-8f1d-4177-c1c8-ef768232634e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/392k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2730fbac833e4f5a912c1449227753ff"
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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": "d38621df8eaa4139a3f8b6f42d14a02f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset_name = 'financial_phrasebank'\n",
        "# load dataset\n",
        "dataset = load_dataset(dataset_name, \"sentences_50agree\", split = [\"train\"])[0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyj74W9XSuUb",
        "outputId": "45f9958d-32c3-4e02-de6e-f0d3e6b307f3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['sentence', 'label'],\n",
              "    num_rows: 4846\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqbbKdPfLoqx"
      },
      "source": [
        "## Set label ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AANYNpnoB4lp",
        "outputId": "29c09b65-2ca4-4e86-cb77-479b030b9b51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['negative', 'neutral', 'positive']\n"
          ]
        }
      ],
      "source": [
        "labels = dataset.features['label'].names\n",
        "print(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQuWiYt9B5uf",
        "outputId": "cb499ba0-957e-4193-f225-200a2f8a3c40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{0: 'negative', 1: 'neutral', 2: 'positive'}\n"
          ]
        }
      ],
      "source": [
        "id2label = {idx:label for idx, label in enumerate(labels)}\n",
        "label2id = {label:idx for idx, label in enumerate(labels)}\n",
        "print(id2label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fca4H2TTKt4M"
      },
      "source": [
        "## Load model and tokenizer"
      ]
    },
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        "outputId": "bd6dd576-0ee9-4b2c-a984-f02af46625fc"
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      "outputs": [
        {
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            "text/plain": [
              "config.json:   0%|          | 0.00/863 [00:00<?, ?B/s]"
            ]
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            "text/plain": [
              "configuration_phi.py:   0%|          | 0.00/9.26k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
            "- configuration_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "data": {
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              "modeling_phi.py:   0%|          | 0.00/62.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
            "- modeling_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "data": {
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              "model_id": "71bf824baf0a4e179efdb89082b8c9ae",
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            },
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/35.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3963fa455cfd4b00be8dc26aef72eb70",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b690ce74603f40b48d1ec0bd16878afe",
              "version_major": 2,
              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "024fe1b1fc0846f89160567128b952ee",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c881802e8f740dcaa3cf97fb4e25c84",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "724d04c5ceab480ba9bca69481f15125",
              "version_major": 2,
              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 10,485,760 || all params: 2,790,169,600 || trainable%: 0.37581084676716425\n"
          ]
        }
      ],
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"microsoft/phi-2\",\n",
        "    device_map=\"auto\",\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config\n",
        ")\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\"dense\",\n",
        "    \"q_proj\",\n",
        "    \"k_proj\",\n",
        "    \"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, lora_config)\n",
        "\n",
        "model.print_trainable_parameters()"
      ]
    },
    {
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        "id": "_kp5DpfpFJUy",
        "outputId": "619c1468-a79d-4a12-8cc6-72c0194575c7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f903dfba64c41c7a7ce43c26974e849",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.34k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "dd242ac279344d4293e0e5d23f8c4443",
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              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "15179512888f4c4daef25f9e1b709735",
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            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "95ed3562f7144903ba7a1a83aa1913be",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "55b55371fcd24be1aecfe41038ab08d2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "41df4d661e5e44b497628d9a27d8a85e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-2\", padding=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQhAt5w1PgLI"
      },
      "source": [
        "# Tokenize data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc1q7efRTvl0",
        "outputId": "e7ce684f-9662-46f2-e40e-41d8359beb1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['sentence', 'label'],\n",
              "    num_rows: 4846\n",
              "})"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6BC-QfR-Br52"
      },
      "outputs": [],
      "source": [
        "data_df = dataset.to_pandas()\n",
        "\n",
        "conversation_list = []\n",
        "\n",
        "# Iterate over the DataFrame rows\n",
        "for index, row in data_df.iterrows():\n",
        "    # Construct the conversation list dynamically\n",
        "    conversation = [\n",
        "        {\"from\": \"human\", \"value\": f\"Text: {row['sentence']}\"},\n",
        "        {\"from\": \"phi\", \"value\": \"I've read this text.\"},\n",
        "        {\"from\": \"human\", \"value\": \"Please determine the sentiment of the given text and choose from the options: Positive, Negative, Neutral, or Cannot be determined.\"},\n",
