NLP_HuggingFace_LLMs/02_HF_Text_Summarizer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, HTML\n",
    "display(HTML('<style>.container { width:100% !important; }</style>'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aEeFv1rMo9U6",
    "outputId": "77b4cad4-6a6e-49cd-8d6f-164fd1c9c245"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Feb 25 13:20:57 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  NVIDIA GeForce RTX 4090        On  | 00000000:01:00.0 Off |                  Off |\n",
      "|  0%   36C    P8              31W / 450W |   8684MiB / 24564MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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[0m\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[0m\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[0m\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[0m"
     ]
    }
   ],
   "source": [
    "!pip -q uninstall transformers -y\n",
    "!pip -q install transformers[sentencepiece]\n",
    "!pip -q install transformers\n",
    "!pip -q install accelerate -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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[0m"
     ]
    }
   ],
   "source": [
    "!pip -q install pandas matplotlib torch nltk tqdm transformers datasets transformers[sentencepiece]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uqOtyVtZqKnK",
    "outputId": "fefd4b58-2229-4345-d939-7b504bf05587"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "2024-02-25 13:21:18.076170: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-02-25 13:21:18.076240: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-02-25 13:21:18.077366: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-02-25 13:21:18.084780: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-25 13:21:19.164485: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# Importing\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import nltk\n",
    "\n",
    "from nltk.tokenize import sent_tokenize\n",
    "from tqdm import tqdm\n",
    "nltk.download('punkt')\n",
    "\n",
    "# model download\n",
    "from transformers import pipeline\n",
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
    "from datasets import load_dataset, load_from_disk, load_metric\n",
    "\n",
    "# finetuning\n",
    "from transformers import TrainingArguments, Trainer, DataCollatorForSeq2Seq\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3viz1DDJqNRf",
    "outputId": "a785e0e5-ce91-41da-d845-faa2e67489d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gpu\n"
     ]
    }
   ],
   "source": [
    "device='gpu' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
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    "colab": {
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    "id": "ICJRXvIaqPXR",
    "outputId": "6238d8bc-56cf-45ae-bd50-311ca63635a4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at google/pegasus-cnn_dailymail and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model='google/pegasus-cnn_dailymail'\n",
    "tokenizer=AutoTokenizer.from_pretrained(model)\n",
    "model_pegasus=AutoModelForSeq2SeqLM.from_pretrained(model).to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "ot0b0u3ys3X2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 14732\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 819\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary'],\n",
       "        num_rows: 818\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=load_dataset('samsum')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "xY8UFUhatU9o"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetch first dialogue and summary\n",
      "Amanda: I baked  cookies. Do you want some?\n",
      "Jerry: Sure!\n",
      "Amanda: I'll bring you tomorrow :-) Amanda baked cookies and will bring Jerry some tomorrow.\n"
     ]
    }
   ],
   "source": [
    "# train dataset\n",
    "train = dataset['train']\n",
    "\n",
    "# dialogues in train dataset\n",
    "dialogue = train['dialogue']\n",
    "\n",
    "# summary in train dataset\n",
    "summary = train['summary']\n",
    "\n",
    "print('Fetch first dialogue and summary')\n",
    "print(dialogue[0], summary[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "zYUGzUvnzq-i"
   },
   "outputs": [],
   "source": [
    "def convert_examples_to_features(data_in_batch):\n",
    "    # Tokenize the dialogues in the batch\n",
    "    input_encoding = tokenizer(data_in_batch['dialogue'],\n",
    "                               max_length=1024,\n",
    "                               truncation=True)\n",
    "\n",
    "    # Tokenize the summaries in the batch\n",
    "    target_encoding = tokenizer(data_in_batch['summary'],\n",
    "                                max_length=128,\n",
    "                                truncation=True)\n",
    "\n",
    "    # Return a dictionary containing input and target tokenized sequences\n",
    "    return {\n",
    "        'input_ids': input_encoding['input_ids'],  # Input token IDs for dialogues\n",
    "        'attention_mask': input_encoding['attention_mask'],  # Attention mask for dialogue inputs\n",
