05_finetune/06_fine_tune_dialogsum_flan_t5_huggingface.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Fine-tune the Instructor-Model for Dialogue Summarization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Tested on ml.m5.2xlarge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name='1'></a>\n",
    "## Set up Kernel and Required Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install --disable-pip-version-check \\\n",
    "    torch==2.0.1 \\\n",
    "    transformers==4.34.1 \\\n",
    "    datasets==2.12.0 \\\n",
    "    accelerate==0.23.0 \\\n",
    "    evaluate==0.4.0 \\\n",
    "    py7zr==0.20.4 \\\n",
    "    sentencepiece==0.1.99 \\\n",
    "    rouge_score==0.1.2 \\\n",
    "    loralib==0.1.1 \\\n",
    "    peft==0.4.0 \\\n",
    "    trl==0.7.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_checkpoint='google/flan-t5-base'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this directory is created in the previous notebook\n",
    "local_data_processed_path = './data-summarization-processed/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, GenerationConfig\n",
    "from datasets import load_dataset\n",
    "import datasets\n",
    "import torch\n",
    "import time\n",
    "import evaluate\n",
    "import numpy as np\n",
    "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load HuggingFace Model\n",
    "\n",
    "We can load the pre-trained Flan-T5 model directly from HuggingFace. Notice that we will be using the [base version](https://huggingface.co/google/flan-t5-base) of flan. This model version has ~247 million model parameters which makes it small compared to other LLMs. For higher quality results, we recommend looking into the larger versions of this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Model Parameters: 247577856\n"
     ]
    }
   ],
   "source": [
    "params = sum(p.numel() for p in model.parameters())\n",
    "print(f'Total Number of Model Parameters: {params}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Processed Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load Dataset\n",
    "\n",
    "The DialogSum dataset which we processed already can loaded directly from our local directory. There are ~15k examples of dialogue in this dataset with associated human summarizations of these datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset parquet (/root/.cache/huggingface/datasets/parquet/data-summarization-processed-38e385bef3c7b73c/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d68de47bc7e4396a9a48de43acce75d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['input_ids', 'labels'],\n",
       "        num_rows: 13014\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['input_ids', 'labels'],\n",
       "        num_rows: 723\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['input_ids', 'labels'],\n",
       "        num_rows: 723\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_dataset = load_dataset(\n",
    "    local_data_processed_path,\n",
    "    data_files={'train': 'train/*.parquet', 'test': 'test/*.parquet', 'validation': 'validation/*.parquet'}\n",
    ").with_format(\"torch\")\n",
    "tokenized_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Test the Model with Zero-Shot Prompts BEFORE Fine-Tuning\n",
    "\n",
    "In the example below, we highlight how the summarization capability of the model is lacking compared to the baseline summary provided in the dataset. You can see that the model struggles to summarize the dialogue compared to the baseline summary, but it does pull out some important information from the text which indicates the model can be fine tuned to the task at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt:\n",
      "--------------------------\n",
      "Summarize the following conversation. \n",
      "#Person1#: Hello. My name is John Sandals, and I've got a reservation. \n",
      "#Person2#: May I see some identification, sir, please? \n",
      "#Person1#: Sure. Here you are. \n",
      "#Person2#: Thank you so much. Have you got a credit card, Mr. Sandals? \n",
      "#Person1#: I sure do. How about American Express? \n",
      "#Person2#: Unfortunately, at the present time we take only MasterCard or VISA. \n",
      "#Person1#: No American Express? Okay, here's my VISA. \n",
      "#Person2#: Thank you, sir. You'll be in room 507, nonsmoking, with a queen-size bed. Do you approve, sir? \n",
      "#Person1#: Yeah, that'll be fine. \n",
      "#Person2#: That's great. This is your key, sir. If you need anything at all, anytime, just dial zero. Summary: \n",
      "--------------------------\n",
      "\n",