        "        {\"from\": \"phi\", \"value\": f\"Sentiment of given text is {row['label']}\"},\n",
        "    ]\n",
        "    # Append the conversation to the list\n",
        "    conversation_list.append(conversation)\n",
        "\n",
        "# Add the conversation list as a new column in the DataFrame\n",
        "data_df['conversation'] = conversation_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "dLHI2LiGDH3p",
        "outputId": "57830f37-1a49-4336-ed76-6de1f93a2eb9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format (without BOS/EOS tokens!). If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"{% for message in messages %}{{'<|im_start|>' + message['from'] + '\\n' + message['value'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}\""
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "template = tokenizer.default_chat_template\n",
        "template = template.replace(\"role\", \"from\").replace(\"content\", \"value\")  # Change the system token\n",
        "tokenizer.chat_template = template  # Set the new template\n",
        "tokenizer.chat_template"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z3Q4PERHDBhp"
      },
      "outputs": [],
      "source": [
        "data_df['chat_conversations'] = data_df['conversation'].apply(lambda x: tokenizer.apply_chat_template(x, tokenize=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RVTIiCt2Brxx"
      },
      "outputs": [],
      "source": [
        "new_dataset = Dataset.from_pandas(data_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OuSgAn0jCxl9"
      },
      "outputs": [],
      "source": [
        "def tokenize(sample):\n",
        "    tokenized_text =  tokenizer(sample[\"chat_conversations\"], padding=True, truncation=True, max_length=512)\n",
        "    return tokenized_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "2fdc13abdd534ce895bfe0c0133c2008",
            "97303654b715430998c4f9ed57d7b3f3",
            "eb821a74944b4dcbb3a41fc69717f8a7",
            "57e475e5f1d04d28bb083f9e81301cb0",
            "bf8e442639724a1d8946dbaf0da097a5",
            "cae2567a69264ac4842ab0073d142aa4",
            "b828149d50d44d33acf35689d8781cd2",
            "8326fdeca16949d1ac4f0f5bd9838dd6",
            "83bc6ba2dbb7448588ad4c7050b12a27",
            "1d358b7377ca4e7fb5b4503fc2f74c66",
            "6c69197540f94d3fa1983737590d8a21"
          ]
        },
        "id": "ZnpNKmlcCypJ",
        "outputId": "98a7e8f5-f57b-4117-d00d-307509a00d34"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2fdc13abdd534ce895bfe0c0133c2008",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Tokenizing data:   0%|          | 0/4846 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'attention_mask'],\n",
              "    num_rows: 4846\n",
              "})"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenized_data = new_dataset.map(tokenize, batched=True, desc=\"Tokenizing data\", remove_columns=new_dataset.column_names)\n",
        "\n",
        "tokenized_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "fZsX12OCDn_H",
        "outputId": "3295c6e0-172c-4d7e-a696-3a112fb4d6d1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"<|im_start|>human\\nText: According to Gran, the company has no plans to move all production to Russia, although that is where the company is growing.<|im_end|>\\n<|im_start|>phi\\nI've read this text.<|im_end|>\\n<|im_start|>human\\nPlease determine the sentiment of the given text and choose from the options: Positive, Negative, Neutral, or Cannot be determined.<|im_end|>\\n<|im_start|>phi\\nSentiment of given text is 1<|im_end|>\\n<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>\""
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.decode(tokenized_data[0]['input_ids'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "cZTh3FceGoZS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aIk1hBQKD4F6"
      },
      "outputs": [],
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=\"phi-2-classification-sentiment\",\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=1,\n",
        "        learning_rate=2e-4,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=100,\n",
        "        max_steps=10000,\n",
        "        num_train_epochs=5\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zCwbUI2QD4px"
      },
      "outputs": [],
      "source": [
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6jkoI8N4D-3s",
        "outputId": "b1fe7e4f-182c-46b0-d7f3-c6322ff34e41"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='9390' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 9390/10000 2:29:32 < 09:42, 1.05 it/s, Epoch 7.75/9]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.921700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.674000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.684500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.687200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.672500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.675400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.658900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.658800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.663800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.640500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.597300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.625500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.620800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.602400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.630200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.627100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.584400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.593000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.600100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.608200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.611600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.620100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.603100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.594400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.574600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2600</td>\n",
