Sentiment_Analysis_FineTuning_LLMs_with_LoRA_HF_Transformers.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AashiDutt/LLM-Projects/blob/main/Sentiment_Analysis_FineTuning_LLMs_with_LoRA_HF_Transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# -What is fine-tuning?\n",
        "Taking a pre-trained model and training at least one model parameter\n",
        "\n",
        "# -There are three ways to fine-tune a LLM\n",
        "\n",
        "1. Self-supervised learning - uses training corpus with base model\n",
        "2. supervised learning - given a dataset with input and labels with base model to fine tune upon\n",
        "3. Reinforcement learning (RLHF) - combines supervised fine tuning with training reward model and RL with PPO.\n",
        "\n",
        "# -Options for parameter training?\n",
        "\n",
        "1. Retrain all parameters (65M paramteres in base model(distil bert) used in this repo)\n",
        "2. Transfer learning - freeze almost all paramters and only finetune the head\n",
        "3. Parameter Efficient Fine-Tuning(PEFT) - used in this repo -> freeze all the weights and augment the model with additional model parameters that are trainable (small set)\n",
        "\n",
        "## -Method used in this Repo\n",
        "Parameter training method used in this repo \"***LoRA- low Rank Adaptation***\" using PEFT (fine tunes model by adding new trainable parameters like r or intrinsic rank used later)\n",
        "\n",
        "hidden layer before LoRA = h(x) = W0*x\n",
        "\n",
        "hidden layer after LoRA = h(x) = W0*x + delta(W)*x where W0 is frozen\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Note:Turn on GPU for faster training**"
      ],
      "metadata": {
        "id": "lGo1vWuJjwZy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets --quiet\n",
        "!pip install evaluate --quiet\n",
        "!pip install peft --quiet\n",
        "!pip install accelerate==0.25.0 --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DcNri0XmQEwk",
        "outputId": "dceaa650-3ae9-4ac6-f68b-b144c03d01ce"
      },
      "execution_count": 2,
      "outputs": [
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            "\u001b[?25hRequirement already satisfied: accelerate==0.25.0 in /usr/local/lib/python3.10/dist-packages (0.25.0)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate==0.25.0) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.25.0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.25.0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.25.0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.25.0) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate==0.25.0) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "95R_I2K0PMry"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "from datasets import load_dataset, DatasetDict, Dataset\n",
        "\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoConfig,\n",
        "    AutoModelForSequenceClassification,\n",
        "    DataCollatorWithPadding,\n",
        "    TrainingArguments,\n",
        "    Trainer\n",
        ")\n",
        "\n",
        "from peft import PeftModel, PeftConfig, get_peft_model, LoraConfig\n",
        "\n",
        "import evaluate\n",
        "import torch\n",
        "import numpy as np\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose a base model\n",
        "\n",
        "model_checkpoint = 'distilbert-base-uncased'\n",
        "\n",
        "# define label maps\n",
        "index2label = {0:'Negative', 1:'Positive'}\n",
        "label2index = {'Negative': 0, 'Positive': 1}\n",
        "\n",
        "# generate classification model from checkpoint\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels = 2, id2label = index2label,label2id = label2index )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244,
          "referenced_widgets": [
            "7d72391d79b2436f84be5af9b51698fa",
            "184b2eab56b543068416c835566efc8a",
            "1aba8d2f923440a78917379815ee1d6d",
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            "26301a74f101473ca4fa0ede0b2d2b55",
            "7aaf1ff2d0a2486cad2520fc8653b9bc",
            "80f52e0544e24e65bf874b0d3f3c9512",
            "c43f3d9f0c9f4fe6ba6a519d7dbe811b",
            "10e8ebfbf4364ab8b3fa4240f363ec5f",
            "d8f3057c9d9e4174af637b29703c8162",
            "a94860371d8e4b929ade8e11734674a1",
            "befdadd7a2944556bd8e44532aa96cee",
            "c33286463ef04108ae683c4af8b0ab71",
            "97d0b620245d4b328affade2661b38ae",
            "a767e480f66b487292daf96639d78ed1",
            "eddbb3f27c5845fda837d65ddb47876a"
          ]
        },
        "id": "qNZZ0UhlP6XZ",
        "outputId": "1b8052d7-7e84-439e-cd58-7f62f3941148"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: 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": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d72391d79b2436f84be5af9b51698fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7aaf1ff2d0a2486cad2520fc8653b9bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'pre_classifier.bias', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset\n",
        "dataset = load_dataset(\"shawhin/imdb-truncated\")\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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            "16f2abd697e84d6fa18a65dc5a3ab24d",