      "Original Model Response: John Sandals has a reservation for a room at the Venetian Hotel in Las Vegas.\n",
      "Baseline Summary : John Sandals has got a reservation. #Person1# asks for his identification and credit card and helps his check-in.\n"
     ]
    }
   ],
   "source": [
    "idx = 2\n",
    "diag = tokenizer.decode(tokenized_dataset['test'][idx]['input_ids'], skip_special_tokens=True)\n",
    "model_input = tokenizer(diag, return_tensors=\"pt\").input_ids\n",
    "summary = tokenizer.decode(tokenized_dataset['test'][idx]['labels'], skip_special_tokens=True)\n",
    "\n",
    "original_outputs = model.to('cpu').generate(model_input, GenerationConfig(max_new_tokens=200))\n",
    "original_text_output = tokenizer.decode(original_outputs[0], skip_special_tokens=True)\n",
    "\n",
    "diag_print = diag.replace(' #',' \\n#')\n",
    "print(f\"Prompt:\\n--------------------------\\n{diag_print}\\n--------------------------\")\n",
    "print(f'\\nOriginal Model Response: {original_text_output}')\n",
    "print(f'Baseline Summary : {summary}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fine-tune the instructor model\n",
    "\n",
    "Now that the dataset is preprocessed, we can utilize the built-in HuggingFace `Trainer` class to fine tune our model to the task at hand. Please note that training this full model takes a few hours on a GPU, so for the sake of time, a checkpoint for a model which has been trained on 10 epochs without downsampling has been provided. If you have time to experiment on fully training the model yourself, please see the inline comments for how to change up the code. If you are looking to train on a GPU machine, we have used a `ml.g5.xlarge` instance for the checkpoint provided as a place to start."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/13014 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/723 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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      "text/plain": [
       "Filter:   0%|          | 0/723 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for the sake of time in the lab, we will subsample our dataset\n",
    "# if you want to take the time to train a model fully, feel free to alter this subsampling to create a larger dataset\n",
    "sample_tokenized_dataset = tokenized_dataset.filter(lambda example, indice: indice % 100 == 0, with_indices=True)\n",
    "\n",
    "output_dir = f'./diag-summary-training-{str(int(time.time()))}'\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=output_dir,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    learning_rate=1e-5,\n",
    "    num_train_epochs=1,\n",
    "    # num_train_epochs=10, # Use a higher number of epochs when you are not in the lab and have more time to experiment\n",
    "    per_device_train_batch_size=4,\n",
    "    per_device_eval_batch_size=4,\n",
    "    weight_decay=0.01,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=sample_tokenized_dataset['train'],\n",
    "    eval_dataset=sample_tokenized_dataset['validation']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='33' max='33' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [33/33 06:45, Epoch 1/1]\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>35.780022</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=33, training_loss=38.12218498461174, metrics={'train_runtime': 418.7129, 'train_samples_per_second': 0.313, 'train_steps_per_second': 0.079, 'total_flos': 89703213170688.0, 'train_loss': 38.12218498461174, 'epoch': 1.0})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Trained Model and Original Model\n",
    "\n",
    "Once the model has finished training, we will load both the original model from HuggingFace and the fine-tuned model to do some qualitative and quantitative comparisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/generation_config.json to flan-dialogue-summary-checkpoint/generation_config.json\n",
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/trainer_state.json to flan-dialogue-summary-checkpoint/trainer_state.json\n",
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/rng_state.pth to flan-dialogue-summary-checkpoint/rng_state.pth\n",
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/scheduler.pt to flan-dialogue-summary-checkpoint/scheduler.pt\n",
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/config.json to flan-dialogue-summary-checkpoint/config.json\n",
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/training_args.bin to flan-dialogue-summary-checkpoint/training_args.bin\n",
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/pytorch_model.bin to flan-dialogue-summary-checkpoint/pytorch_model.bin\n",