              "      <td>0.575000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>0.546300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>0.586100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2900</td>\n",
              "      <td>0.564600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.553600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3100</td>\n",
              "      <td>0.587000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3200</td>\n",
              "      <td>0.604100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3300</td>\n",
              "      <td>0.568600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3400</td>\n",
              "      <td>0.559000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.561300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3600</td>\n",
              "      <td>0.559100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3700</td>\n",
              "      <td>0.535900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3800</td>\n",
              "      <td>0.538800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3900</td>\n",
              "      <td>0.540600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.537300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4100</td>\n",
              "      <td>0.538900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4200</td>\n",
              "      <td>0.535100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4300</td>\n",
              "      <td>0.518300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4400</td>\n",
              "      <td>0.536600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.527000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4600</td>\n",
              "      <td>0.532100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4700</td>\n",
              "      <td>0.526200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4800</td>\n",
              "      <td>0.522800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4900</td>\n",
              "      <td>0.545500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.491900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5100</td>\n",
              "      <td>0.499700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5200</td>\n",
              "      <td>0.501300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5300</td>\n",
              "      <td>0.496200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5400</td>\n",
              "      <td>0.496400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.500500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5600</td>\n",
              "      <td>0.490100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5700</td>\n",
              "      <td>0.501000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5800</td>\n",
              "      <td>0.513700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5900</td>\n",
              "      <td>0.496700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.503100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6100</td>\n",
              "      <td>0.485200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6200</td>\n",
              "      <td>0.449500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6300</td>\n",
              "      <td>0.469900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6400</td>\n",
              "      <td>0.460400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.462900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6600</td>\n",
              "      <td>0.489200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6700</td>\n",
              "      <td>0.479700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6800</td>\n",
              "      <td>0.475500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6900</td>\n",
              "      <td>0.484800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.486000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7100</td>\n",
              "      <td>0.467500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7200</td>\n",
              "      <td>0.475100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7300</td>\n",
              "      <td>0.467100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7400</td>\n",
              "      <td>0.458900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.449800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7600</td>\n",
              "      <td>0.445100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7700</td>\n",
              "      <td>0.462100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7800</td>\n",
              "      <td>0.457800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7900</td>\n",
              "      <td>0.446800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.456200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8100</td>\n",
              "      <td>0.450600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8200</td>\n",
              "      <td>0.454700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8300</td>\n",
              "      <td>0.463200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8400</td>\n",
              "      <td>0.458600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.448400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8600</td>\n",
              "      <td>0.433200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8700</td>\n",
              "      <td>0.450500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8800</td>\n",
              "      <td>0.445600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8900</td>\n",
              "      <td>0.435600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.463000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9100</td>\n",