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            "6bfa6182d0884d8bafd04198152857e2",
            "1ab1df43aa684c8d9360c1c99bc0bdd2",
            "8d53e56f974749ad98adba25b71c3ae1",
            "71426a84789241f6b49012dd0ebe30fb",
            "570cdd55bb574cee9909d56115b7a40d",
            "c63746c142504a64a836df9883c71a29",
            "5351637dfb064d58a381275fcfe5877c",
            "c4d2237a45094b20aec9034c3a7657f5",
            "2a51db7837ba4166a487824d1aaa728f",
            "7bb86dc63a3d470fac533b6cfedbdac5",
            "03684237216f4dd1af47423ce25c2b87"
          ]
        },
        "id": "1gh9kdoVRq7L",
        "outputId": "df980819-4019-4cd3-84dd-e817a20e6f57"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/592 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d90cd14be31b463b900a80b2b5bfb321"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/836k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d0d1cbc5205429e95030b39b45d213c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/853k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c83d61b79db45cc80e25e499e2d4b7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97adee5697e44f1e9198be4358d7628a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6bfa6182d0884d8bafd04198152857e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'text'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['label', 'text'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# preprocess the data\n",
        "\n",
        "# Tokenize data\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space = True)\n",
        "\n",
        "def tokenize_fun(examples):\n",
        "  text = examples[\"text\"] # text comes from dataset\n",
        "\n",
        "  tokenizer.truncation_side = \"left\" # to keep size of sentences same\n",
        "  tokenized_inputs = tokenizer(\n",
        "      text,\n",
        "      return_tensors = \"np\", # return numpy tensors\n",
        "      truncation = True,\n",
        "      max_length = 512\n",
        "  )\n",
        "  return tokenized_inputs\n",
        "\n",
        "# add pad token\n",
        "if tokenizer.pad_token is None:\n",
        "  tokenizer.add_special_tokens({'pad_token':'[PAD]'})\n",
        "  model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "tokenized_dataset = dataset.map(tokenize_fun, batched = True)\n",
        "tokenized_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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        "id": "xZ0wWXNZR05n",
        "outputId": "de6534bd-4e15-4fd2-f1e5-a36f3499e70e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5616d276abe4729a63914ff7c804068"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f9f49ec690543dfb9b1806530b8b6aa"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f23e6e088ea64770aeccac002d1e7b9c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f8ae81758c24461b370f4d9c5e4d9ee"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb46f024a24e4c98b0eb1b426b07e851"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'text', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['label', 'text', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer = tokenizer) # helps to pad small sequences dynamically"
      ],
      "metadata": {
        "id": "IbGFSc6DTc0_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluation metrics\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(p): # evaluation function to pass into trainer later\n",
        "  predictions, labels = p\n",
        "  predictions = np.argmax(predictions, axis = 1)\n",
        "  return {\"accuracy\": accuracy.compute(predictions = predictions, references = labels)}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "efb64f2d33684dfe8d52950efb78ab20",
            "c41e53ca66e94679b5d1a3507b03278f",
            "d0b8bc5a24ce4e9cabc02443de71c620"
          ]
        },
        "id": "1y4TZWy0TqwH",
        "outputId": "264f3384-bd3a-426b-ec87-1bb7894e1a44"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "944075fe5fc5447ca7387166e06633c0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# untrained model performance\n",
        "\n",
        "text_list = [\"It was good.\", \"Not a fan, don't recommend\", \"Better than first one\"]\n",
        "print(\"Untrained model performance\")\n",
        "print(\"---------------------------\")\n",
        "for text in text_list:\n",
        "  inputs = tokenizer.encode(text, return_tensors = \"pt\")\n",
        "  logits = model(inputs).logits\n",
        "  predictions= torch.argmax(logits)\n",
        "\n",
        "  print(text + \" - \" + index2label[predictions.tolist()])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKf3bq_LUMQj",
        "outputId": "4f148d97-8996-43f8-eeba-60b507647c82"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Untrained model performance\n",
            "---------------------------\n",
            "It was good. - Negative\n",
            "Not a fan, don't recommend - Negative\n",
            "Better than first one - Negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fine tuning distil bert to do sentiment analysis\n",
        "\n",
        "peft_config = LoraConfig(task_type = \"SEQ_CLS\", # sequence_classfication\n",
        "                         r = 4, # rank of trainable weight matrix\n",
        "                         lora_alpha = 32, # learning rate\n",