      "download: s3://dsoaws/models/flan-dialogue-summary-checkpoint/optimizer.pt to flan-dialogue-summary-checkpoint/optimizer.pt\n"
     ]
    }
   ],
   "source": [
    "!aws s3 cp --recursive s3://dsoaws/models/flan-dialogue-summary-checkpoint/ ./flan-dialogue-summary-checkpoint/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# if you have trained your own model and want to check it out compared to ours, change the line of code\n",
    "# below to contain your checkpoint directory\n",
    "\n",
    "supervised_fine_tuned_model_path = \"./flan-dialogue-summary-checkpoint\"\n",
    "# supervised_fine_tuned_model_path = f\"./{output_dir}/<put-your-checkpoint-dir-here>\"\n",
    "\n",
    "tuned_model = AutoModelForSeq2SeqLM.from_pretrained(supervised_fine_tuned_model_path)\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'supervised_fine_tuned_model_path' (str)\n"
     ]
    }
   ],
   "source": [
    "%store supervised_fine_tuned_model_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Qualitative Results with Zero Shot Inference AFTER Fine-Tuning\n",
    "\n",
    "As with many GenAI applications, a qualitative approach where you ask yourself the question \"is my model behaving the way it is supposed to?\" is usually a good starting point. In the example below (the same one we started this notebook with), you can see how the fine-tuned model is able to create a reasonable summary of the dialogue compared to the original inability to understand what is being asked of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prompt:\n",
      "--------------------------\n",
      "Summarize the following conversation. \n",
      "#Person1#: Hello. My name is John Sandals, and I've got a reservation. \n",
      "#Person2#: May I see some identification, sir, please? \n",
      "#Person1#: Sure. Here you are. \n",
      "#Person2#: Thank you so much. Have you got a credit card, Mr. Sandals? \n",
      "#Person1#: I sure do. How about American Express? \n",
      "#Person2#: Unfortunately, at the present time we take only MasterCard or VISA. \n",
      "#Person1#: No American Express? Okay, here's my VISA. \n",
      "#Person2#: Thank you, sir. You'll be in room 507, nonsmoking, with a queen-size bed. Do you approve, sir? \n",
      "#Person1#: Yeah, that'll be fine. \n",
      "#Person2#: That's great. This is your key, sir. If you need anything at all, anytime, just dial zero. Summary: \n",
      "--------------------------\n",
      "Flan-T5 response: John Sandals has a reservation for a room at the Venetian Hotel in Las Vegas.\n",
      "Our instruct-tuned response (on top of Flan-T5): John Sandals has a reservation and checks in with his VISA. #Person2# helps him to check in and approves his reservation.\n",
      "Baseline summary from original dataset: John Sandals has got a reservation. #Person1# asks for his identification and credit card and helps his check-in.\n"
     ]
    }
   ],
   "source": [
    "idx = 2\n",
    "diag = tokenizer.decode(tokenized_dataset['test'][idx]['input_ids'], skip_special_tokens=True)\n",
    "model_input = tokenizer(diag, return_tensors=\"pt\").input_ids\n",
    "summary = tokenizer.decode(tokenized_dataset['test'][idx]['labels'], skip_special_tokens=True)\n",
    "\n",
    "original_outputs = model.to('cpu').generate(\n",
    "    model_input,\n",
    "    GenerationConfig(max_new_tokens=200, num_beams=1),\n",
    ")\n",
    "outputs = tuned_model.to('cpu').generate(\n",
    "    model_input,\n",
    "    GenerationConfig(max_new_tokens=200, num_beams=1,),\n",
    ")\n",
    "text_output = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "diag_print = diag.replace(' #',' \\n#')\n",
    "print(f\"Prompt:\\n--------------------------\\n{diag_print}\\n--------------------------\")\n",
    "print(f'Flan-T5 response: {original_text_output}')\n",
    "print(f'Our instruct-tuned response (on top of Flan-T5): {text_output}')\n",
    "print(f'Baseline summary from original dataset: {summary}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantitative Results with ROUGE Metric\n",
    "\n",
    "The [ROUGE metric](https://en.wikipedia.org/wiki/ROUGE_(metric)) helps quantify the validity of summarizations produced by models. It compares summarizations to a \"baseline\" summary which is usually created by a human. While not perfect, it does give an indication to the overall increase in summarization effectiveness that we have accomplished by fine-tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba9aea8243f54bbab43011976f5a444b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rouge = evaluate.load('rouge')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate a Subsection of Summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# again, for the sake of time, we will only be generating a few summaries with each model\n",