              "      <td>0.443000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9200</td>\n",
              "      <td>0.442700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9300</td>\n",
              "      <td>0.439800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
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        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10000' max='10000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10000/10000 2:39:17, Epoch 8/9]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.921700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.674000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.684500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.687200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.672500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.675400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.658900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.658800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.663800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.640500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.597300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.625500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.620800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.602400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.630200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.627100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.584400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.593000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.600100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.608200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.611600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.620100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.603100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.594400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.574600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2600</td>\n",
              "      <td>0.575000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>0.546300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>0.586100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2900</td>\n",
              "      <td>0.564600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.553600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3100</td>\n",
              "      <td>0.587000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3200</td>\n",
              "      <td>0.604100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3300</td>\n",
              "      <td>0.568600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3400</td>\n",
              "      <td>0.559000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.561300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3600</td>\n",
              "      <td>0.559100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3700</td>\n",
              "      <td>0.535900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3800</td>\n",
              "      <td>0.538800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3900</td>\n",
              "      <td>0.540600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.537300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4100</td>\n",
              "      <td>0.538900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4200</td>\n",
              "      <td>0.535100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4300</td>\n",
              "      <td>0.518300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4400</td>\n",
              "      <td>0.536600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.527000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4600</td>\n",
              "      <td>0.532100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4700</td>\n",
              "      <td>0.526200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4800</td>\n",
              "      <td>0.522800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4900</td>\n",
              "      <td>0.545500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.491900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5100</td>\n",
              "      <td>0.499700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5200</td>\n",
              "      <td>0.501300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5300</td>\n",
              "      <td>0.496200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5400</td>\n",
              "      <td>0.496400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.500500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5600</td>\n",
              "      <td>0.490100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5700</td>\n",
              "      <td>0.501000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5800</td>\n",
              "      <td>0.513700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5900</td>\n",
              "      <td>0.496700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.503100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6100</td>\n",
              "      <td>0.485200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6200</td>\n",
              "      <td>0.449500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6300</td>\n",
              "      <td>0.469900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6400</td>\n",
              "      <td>0.460400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.462900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6600</td>\n",
              "      <td>0.489200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6700</td>\n",
              "      <td>0.479700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6800</td>\n",
              "      <td>0.475500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6900</td>\n",
              "      <td>0.484800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.486000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7100</td>\n",
