Datasci_266_summarization_pretrain_noFT.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"view-in-github"},"source":["<a href=\"https://colab.research.google.com/github/sleighton2022/datasci266-final-project/blob/main/Datasci_266_baseline_summarization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"]},{"cell_type":"markdown","metadata":{"id":"e-k1jXQHLyFU"},"source":["# Datasci 266 final project - pretrained models - no FT\n","\n","**Description:**\n","\n","**Structure of the notebook:**\n","\n","**Expected results:**\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"0QDMDB_KqT8m"},"source":["## Setup"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"elapsed":118098,"status":"ok","timestamp":1722629773110,"user":{"displayName":"Xin C","userId":"03164725939202625018"},"user_tz":240},"id":"Gl5EZTtfK5jL","outputId":"ca7a8bc2-5e5e-4003-9d33-d007e2e4de07"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/547.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.1/316.1 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\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","torch 2.3.1+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.1+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n","gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n","google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n","ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Collecting bert_score\n","  Downloading bert_score-0.3.13-py3-none-any.whl.metadata (15 kB)\n","Requirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.3.1+cu121)\n","Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.1.4)\n","Requirement already satisfied: transformers>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from bert_score) (4.42.4)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bert_score) (1.26.4)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.32.3)\n","Requirement already satisfied: tqdm>=4.31.1 in /usr/local/lib/python3.10/dist-packages (from bert_score) (4.66.4)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from bert_score) (3.7.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from bert_score) (24.1)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2024.1)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2024.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (3.15.4)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (1.13.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (2024.5.0)\n","Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n","Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.0.0->bert_score)\n","  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n","Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (2.3.1)\n","Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.0.0->bert_score)\n","  Downloading nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.23.5)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (6.0.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (2024.5.15)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.4.3)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.19.1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (1.2.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (4.53.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (1.4.5)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (9.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (3.1.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (2024.7.4)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.1->bert_score) (1.16.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.0.0->bert_score) (2.1.5)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.0.0->bert_score) (1.3.0)\n","Downloading bert_score-0.3.13-py3-none-any.whl (61 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.1/61.1 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hUsing cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n","Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n","Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n","Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n","Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n","Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n","Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n","Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n","Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n","Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n","Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n","Downloading nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl (19.7 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.7/19.7 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, bert_score\n","Successfully installed bert_score-0.3.13 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105\n","Collecting sentence-transformers\n","  Downloading sentence_transformers-3.0.1-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: transformers<5.0.0,>=4.34.