        "                         lora_dropout = 0.01, # prob of dropout\n",
        "                         target_modules = ['q_lin'] # apply LoRA to query layer\n",
        "                         )"
      ],
      "metadata": {
        "id": "tn47HXf0RQq2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# finetuning\n",
        "model = get_peft_model(model,peft_config)\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XmQ5TI4YqNo",
        "outputId": "53080324-5bc9-41d8-bf1a-d10dd279f9bb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 628,994 || all params: 67,584,004 || trainable%: 0.9306847223789819\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = 1e-3\n",
        "batch_size = 4\n",
        "num_epochs = 10\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir = model_checkpoint + \"-lora-text-classification\",\n",
        "    learning_rate = lr,\n",
        "    per_device_train_batch_size = batch_size,\n",
        "    num_train_epochs = num_epochs,\n",
        "    weight_decay = 0.01,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    load_best_model_at_end = True,\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "JiVRmM63Y_Sa"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model = model,\n",
        "    args = training_args,\n",
        "    train_dataset = tokenized_dataset[\"train\"],\n",
        "    eval_dataset = tokenized_dataset[\"validation\"],\n",
        "    tokenizer = tokenizer,\n",
        "    data_collator = data_collator,\n",
        "    compute_metrics = compute_metrics\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "PTZyjenyZtQc",
        "outputId": "76c27a57-cc5a-46e5-e7e0-5964047da431"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2500' max='2500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2500/2500 07:55, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.413647</td>\n",
              "      <td>{'accuracy': 0.873}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.412000</td>\n",
              "      <td>0.506150</td>\n",
              "      <td>{'accuracy': 0.871}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.412000</td>\n",
              "      <td>0.500105</td>\n",
              "      <td>{'accuracy': 0.882}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.193300</td>\n",
              "      <td>0.660631</td>\n",
              "      <td>{'accuracy': 0.887}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.193300</td>\n",
              "      <td>0.905590</td>\n",
              "      <td>{'accuracy': 0.872}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.059600</td>\n",
              "      <td>0.935455</td>\n",
              "      <td>{'accuracy': 0.885}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.059600</td>\n",
              "      <td>0.974872</td>\n",
              "      <td>{'accuracy': 0.886}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.022400</td>\n",
              "      <td>1.013547</td>\n",
              "      <td>{'accuracy': 0.889}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.022400</td>\n",
              "      <td>1.087178</td>\n",
              "      <td>{'accuracy': 0.89}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.005700</td>\n",
              "      <td>1.109543</td>\n",
              "      <td>{'accuracy': 0.89}</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Trainer is attempting to log a value of \"{'accuracy': 0.873}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.871}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.882}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.887}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.872}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.885}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.886}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.889}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.89}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n",
            "Trainer is attempting to log a value of \"{'accuracy': 0.89}\" of type <class 'dict'> for key \"eval/accuracy\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2500, training_loss=0.13859124555587768, metrics={'train_runtime': 477.3194, 'train_samples_per_second': 20.95, 'train_steps_per_second': 5.238, 'total_flos': 1113026652407424.0, 'train_loss': 0.13859124555587768, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to('cuda') # for mac\n",
        "\n",
        "text_list = [\"It was good.\", \"Not a fan, don't recommend\", \"Better than first one\"]\n",
        "print(\"trained model performance\")\n",
        "print(\"---------------------------\")\n",
        "for text in text_list:\n",
        "  inputs = tokenizer.encode(text, return_tensors = \"pt\").to(\"cuda\")\n",
        "  logits = model(inputs).logits\n",
        "  predictions= torch.max(logits, 1).indices\n",
        "\n",
        "  print(text + \" - \" + index2label[predictions.tolist()[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cf0GTz7sbacA",
        "outputId": "522c0841-8b86-483a-8b64-23cc027acbbc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trained model performance\n",
            "---------------------------\n",
            "It was good. - Positive\n",
            "Not a fan, don't recommend - Negative\n",
            "Better than first one - Positive\n"
          ]
        }
      ]
    }
  ]
}