              "      <td>0.467500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7200</td>\n",
              "      <td>0.475100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7300</td>\n",
              "      <td>0.467100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7400</td>\n",
              "      <td>0.458900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>0.449800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7600</td>\n",
              "      <td>0.445100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7700</td>\n",
              "      <td>0.462100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7800</td>\n",
              "      <td>0.457800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7900</td>\n",
              "      <td>0.446800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>0.456200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8100</td>\n",
              "      <td>0.450600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8200</td>\n",
              "      <td>0.454700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8300</td>\n",
              "      <td>0.463200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8400</td>\n",
              "      <td>0.458600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>0.448400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8600</td>\n",
              "      <td>0.433200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8700</td>\n",
              "      <td>0.450500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8800</td>\n",
              "      <td>0.445600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8900</td>\n",
              "      <td>0.435600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>0.463000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9100</td>\n",
              "      <td>0.443000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9200</td>\n",
              "      <td>0.442700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9300</td>\n",
              "      <td>0.439800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9400</td>\n",
              "      <td>0.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>0.433800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9600</td>\n",
              "      <td>0.447600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9700</td>\n",
              "      <td>0.443700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9800</td>\n",
              "      <td>0.442400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9900</td>\n",
              "      <td>0.438700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.439500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10000, training_loss=0.529747783279419, metrics={'train_runtime': 9559.7962, 'train_samples_per_second': 4.184, 'train_steps_per_second': 1.046, 'total_flos': 1.4279513657856e+17, 'train_loss': 0.529747783279419, 'epoch': 8.25})"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_data,\n",
        "    args=training_arguments,\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "9daab71015c24df3bb482bda855ac009",
            "2d3981cf71ee4f8db727e5b7e743aeb5",
            "ac6a03843f1547f3bfde97051abd7c61",
            "00cad96e0dc64c28a14786c50bcaa163",
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            "2399e46839884ae181d97bad365edde4",
            "1ee40b5087cc41a28a7a7360b2f2b7be",
            "44a93bbdb669419f9211da9dbb187db1",
            "e8de619ba31d41d7971d4c840bc7454c",
            "c51e1ae792aa42e197b3388561f8d3bc"
          ]
        },
        "id": "1_wQE9dQy0K4",
        "outputId": "4f519ec7-54e9-4585-bbe9-997403070c14"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9daab71015c24df3bb482bda855ac009",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i0_Us30v99NN"
      },
      "outputs": [],
      "source": [
        "trainer.save_model(\"./classification\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "773555cf780f4bb3be0b67f41d528312",
            "652a474cc87e4b7cad8ef27712df894f",
            "36f5ed2a39844fc6a3cd5454ae61608c",
            "b43afa9f77f04a78adb9f9e0dfe3b2a1",
            "3ce85937260f4707ab163401720d3d7a",
            "f8bb9717ea464c0781c36e6386ad8292",
            "a3a086e2adcd4ea1a3a4bbfd6f11d4af",
            "69e7a9e2ecd649ff8a5bc9bb78cfe738",
            "10a59abe9b3b4225b96f7a4909777d14",
            "0b3a008f67b0437cbed48ab17a7b51e7",
            "978c95005eb24bb8a2e4cf9204e97bcd"
          ]
        },
        "id": "CLLcHnfA-PNG",
        "outputId": "de4e0fff-a936-46ff-98f3-4e3a6b103626"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "773555cf780f4bb3be0b67f41d528312",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "\n",
        "temp_model = PeftModel.from_pretrained(base_model, \"./classification\")\n",
        "temp_model = temp_model.merge_and_unload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DodJrTgnAMzz",
        "outputId": "634db0d2-1388-4d7b-c85a-5dfd10f5337c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('phi-2-classification-sentiment-merged/tokenizer_config.json',\n",
              " 'phi-2-classification-sentiment-merged/special_tokens_map.json',\n",
              " 'phi-2-classification-sentiment-merged/vocab.json',\n",
              " 'phi-2-classification-sentiment-merged/merges.txt',\n",
              " 'phi-2-classification-sentiment-merged/added_tokens.json',\n",
              " 'phi-2-classification-sentiment-merged/tokenizer.json')"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_model.save_pretrained(\"phi-2-classification-sentiment-merged\")\n",
        "tokenizer.save_pretrained(\"phi-2-classification-sentiment-merged\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "372b990aca4649d8b3c27cb02258a660",
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            "1c42e4eac303496eb460acc462555310",
            "b18ee16d749f4d1f9c665ebad0c0cebc",
            "c45b6d7868d34cf38c9c505f18608ff4",