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.42.4)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.4)\n","Requirement already satisfied: torch>=1.11.0 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/usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (2.1.5)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2024.7.4)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\n","Downloading sentence_transformers-3.0.1-py3-none-any.whl (227 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: sentence-transformers\n","Successfully installed sentence-transformers-3.0.1\n","\u001b[31mERROR: Could not find a version that satisfies the requirement time (from versions: none)\u001b[0m\u001b[31m\n","\u001b[0m\u001b[31mERROR: No matching distribution found for time\u001b[0m\u001b[31m\n","\u001b[0m"]}],"source":["!pip install -q sentencepiece\n","!pip install -q transformers\n","!pip install -q evaluate\n","!pip install -q datasets\n","!pip install -q pandas\n","!pip install -q rouge_score\n","!pip install -q nltk\n","!pip install bert_score\n","!pip install sentence-transformers\n","!pip install -q time\n","!pip install -q torchinfo\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KzQBFiaPnr4j"},"outputs":[],"source":["#import evaluate\n","import torch\n","my_device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n","my_device\n","\n","#from transformers import T5Tokenizer, TFT5ForConditionalGeneration\n","#from transformers import BertTokenizer, BertModel\n","\n","import evaluate\n","import numpy as np\n","import time\n","from transformers import pipeline\n","from transformers import Trainer, TrainingArguments"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zs00P-oRbwev"},"outputs":[],"source":["#from transformers import T5Tokenizer, TFT5ForConditionalGeneration  #tensorflow implementation\n","from transformers import T5Tokenizer, T5ForConditionalGeneration #torch\n","from transformers import BartTokenizer, BartForConditionalGeneration"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":70},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1722629793661,"user":{"displayName":"Xin C","userId":"03164725939202625018"},"user_tz":240},"id":"S5st3b1gVsB-","outputId":"bb790e71-de95-4a67-de7d-7b649bce828f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\n#!pip install git+https://github.com/sleighton2022/datasci266-final-project.git\\n!wget https://raw.githubusercontent.com/sleighton2022/datasci266-final-project/main/summary_utils.py\\nfrom summary_utils import SummaryEvaluator, DatasetManager, SummaryModel\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":4}],"source":["'''\n","#!pip install git+https://github.com/sleighton2022/datasci266-final-project.git\n","!wget https://raw.githubusercontent.com/sleighton2022/datasci266-final-project/main/summary_utils.py\n","from summary_utils import SummaryEvaluator, DatasetManager, SummaryModel\n","'''\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":52},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1722629793662,"user":{"displayName":"Xin C","userId":"03164725939202625018"},"user_tz":240},"id":"1TY2dvNn3znE","outputId":"69e28e89-475a-4306-aa43-3c8b40df6e70"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\n# Load dataset\\ndataset_manager = DatasetManager(dataset_name=\"xsum\", sample_size=10)\\nsampled_dataset = dataset_manager.load_sampled_dataset()\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":5}],"source":["'''\n","# Load dataset\n","dataset_manager = DatasetManager(dataset_name=\"xsum\", sample_size=10)\n","sampled_dataset = dataset_manager.load_sampled_dataset()\n","'''"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":365,"referenced_widgets":["89f119da999e4de681c7199a9e7d7589","5e4415fc3dc24548b9b67d2f0969f35f","3dc685a4451549a0bd4e08c8f8d8f218","a99d0e5413bb481dbb6fe04e990bde51","f0bac783eb544721b48ac68cdea6dd61","e03d1828340e489588c9325e74538c52","d439f4fa3d85445da9fbfec1b1fec130","73439f36d3ae4011ae6f3a5d4ac1335e","0a14fe16880a4b99a096700fa59f5e17","06c95028e0d7491697ed26f7dd1a9a4d","a50d8fcecd1b43f294ab122d37f1c722","2d344140882c4ac0b7b354f7b094c760","7ceaf751c74945febb6ee0856fdae662","44b424e0061e486ebe824dc27f82aaf7","d016336591854a919c03aeb8e975ff49","73de46e844844ddf82ddbe2a630d551f","8df36b219ca54b22900006319178bef6","8b48e4a1d4cb4c4193cf8560fbc6a5b5","d6202ea0bbb0418382e5ea9f91fc5ac0","58080d16f50a4273aad153da29ead4bd","c3fd1aab208c403898c8fa794923f030","303e813c71c04353832059831493f481","7854d4a4247049d38e06aa1ab400aa69","1a71e49253d64d06bb8c98c9f1d1f90a","5ea4fa3941744bb3b7cba0774f7f8304","3d509a51b8c24773a3421b1cdef53474","ad836365a56f41d6b2131d218bf97314","e8bb84542d204bff840d1c8fd03b1627","0c9c40a480fd47318bf2b171d0ed296f","452b8626ac08421c976317bb56b6c60e","8ec923224c504888a625b9419debdf4c","33288bc873e14ef392791208b97cfa1c","223e51fe04a94d61ba3edde14797b154","2a80e632e7a243a9b0a991fa7394dfcb","bf022af354db4fe198546f18864325cc","2c9fcb6039924de89a03abbfa9e60579","beb619a19dba4cc8a168f72a8a724466","9d86dc9ee61d41249dd2c553c1f63cc2","32e34ba86e1848939be6e5bfa0f3fb4c","d10fbcf68e14428181fe25dd86ebffdd","bcd284f12f7748a4afed7931689d46b0","d8c637c125b449efa977853d95e7abad","1c03a7c3804c469ca279c4d2220c4b91","251370363cbc4f2fa77eb46879d83da6","f16c210dbf74406696943c267d278177","27aba955b30b4300a7961b2222a0ec4b","cb501865fc244f09aa3180fe58c25393","c50df05fa97146148b7bdd3c15348206","8ec7bf5f47844949959697d25f7be851","ee871b34f22647ce95332303674f30ee","48360e742f7e46c1bbe9745365573c8a","99c764bb9b31411290b55424d878835e","d6b4a35a098146968aced89be99e4279","97a97cbe7a514d1b8af8765bb54152c1","53b66c20441744708e31f5939df6ce6a","aabb4a6d9fcb48b3a414d521db744916","13acc888b12c4069ac28217214ae9111","e155982fc2ae4a28914cb834055b2e15","502d01dd48704794851cb08ab44945ec","466adb3234124d7c9aa75e8a9eada99c","ea89d1fa6f744cff8f7cb3cc4a57c7b5","d062e5179fd2472283af8e587d669b47","e794020fea824e579f0b1aa9fba1249a","749a96701a494261931c5f0abce59a65","46872e666b454246b4a6628e545f20f1","ad25545b3ac2470780805076f6740a49","02927603775a43d4a3d875cb6776f643","e7aa8a4390ad4069bf4fa4aad13c2fac","0e181083be93454598457ed3145163bb","c78d518af48844bf98979f1139f0bcee","5b5c8f591a0f46a0a40abb232d472ee3","895c094db1594f55a53f8ae2d178e1a3","6dbd96e04ff94b55b75a55e91dfc6262","f3772c2e6b6b46be8a23b29ed411f9f3","85d651c752c543929be692a561333ba1","7f67be710c1b4d3493117013ee49102c","924408a1db904ac18aaeaa3889f69388"]},"executionInfo":{"elapsed":140433,"status":"ok","timestamp":1722629934083,"user":{"displayName":"Xin C","userId":"03164725939202625018"},"user_tz":240},"id":"InbcAQuqJxpi","outputId":"ea976774-a438-4d64-bb29-cf612de274be","collapsed":true},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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 builder script:   0%|          | 0.00/5.76k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"89f119da999e4de681c7199a9e7d7589"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/6.24k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2d344140882c4ac0b7b354f7b094c760"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/255M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7854d4a4247049d38e06aa1ab400aa69"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/1.00M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2a80e632e7a243a9b0a991fa7394dfcb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/204045 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f16c210dbf74406696943c267d278177"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/11332 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aabb4a6d9fcb48b3a414d521db744916"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/11334 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"02927603775a43d4a3d875cb6776f643"}},"metadata":{}}],"source":["from datasets import load_dataset\n","xsum_dataset = load_dataset(\"xsum\", trust_remote_code=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":207,"status":"ok","timestamp":1722629934286,"user":{"displayName":"Xin C","userId":"03164725939202625018"},"user_tz":240},"id":"OPkCcvj9KAoz","outputId":"ff634171-f713-43cd-b02f-eb58f536a679"},"outputs":[{"output_type":"stream","name":"stdout","text":["204045\n","11334\n","{'document': 'In Wales, councils are responsible for funding and overseeing schools.