    "        'labels': target_encoding['input_ids']  # Target token IDs for summaries (used as labels)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "Wh1VhhIotf1Z"
   },
   "outputs": [],
   "source": [
    "dataset_en = dataset.map(convert_examples_to_features, batched = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "JkuS9Md53ndP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['id', 'dialogue', 'summary', 'input_ids', 'attention_mask', 'labels'],\n",
       "    num_rows: 14732\n",
       "})"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
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       " 1]"
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     "metadata": {},
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   ],
   "source": [
    "# Display the first example from the training dataset\n",
    "display(\n",
    "    dataset_en['train'],  # Display the training dataset\n",
    "    dataset_en['train']['input_ids'][0],  # Display the input token IDs of the first example\n",
    "    dataset_en['train']['attention_mask'][0],  # Display the attention mask of the first example\n",
    "    dataset_en['train']['labels'][0]  # Display the target token IDs (labels) of the first example\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "CfOP_ysM7KaT"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='920' max='920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [920/920 23:22, Epoch 0/1]\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",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>1.665300</td>\n",
       "      <td>1.482481</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=920, training_loss=1.8287915426751842, metrics={'train_runtime': 1404.8123, 'train_samples_per_second': 10.487, 'train_steps_per_second': 0.655, 'total_flos': 5528248038285312.0, 'train_loss': 1.8287915426751842, 'epoch': 1.0})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reference: https://huggingface.co/docs/transformers/v4.38.1/en/main_classes/trainer#transformers.Seq2SeqTrainingArguments\n",
    "seq2seq_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)\n",
    "\n",
    "training_args=TrainingArguments(\n",
    "    output_dir='pegasus-samsum',\n",
    "    num_train_epochs=1,\n",
    "    warmup_steps=500,\n",
    "    per_device_train_batch_size=1,\n",
    "    per_device_eval_batch_size=1,\n",
    "    weight_decay=0.01,\n",
    "    logging_steps=10,\n",
    "    evaluation_strategy='steps',\n",
    "    eval_steps=500,\n",
    "    save_steps=1e6,\n",
    "    gradient_accumulation_steps=16\n",
    ")\n",
    "trainer=Trainer(model=model_pegasus,\n",
    "                args=training_args,\n",
    "                tokenizer=tokenizer,\n",
    "                data_collator=seq2seq_data_collator,\n",
    "                train_dataset=dataset_en['train'],\n",
    "                eval_dataset=dataset_en['validation'])\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "h7DQmI2z8jky"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
      "Non-default generation parameters: {'max_length': 128, 'min_length': 32, 'num_beams': 8, 'length_penalty': 0.8, 'forced_eos_token_id': 1}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('samsum-tokenizer/tokenizer_config.json',\n",
       " 'samsum-tokenizer/special_tokens_map.json',\n",
       " 'samsum-tokenizer/spiece.model',\n",
       " 'samsum-tokenizer/added_tokens.json',\n",
       " 'samsum-tokenizer/tokenizer.json')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the Pegasus model\n",
    "model_pegasus.save_pretrained('pegasus-samsum-model')\n",
    "\n",
    "# Save the tokenizer used with the Pegasus model\n",
    "tokenizer.save_pretrained('samsum-tokenizer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "2BPx6ee3_s16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample text: Tim: Hi, what's up?\n",
      "Kim: Bad mood tbh, I was going to do lots of stuff but ended up procrastinating\n",
      "Tim: What did you plan on doing?\n",
      "Kim: Oh you know, uni stuff and unfucking my room\n",
      "Kim: Maybe tomorrow I'll move my ass and do everything\n",
      "Kim: We were going to defrost a fridge so instead of shopping I'll eat some defrosted veggies\n",
      "Tim: For doing stuff I recommend Pomodoro technique where u use breaks for doing chores\n",
      "Tim: It really helps\n",
      "Kim: thanks, maybe I'll do that\n",
      "Tim: I also like using post-its in kaban style\n"
     ]
    }
   ],
   "source": [
    "# Load tokenizer and model from the pretrained directories\n",
    "tokenizer = AutoTokenizer.from_pretrained('samsum-tokenizer')\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained('pegasus-samsum-model')\n",
    "\n",
    "# Create a pipeline object for summarization using the loaded model and tokenizer\n",
    "pipeline_obj = pipeline('summarization', model=model, tokenizer=tokenizer)\n",
    "\n",
    "# Define a sample text for summarization\n",
    "sample_text_for_text = dataset['train']['dialogue'][2]\n",
    "actual_summary = dataset['train']['summary'][2]\n",