            "ca2a05f809eb496b8ec0acb47d8d1522",
            "87d3b2e89c4f44559e443802b8e52e3f",
            "8dc998cc9feb420c9c58003d2ca5799e"
          ]
        },
        "id": "0GQoGPxU_IRr",
        "outputId": "986a5377-82e0-4c11-dfec-fa83ebbc3f65"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2fc6d4e552f14938b745b3f49121a791",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "da3d361ecdce439e86c5e1bc653c9bc6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5be57a0193334586a5109acbef8962d2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/564M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8dc998cc9feb420c9c58003d2ca5799e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Mit1208/phi-2-classification-sentiment-merged/commit/49d441b73cff032c609240acb18f0778de64c319', commit_message='Upload tokenizer', commit_description='', oid='49d441b73cff032c609240acb18f0778de64c319', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_model.push_to_hub(\"phi-2-classification-sentiment-merged\", use_temp_dir=False)\n",
        "tokenizer.push_to_hub(\"phi-2-classification-sentiment-merged\", use_temp_dir=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "JxKKE42xGitA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "d68jdFNyHEGM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208,
          "referenced_widgets": [
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            "12bd514a485b4c7f8a9abab4cdbde412",
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            "b97fd6ef433547faa4bf8bc004922c48",
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            "5ee129b2cc1542058068610b106e4211",
            "f163105156fb4b09bd1d423871c98fe6",
            "9475b144fb644e93bb1748ea47b4b2fe",
            "17b075e3a3794a00bad5baf38c09a499",
            "1af14adf0ff34348a24a915dd8846cf6",
            "af1c79d98eed4ab5a2801d919d3a4ace",
            "5d0e8783f66b477a8b667272b08ed3af"
          ]
        },
        "outputId": "3a6186e8-372b-4981-c511-363633d41a3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/171k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9850dbdfb93f4d4cb4360ea75c89509b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/2264 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d0bbaa1f5234b7db1722c02c6b8d0a9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset_name = 'financial_phrasebank'\n",
        "# load dataset\n",
        "dataset = load_dataset(dataset_name, \"sentences_allagree\", split = [\"train\"])[0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(dataset)"
      ],
      "metadata": {
        "id": "bVqRPMOJfuwL"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tc69BKj0f3i_",
        "outputId": "637321da-9c24-40e2-a55a-e5e6db30234c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1391\n",
              "2     570\n",
              "0     303\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_records = 100\n",
        "# Create a new dataset with an equal number of records for each label\n",
        "data_df = pd.concat([df[df['label'] == label].sample(min_records) for label in df['label'].unique()])\n",
        "\n",
        "# Reset the index of the balanced dataset\n",
        "data_df.reset_index(drop=True, inplace=True)"
      ],
      "metadata": {
        "id": "C9F221nZIb6L"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conversation_list = []\n",
        "\n",
        "# Iterate over the DataFrame rows\n",
        "for index, row in data_df.iterrows():\n",
        "    # Construct the conversation list dynamically\n",
        "    conversation = [\n",
        "        {\"from\": \"human\", \"value\": f\"Text: {row['sentence']}\"},\n",
        "        {\"from\": \"phi\", \"value\": \"I've read this text.\"},\n",
        "        {\"from\": \"human\", \"value\": \"Please determine the sentiment of the given text and choose from the options: Positive, Negative, Neutral, or Cannot be determined.\"}\n",
        "    ]\n",
        "    # Append the conversation to the list\n",
        "    conversation_list.append(conversation)\n",
        "\n",
        "# Add the conversation list as a new column in the DataFrame\n",
        "data_df['conversation'] = conversation_list"
      ],
      "metadata": {
        "id": "vd1FN2GJHV0S"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model directly\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Mit1208/phi-2-classification-sentiment-merged\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"Mit1208/phi-2-classification-sentiment-merged\", device_map=\"auto\", trust_remote_code=True).eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622,
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        },
        "id": "lDru3jpDKqXH",
        "outputId": "294cd424-51b4-4fbb-b173-f5b84e591f99"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.61k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
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        },
        {
          "output_type": "display_data",
          "data": {
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          "data": {
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        {
          "output_type": "display_data",
          "data": {
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            ],
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        {
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          "data": {
            "text/plain": [
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            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/897 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
            "- configuration_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_phi.py:   0%|          | 0.00/62.7k [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
            "- modeling_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/35.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b50b62547ca46dc915e57212f24b18b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "374e7f0a774040219670727bd54ddb52"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/564M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09bc948650f84ccbac263f66c8e3809a"