    "# outside of the lab, a good exercise is to increase the number of validation summaries generated\n",
    "dialogues = tokenized_dataset['test'][0:10]['input_ids']\n",
    "baseline_summaries = tokenized_dataset['test'][0:10]['labels']\n",
    "\n",
    "# decode the original summaries\n",
    "human_baseline_summaries = []\n",
    "for base_summary in baseline_summaries:\n",
    "    human_baseline_summaries.append(tokenizer.decode(base_summary, skip_special_tokens=True))\n",
    "\n",
    "# generate the summaries\n",
    "original_outputs = model.generate(dialogues, GenerationConfig(max_new_tokens=200))\n",
    "tuned_outputs = tuned_model.generate(dialogues, GenerationConfig(max_new_tokens=200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# store the summaries in lists\n",
    "original_model_summaries = []\n",
    "tuned_model_summaries = []\n",
    "\n",
    "# decode all the summaries\n",
    "for original_summary, tuned_summary in zip(original_outputs, tuned_outputs):\n",
    "    original_model_summaries.append(tokenizer.decode(original_summary, skip_special_tokens=True))\n",
    "    tuned_model_summaries.append(tokenizer.decode(tuned_summary, skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "original_results = rouge.compute(\n",
    "    predictions=original_model_summaries,\n",
    "    references=human_baseline_summaries,\n",
    "    use_aggregator=True,\n",
    "    use_stemmer=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tuned_results = rouge.compute(\n",
    "    predictions=tuned_model_summaries,\n",
    "    references=human_baseline_summaries,\n",
    "    use_aggregator=True,\n",
    "    use_stemmer=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rouge1': 0.23899693678641046,\n",
       " 'rouge2': 0.08932806324110672,\n",
       " 'rougeL': 0.21776705653021444,\n",
       " 'rougeLsum': 0.21325132275132275}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rouge1': 0.5243400833160587,\n",
       " 'rouge2': 0.240785255433749,\n",
       " 'rougeL': 0.3960164115550142,\n",
       " 'rougeLsum': 0.396040090323604}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evalute the Full Dataset\n",
    "\n",
    "The file called \"diag-summary-training-results.csv\" contains a pre-populated list of all model results which we can use to evaluate on a larger section of data. The results show substantial improvement in all ROUGE metrics!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "results = pd.read_csv(\"diag-summary-training-results.csv\")\n",
    "original_model_summaries = results['original_model_summaries'].values\n",
    "tuned_model_summaries = results['tuned_model_summaries'].values\n",
    "human_baseline_summaries = results['human_baseline_summaries'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "original_results = rouge.compute(\n",
    "    predictions=original_model_summaries,\n",
    "    references=human_baseline_summaries[0:len(original_model_summaries)],\n",
    "    use_aggregator=True,\n",
    "    use_stemmer=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tuned_results = rouge.compute(\n",
    "    predictions=tuned_model_summaries,\n",
    "    references=human_baseline_summaries[0:len(tuned_model_summaries)],\n",
    "    use_aggregator=True,\n",
    "    use_stemmer=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rouge1': 0.2334158581572823,\n",
       " 'rouge2': 0.07603964187010573,\n",
       " 'rougeL': 0.20145520923859048,\n",
       " 'rougeLsum': 0.20145899339006135}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rouge1': 0.42161291557556113,\n",
       " 'rouge2': 0.18035380596301792,\n",
       " 'rougeL': 0.3384439349963909,\n",
       " 'rougeLsum': 0.33835653595561666}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rouge1 absolute percentage improvement after instruct fine-tuning: 18.82%\n",
      "rouge2 absolute percentage improvement after instruct fine-tuning: 10.43%\n",
      "rougeL absolute percentage improvement after instruct fine-tuning: 13.70%\n",
      "rougeLsum absolute percentage improvement after instruct fine-tuning: 13.69%\n"
     ]
    }
   ],
   "source": [
    "improvement = (np.array(list(tuned_results.values())) - np.array(list(original_results.values())))\n",
    "for key, value in zip(tuned_results.keys(), improvement):\n",
    "    print(f'{key} absolute percentage improvement after instruct fine-tuning: {value*100:.2f}%')"
   ]
  }
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