    "print(f'Sample text: {sample_text_for_text}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual summary: Kim may try the pomodoro technique recommended by Tim to get more stuff done.\n"
     ]
    }
   ],
   "source": [
    "print(f'Actual summary: {actual_summary}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "aqWPIU6MCAO4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'summary_text': \"Kim was going to do lots of stuff but ended up procrastinating. She'll move her ass tomorrow and do everything. Tim recommends Pomodoro technique for doing chores.\"}]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a summary using the pipeline object\n",
    "gen_kwargs = {'length_penalty': 0.8, 'num_beams':8, 'max_length': 128}\n",
    "display(pipeline_obj(sample_text_for_text, **gen_kwargs))\n",
    "prediction = pipeline_obj(sample_text_for_text, **gen_kwargs)[0]['summary_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "8V7-E1kLDd3k"
   },
   "outputs": [],
   "source": [
    "# Evaluation\n",
    "# evalation of model\n",
    "# Evaluation\n",
    "\n",
    "def generate_batch_sized_chunks(list_of_elements, batch_size):\n",
    "    '''split the dataset into smaller batches that we can process simultaneously\n",
    "    Yield successive batch-sized chunks from list_of_elements.'''\n",
    "    for i in range(0, len(list_of_elements), batch_size):\n",
    "        yield list_of_elements[i : i + batch_size]\n",
    "\n",
    "\n",
    "\n",
    "def calculate_metric_on_test_ds(dataset, metric, model, tokenizer,\n",
    "                               batch_size=16, device=device,\n",
    "                               column_text='article',\n",
    "                               column_summary='highlights'):\n",
    "    # Divide input text data and target summaries into batches\n",
    "    article_batches = list(generate_batch_sized_chunks(dataset[column_text], batch_size))\n",
    "    target_batches = list(generate_batch_sized_chunks(dataset[column_summary], batch_size))\n",
    "\n",
    "    # Iterate over batches and track progress using tqdm\n",
    "    for article_batch, target_batch in tqdm(zip(article_batches, target_batches), total=len(article_batches)):\n",
    "\n",
    "        # Tokenize the input text data for model input\n",
    "        inputs = tokenizer(article_batch, max_length=1024, truncation=True,\n",
    "                           padding='max_length', return_tensors='pt')\n",
    "\n",
    "        # Generate summaries using the model\n",
    "        summaries = model.generate(input_ids=inputs['input_ids'].to('cuda'),\n",
    "                                   attention_mask=inputs['attention_mask'].to('cuda'),\n",
    "                                   length_penalty=0.8, num_beams=8, max_length=128)\n",
    "\n",
    "        ''' Parameter for length penalty ensures that the model\n",
    "        does not generate sequences that are too long. '''\n",
    "\n",
    "        # Decode the generated summaries for evaluation\n",
    "        decoded_summaries = [tokenizer.decode(s, skip_special_tokens=True,\n",
    "                                              clean_up_tokenization_spaces=True)\n",
    "                            for s in summaries]\n",
    "\n",
    "        decoded_summaries = [d.replace('', ' ') for d in decoded_summaries]\n",
    "\n",
    "        # Add the generated summaries and references to the metric for evaluation\n",
    "        metric.add_batch(predictions=decoded_summaries, references=target_batch)\n",
    "\n",
    "    #  Finally compute and return the ROUGE scores.\n",
    "    score = metric.compute()\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "RVTphNwbGuNN"
   },
   "outputs": [],
   "source": [
    "rouge_names=['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n",
    "rouge_metric = load_metric('rouge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "ogY_PBYzHBXN"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [00:03<00:00,  1.30it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rouge1</th>\n",
       "      <th>rouge2</th>\n",
       "      <th>rougeL</th>\n",
       "      <th>rougeLsum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pegasus</th>\n",
       "      <td>0.0247</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024573</td>\n",
       "      <td>0.024587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rouge1  rouge2    rougeL  rougeLsum\n",
       "pegasus  0.0247     0.0  0.024573   0.024587"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = calculate_metric_on_test_ds(dataset['test'][0:10],\n",
    "                                    rouge_metric,\n",
    "                                    trainer.model,\n",
    "                                    tokenizer,\n",
    "                                    batch_size = 2,\n",
    "                                    column_text = 'dialogue',\n",
    "                                    column_summary = 'summary'\n",
    "                                    )\n",
    "\n",
    "rouge_dict = dict( (rn, score[rn].mid.fmeasure) for rn in rouge_names )\n",
    "\n",
    "pd.DataFrame(rouge_dict, index = [f'pegasus'] )"
   ]
  }
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