\\nBut in England, Mr Osborne\\'s plan will mean local authorities will cease to have a role in providing education.\\nAcademies are directly funded by central government and head teachers have more freedom over admissions and to change the way the school works.\\nIt is a significant development in the continued divergence of schools systems on either side of Offa\\'s Dyke.\\nAnd although the Welsh Government will get extra cash to match the money for English schools to extend the school day, it can spend it on any devolved policy area.\\nMinisters have no plans to follow suit.\\nAt the moment, governing bodies are responsible for setting school hours and they need ministerial permission to make significant changes.\\nThere are already more than 2,000 secondary academies in England and its extension to all state schools is unlikely to shake the Welsh Government\\'s attachment to what they call a \"community, comprehensive model\" for schools.\\nIt rejects claims that freedom given to academies can help drive up standards, and it points to academy-free Scotland as the best performing school system in the UK.\\nEducation Minister Huw Lewis said there was \"very little evidence to suggest\" academies have a positive impact in driving up standards and Wales would not be following the model.\\n\"The Tories have wasted hundreds of millions of pounds on academies and free schools and as the Chancellor finalises his budget plans to slash vital services even further, he is committing them to wasting even more on a failing endeavour.\\n\"We have no plans to introduce the chaos and waste of academies and free schools here in Wales.\"\\nNone of the main parties in May\\'s Assembly election - including the Welsh Conservatives - have said they want to introduce academies in Wales.\\nOwen Hathway, NUT Cymru\\'s policy officer, called the academy plans for England \"scandalous.\".\\n\"There is no evidence that academies work, no evidence that they raise standards, no evidence that they offer better quality education and no evidence that they are what parents and communities want,\" he said.\\n\"Certainly a commitment to comprehensive education is something we would want, and indeed expect, all parties to hold firm to in their manifestos for the forthcoming Welsh election.\"\\nBut the Welsh and English schools systems are still linked by a joint arrangement for teachers\\' pay and conditions.\\nAcademies are not tied to these pay scales so in effect Wednesday\\'s announcement will take all English schools out of the system and raise questions about the viability of an England and Wales pay and conditions structure.\\nThere is already growing momentum for the devolution of teachers\\' pay and conditions.\\nOriginally sceptical, the Welsh Labour Government is now broadly in favour.\\nSome teaching unions remain opposed because of concern that Welsh teachers would end up being paid less than those in England.\\nMr Hathway said teachers were concerned it could lead to regional pay.\\n\"At the same time we do of course recognise that the issue of pay is already becoming a grey area due to the negative changes we see taking place in England,\" he said.\\nBut an even bigger difference between the schools landscape on either side of the border, appears to make separate arrangements for pay increasingly likely in future.', 'summary': 'As Chancellor George Osborne announced all English state schools will become academies, the Welsh Government continues to reject the model here.', 'id': '35821725'}\n"]}],"source":["train_data = xsum_dataset['train']\n","train_data = train_data.shuffle(seed=42)\n","test_data = xsum_dataset['test']\n","test_data = test_data.shuffle(seed=42)\n","valid_data = xsum_dataset['validation']\n","valid_data = test_data.shuffle(seed=42)\n","print(len(train_data))\n","print(len(test_data))\n","print(train_data[0])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UCNR7tXRZZR1"},"outputs":[],"source":["#set test dataset for perf eval\n","test_dataset = test_data.select(range(200))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GHlJmGNfKVRj"},"outputs":[],"source":["#global params\n","SUMMARY_MAX_LEN = 150\n","SUMMARY_MIN_LEN = 30\n","\n","#very important to put in front of each article\n","PROMPT = 'summarize: '"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":529,"referenced_widgets":["c439f72a680044ddb27772459f3f1575","858a6e391d6d49d3aa92ba178e425ac7","bb1d9396030c4496b79c86c492fdc77b","d9a95f39c4284852981a9f916d077ba5","e38adc87a9a4478aba54ce57ee76ba3a","25eeeef291be4a06b3d542e672335b38","9357fc7cb4434d10bd35ba2e7d7eabe1","19786635f28f4f69aaee800fc2361657","6fb76bd141fe4a638a8198b8b43b74fc","04baa0e36dee43819ca8d7b3daaeae7a","f8dcd3bb663140beb4e40a9a08b0252e","d87b7fcd003040f9bf14e405a553aa72","eb5ee0375ac441208b986bf7b53ad0e1","973b53a0e67d4082aad86b0bdd774874","5be675d9028f4454a7f98ec30a2c3180","45d98d7c5aa5403b89a20f104a7e2982","8462e08ffe924c9ca1e22ebf9621af68","f0b037a84add4c4b8c491acb572d5190","55361055a97d43ac8efd7bd09ff17ac6","4c49d02319364673b230c3287f6d8b71","a30a21c6bd89465baca08fa5028d0e27","e5428999da4a4e2082e34c4a3b530d84","05a7c7edec374247980aa159a954da8f","d8976ec0ec254a96b8ab611eac366efe","6bf9f255fea84a3db7ec4065b36d6dae","ab0b1bf79ab644569754a83a5d5c7ade","a283534ff74d4379a69a7965752aa7c9","59d6c52310f24cd29bf1a0b9289dafc8","5b9401636ea94c75b2ba4d7cbfb059ff","7d25016bc0204518972edfb3b5e56f6e","63756a4fe457465cb02497a0e17268af","59f86c79ce1043a38a29f9c2bf989082","db2d81acedc34e2eab9823baa3049cec","b424c8977a674d659be212bede52682d","2aea4eb17e294888ad2ef989c428e0d1","43fa93b4ce224fd8af4dade32750b2b6","7414ae32308641ec9f45d87d5b2a3bef","390d156c248f47be9be214019d30daa7","e612078fe28b409db64d2aba527d0002","952c18c527284c55b0a258770eca0d4d","323d837dbdbc49e3ba5e5df87ed4ce32","312e5a9a106f440db3b2ad42b5be28b6","d124e1b7ba404b5bb22ee72c4a11e5a9","077d68b09a81400b8b394f47c938ebd2","affcc27cc45a463b98dfdae1539a9a5a","48f23c5ce1f84310bacace150b0050ac","3113074a55524013a05be2a1620c2f93","81c81bd12d004e469ba3e3cc79480c94","773413489fba49a7a316eac1e09beae2","c6b81cb217de498481fe7b2b332cb40a","922ffeb6702846a5a431a3caa9b7a051","4f1788c783d140e4b39ad5925856c3c4","4c55c8a8ac4e457eaa3c0eaf12087311","99585e9b39fe458fbd08a778abe50fc2","7d09829481ed4100a7517df4085530cd","a20e2253c38b4888b23b9778686d7918","a7130f8ff8e54e7193c20bd9a31a3206","30ad543ed3ea467cb5c24845bc120d58","2aee21122e654d68a81456de991a562e","2bc881f033184acb81e91e4e4f40dfa9","a023211c910b4840b5bc7688247ae1f4","a70226843e8c43a7bcc3d2544d277803","c7f31b18fe854fd7988bd3d7659d0d5c","453d5a3751be437385cd84afa41d61b7","d514625a99df4608a14c6d12be2f79d6","3083229068634bbca5e6f5d9d082eccd","3d8ca2cb9f544e85ad33a1bdef53b317","f122c5c2eed246c4b8886c7b3181ba7a","fd2939922ae74b9fbfbf76b886fbe654","c62fbf575f4a4bf2952def18e50c9dbb","4c233a3cb5da405599328d5f72272bad","572150c07b5f430bbdfc3b97d76b0723","a618eb0f51de46ea8280e17cc94d1a36","f6a33c87a2b44360a3cb04d7e8385419","973667003728484abc7a5237b7be9fb8","4a4caa0e0f334aa293de8ab81d017875","df8ff2d44a8c4f88ac3f479bf42fdb0e","e0ba54b0ba9440d780a997db0efa581b","a5d2ca9dcfc24793a7669cbc33a626f3","c9de9d7bad5f486eb389c0320e2b023e","672605114a9a40a6a6247bea808cc758","fc564541c08b48498a7bd0627907504f","1a1faa66d0604dca97d23a93ef107d5d","57b79e954fd0497d88d0fd95dc8d8020","7c7e6a4c544f456da4001f144dbadd66","b8835d6f965c4bd9b4575541f9027d32","ff73a9c3a7d044fb8475d302052e0f79","0285a3cefff14846bdb31beabcf91bd6","0c55fea23c644f648f101f76f8d5e943","de6e182b7c854141af2db63aeda76fee","f3c1b2c2d1da4eeebcdb5f9345d39aad","282d4a26a22147f1a6e96fd4ef92f486","7bd0e13b90fa4c9f85187ab08e49c46b","f3a53bc8065f4d85b633f243af84f92a","63b1b0342e544d9b9d1ea4e5602553a1","52164c0ddacf446fb03ee8a003f48d90","b8b144366fe7460d895c7731c37a5ed6","86edcbba97c745a5b309e3f5a0460245","d7fb1afbec72421895c49fe5a53a4ac2","649616467a554f7fac073d3ea235e52d","a441ca13a55241babdc54c2227bd487d","673f0774f30547309f6e49b7bc0c39ba","95fb2d3003e84e93828f80ffff3b64cb","ad83738cb67246b7a986b50448250496","050e176da6d14fc383dea0a6335dc763","6f028084838c4a4ebaafc07d12308709","1a9bc32159424d3d82a15e5430f520f3","72c7e2cb685847c3ad7b113305d4dfff","a7d66129d20746ad9b05501c977a212d","8b8373aad45443b8baffb74c42916361","78ca216358a44083ab1593470131d8f6","d710af1a910a4645a840e49c2fc565d8","5aeeb06dc060438694bfe26d24b87aa0","91b3c4af553542caad88594648638f5b","5c391677fe5643e59802184ebe8c0b97","f4718bb7ae864a1684329610bbca63ef","3373be7b82614d52b73e749204831318","bd911c7d39494a068887a81634f09c03","2af9ab9a75a34e8da88d1aed0d1ca89c","27a9981c5aa74959a18dc1e46058725a","e09bb8c6404549fd8d26e542b262ec31","0f8f5adbe8cc40eaab38ed6c83a04b21","3432a78d731d475c8a03a6b100548539","0b3