            }
          },
          "metadata": {}
        },
        {
          "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": "720b01632f1a4042b024f4e38ed5111e"
            }
          },
          "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": "2e9f4c28b79745d794e86f3051df07de"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# {0: 'negative', 1: 'neutral', 2: 'positive'}\n",
        "def evaluate(y_true, y_pred):\n",
        "    labels = ['positive', 'neutral', 'negative']\n",
        "    mapping = {'positive': 2, 'neutral': 1, 'none':1, 'negative': 0}\n",
        "    def map_func(x):\n",
        "        return mapping.get(x, 1)\n",
        "\n",
        "    y_true = np.vectorize(map_func)(y_true)\n",
        "    y_pred = np.vectorize(map_func)(y_pred)\n",
        "\n",
        "    # Calculate accuracy\n",
        "    accuracy = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
        "    print(f'Accuracy: {accuracy:.3f}')\n",
        "\n",
        "    # Generate accuracy report\n",
        "    unique_labels = set(y_true)  # Get unique labels\n",
        "\n",
        "    for label in unique_labels:\n",
        "        label_indices = [i for i in range(len(y_true))\n",
        "                         if y_true[i] == label]\n",
        "        label_y_true = [y_true[i] for i in label_indices]\n",
        "        label_y_pred = [y_pred[i] for i in label_indices]\n",
        "        accuracy = accuracy_score(label_y_true, label_y_pred)\n",
        "        print(f'Accuracy for label {label}: {accuracy:.3f}')\n",
        "\n",
        "    # Generate classification report\n",
        "    class_report = classification_report(y_true=y_true, y_pred=y_pred)\n",
        "    print('\\nClassification Report:')\n",
        "    print(class_report)\n",
        "\n",
        "    # Generate confusion matrix\n",
        "    conf_matrix = confusion_matrix(y_true=y_true, y_pred=y_pred, labels=[0, 1, 2])\n",
        "    print('\\nConfusion Matrix:')\n",
        "    print(conf_matrix)"
      ],
      "metadata": {
        "id": "FNwxXSVGKZZd"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import StoppingCriteria\n",
        "class EosListStoppingCriteria(StoppingCriteria):\n",
        "    def __init__(self, eos_sequence = tokenizer.encode(\"<|im_end|>\")):\n",
        "        self.eos_sequence = eos_sequence\n",
        "\n",
        "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:\n",
        "        last_ids = input_ids[:,-len(self.eos_sequence):].tolist()\n",
        "        return self.eos_sequence in last_ids"
      ],
      "metadata": {
        "id": "GaYXIlMHKmjn"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(X_test, model, tokenizer):\n",
        "    y_pred = []\n",
        "    for i in tqdm(range(len(X_test))):\n",
        "        inf_conv = X_test.loc[i]['conversation'][:3]\n",
        "        inference_text = tokenizer.apply_chat_template(inf_conv, tokenize=False) + '<|im_start|>phi:\\n'\n",
        "        inputs = tokenizer(inference_text, return_tensors=\"pt\", return_attention_mask=False, truncation=True).to('cuda')\n",
        "        outputs = model.generate(inputs[\"input_ids\"], max_new_tokens=1024, pad_token_id= tokenizer.eos_token_id,\n",
        "            stopping_criteria = [EosListStoppingCriteria()])\n",
        "\n",
        "        result = tokenizer.batch_decode(outputs)[0]\n",
        "        answer = result.split(\"<|im_start|>phi:\")[-1].replace(\"<|im_end|>\", \"\").lower()\n",
        "        # print(answer)\n",
        "        if \"2\" in answer:\n",
        "            # print(\"in\")\n",
        "            y_pred.append(\"positive\")\n",
        "        elif \"0\" in answer:\n",
        "            y_pred.append(\"negative\")\n",
        "        elif \"1\" in answer:\n",
        "            # print(\"n\")\n",
        "            y_pred.append(\"neutral\")\n",
        "        else:\n",
        "            y_pred.append(\"none\")\n",
        "    return y_pred\n"
      ],
      "metadata": {
        "id": "ycLHeHSWKcpP"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.reset_index(inplace=True, drop=True)"
      ],
      "metadata": {
        "id": "SCOehWmiNCPL"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df.loc[0]['conversation'][:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnx7I4g8NDVx",
        "outputId": "c476dd14-111c-4e2a-8855-96994a36cd5f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'from': 'human',\n",
              "  'value': \"Text: there may not be a critic alive who harbors as much affection for shlock monster movies as i do .\\ni delighted in the sneaky - smart entertainment of ron underwood 's big - underground - worm yarn tremors ; i even giggled at last year 's critically - savaged big - underwater - snake yarn anaconda .\\nsomething about these films causes me to lower my inhibitions and return to the saturday afternoons of my youth , spent in the company of ghidrah , the creature from the black lagoon and the blob .\\ndeep rising , a big - undersea - serpent yarn , does n't quite pass the test .\\nsure enough , all the modern monster movie ingredients are in place : a conspicuously multi - ethnic / multi - national collection of bait .\\n.. excuse me , characters ; an isolated location , here a derelict cruise ship in the south china sea ; some comic relief ; a few cgi - enhanced gross - outs ; and at least one big explosion .\\nthere are too - cheesy - to - be - accidental elements , like a sleazy shipping magnate ( anthony heald ) who also appears to have a doctorate in marine biology , or a slinky international jewel thief ( famke janssen ) whose white cotton tank top hides a heart of gold .\\nas it happens , deep rising is noteworthy primarily for the mechanical manner in which it spits out all those ingredients .\\na terrorist crew , led by squinty - eyed mercenary hanover ( wes studi ) and piloted by squinty - eyed boat captain finnegan ( treat williams ) , shows up to loot the cruise ship ; the sea monsters show up to eat the mercenary crew ; a few survivors make it to the closing credits .\\nand up go the lights .\\nit 's hard to work up much enthusiasm for this sort of joyless film - making , especially when a monster moview should make you laugh every time it makes you scream .\\nhere , the laughs are provided almost entirely by kevin\\nj. o'connor , generally amusing as the crew 's fraidy - cat mechanic .\\nwriter / director stephen sommers seems most concerned with creating a tone of action - horror menace -- something over - populated with gore - drenched skeletons , something where the gunfire and special effects are taken a bit too seriously .\\ndeep rising is missing that one unmistakable cue that we 're expected to have a ridiculous good time , not hide our eyes .\\ncase it point , comparing deep rising to its recent cousin anaconda .\\nin deep rising , one of the creature 's victims is regurgitated back into view , partially digested and still alive .\\nhe shrieks in horror at his freakish appearance and pain , in a moment a bit too disturbing to be laughable .\\nin anaconda , we also see a regurgitated victim , partially digested and still alive .\\nhe looks at another character .\\n.. and winks .\\nmake no mistake , deep rising has anaconda beat all to heck when it comes to technical proficiency and pacing .\\nit 's also gloomy , uninspired and not nearly enough fun .\\ni do n't ask much of my monster movies , but i do ask that they act like monster movies .\\nyou do n't have to show me a fantastically impressive , massive beast with tentacles a - flailing .\\njust show me the massive beast burping , and i 'll figure you get the point .\"},\n",
              " {'from': 'phi', 'value': \"I've read this text.\"},\n",
              " {'from': 'human',\n",
              "  'value': 'Please determine the sentiment of the given text and choose from the options: Positive, Negative, Neutral, or Cannot be determined.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = predict(data_df, model, tokenizer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6VcZZP4Ikpj",
        "outputId": "11605256-8dbe-4faa-a2c6-1c807e643451"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 300/300 [04:51<00:00,  1.03it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['prd'] = y_pred"
      ],
      "metadata": {
        "id": "ylI2y5dPRQAF"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['prd'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CkWWWIBmZwey",
        "outputId": "549ea64c-4181-4b1a-9620-5e8144038a06"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "neutral     100\n",
              "positive    100\n",
              "negative    100\n",
              "Name: prd, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_id2label = {0: 'negative', 2: 'positive', 1:'neutral'}"
      ],
      "metadata": {
        "id": "Rqgy6acBRzcT"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['label'] = data_df['label'].map(dataset_id2label)"
      ],
      "metadata": {
        "id": "ZW4lp36IRgj-"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inf_conv = data_df.loc[1]['conversation']\n",
        "\n",
        "inference_text = tokenizer.apply_chat_template(inf_conv, tokenize=False) + '<|im_start|>phi:\\n'\n",
        "inputs = tokenizer(inference_text, return_tensors=\"pt\", return_attention_mask=False).to('cuda')\n",
        "outputs = model.generate(**inputs, max_length=512, pad_token_id= tokenizer.eos_token_id,\n",
        "            stopping_criteria = [EosListStoppingCriteria()])\n",
        "\n",
        "text = tokenizer.batch_decode(outputs)[0]\n",
        "\n",
        "print(text.split(\"The correct option is\")[-1].replace(\"<|im_end|>\", \"\").replace(\".\", \"\"), id2label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INQ8KAM8SGu6",
        "outputId": "0cd653f4-2e1e-4a52-94a2-43bdfc3f395f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|im_start|>human\n",
            "Text: unflinchingly bleak and desperate \n",
            "<|im_start|>phi\n",
            "I've read this text\n",
            "<|im_start|>human\n",
            "Please determine the sentiment of the given text and choose from the options: Positive, Negative, Neutral, or Cannot be determined\n",
            "<|im_start|>phi:\n",
            "Sentiment of given text is 1 {0: 'negative', 1: 'neutral', 2: 'positive'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df[data_df['label']==data_df['prd']].shape[0]/len(data_df), len(data_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fx2XYxDBcP4O",
        "outputId": "45f34a77-d90d-447e-fdf0-25cc4d7c9106"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1.0, 300)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_report = classification_report(data_df['label'], data_df['prd'])\n",
        "print(class_report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5vmwiLkSEDV",
        "outputId": "2ec167da-e53e-4957-eeaa-9f5f85f57b30"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       1.00      1.00      1.00       100\n",
            "     neutral       1.00      1.00      1.00       100\n",
            "    positive       1.00      1.00      1.00       100\n",
            "\n",
            "    accuracy                           1.00       300\n",
            "   macro avg       1.00      1.00      1.00       300\n",
            "weighted avg       1.00      1.00      1.00       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vItNcWJYPVlw"
      },
      "source": [
        "# Reference\n",
        "\n",
        "\n",
        "1.   https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Perceiver/Fine_tune_Perceiver_for_text_classification.ipynb\n",
        "2.   https://medium.com/@lukas.hauzenberger/multilabel-classification-using-mistral-7b-on-a-single-gpu-with-quantization-and-lora-8f848b5237f3\n",
        "\n"
      ]
    }
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