886fe018f4ce7a767970c270b4470","74985a80c3d8470fa60377aa7fe41cbf","6d752fba78c5497db6a508e45e59736f","42c16d9892774d859034c426af0236d0","b3cf44237c8c4e778eb7b1a36d9b068a","c5dcf834bd5048aeb05f8e7ac6f493ef","7823112b684147989e10b3e1fe077ae9","83a5a4bd1c1d4b4da9935b43484cd272","4290dc30de8b4d86acb4d780ac909566","d5f9b28a379d46fc9a7da0fea1edfb2d","61c635c3596642c8a75099ada44128ec","f965ab992ff3470895fa8a6f6f15120d","e00177b4a0384b70bdd6ca5e9424e479","c03d1e64542c42a0848ce21e1ccd89b8","7e31f5340c9d4c9dad65c042b48b07f9","bb1b77a171e649a5891095bca7f8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C","userId":"03164725939202625018"},"user_tz":240},"id":"m0-ZHy8SdfSq","outputId":"6f5648a3-4617-487f-d06d-26fc57cff4af","collapsed":true},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/5.94k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c439f72a680044ddb27772459f3f1575"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading extra modules:   0%|          | 0.00/1.55k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d87b7fcd003040f9bf14e405a553aa72"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading extra modules:   0%|          | 0.00/3.34k [00:00<?, 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0c55fea23c644f648f101f76f8d5e943"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"649616467a554f7fac073d3ea235e52d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"78ca216358a44083ab1593470131d8f6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0f8f5adbe8cc40eaab38ed6c83a04b21"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|     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metrics\n","bleu = evaluate.load(\"bleu\")\n","rouge = evaluate.load('rouge')\n","bertscore = evaluate.load(\"bertscore\")\n","\n","from sentence_transformers import SentenceTransformer, util\n","sentence_transformer = SentenceTransformer(\"all-mpnet-base-v2\")\n","def calculate_vector_similarity(reference_summaries, generated_summaries):\n","    \"\"\"\n","    Calculates cosine similarity between reference and generated summary embeddings.\n","    \"\"\"\n","    ref_embeddings = sentence_transformer.encode(reference_summaries, convert_to_tensor=True)\n","    gen_embeddings = sentence_transformer.encode(generated_summaries, convert_to_tensor=True)\n","    cosine_scores = util.cos_sim(ref_embeddings, gen_embeddings)\n","\n","    avg_similarity = cosine_scores.diagonal().mean().item()  # Average cosine similarity\n","    return avg_similarity"]},{"cell_type":"markdown","metadata":{"id":"h7n_qBk_C1x3"},"source":["# Run pretrained models without FT"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xQsNfgw1l0uE","colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["70d24f492ef8498295ab493ded05a16a","0faa3ab07a714f13801b81073a9d17a0","f507d7cf7e6f488f827e1c2fec478287","f9d86e1f763a4f61a122069f7842286f","668b0eafe5184fd8bdfbddf63eb42473","205ee49df06e4368939557149e3e3f10","7712018a78a74ed9afa4292b62a5c6f3","75bc93da666e4b96b624fd325ea0fbcd","d11c23cfc4f4451181b1edbfe89c1d4a","c3dab5999a0c4059900b15e2908b3d6f","dad9211160bc4bf193388efcdb326c6c","59d9d7a009424ec7b5a04262105cdd82","582c90d7911d4d359b42db5d518d8187","1edb3236e9094d01bf646cfa42f7edca","fc1dce101efc41ccb10ac164badac1fc","59e4b155320e41f2b620f2599a6f4316","681033b93c964e0999e1447c2f3e2859","271a0911b21e488bb27c3b5d7b7ad201","0ebf2f70d63a459bb5fa77044d16a435","2cfd988b5a2748dcad966b1da52bfdd5","88fc9d82123c4534bc8ccb626e1d5323","9ccfc86c8491482282a53a71df6b0d66","577e374fa2af4cf3a5e9392aef05e02a","b12cad3fedf94b87a30bdda147af4cf6","825341c26b2d49d4a5a3078bba5c4243","c34469ae8d854371b1d8fc86bad1ebdf","62adaf81bc014e5084767a0cda388000","a6935c6bcd014688a81464a54fcd437e","0b5157fa794745aea34717b49b7ee7be","1723f1793cb246108b05f5fc55c3a840","f721134834904d429f0d3f8b434ee2f7","d5ade50224a249a18ba4af7f9d92f668","6ab262ceb85441f99b572df62eef27ad","13a45367950e4219bcfbde79e563a2b4","ef681e23b32747488e16932172427607","18f693397a0547d4a978c8017b25c096","ece25c0b636b401c96fc5fdb4d0fd29b","f9065a0b127743ad907b8cbd0cd0d68f","a960ff873a17481b9c97861d8ad2c223","d1eb604fc3dc485d9c8a817bf75355cc","b8041c22439042b2a6e0f122fc8630ab","c2cff851319d4f29b6e10c6c94c8e8e4","35e0e14b19564c2c933542a2e294c5f9","b7a552aaa90943ca8a7d849d663d4694","1000c1b4116a4a4cb8cad27217d54621","846f265d62d044f68101b21d2be53db4","23e8b7bcde864c75b8ea8fc97f5156af","922966fd5abc4fc89b61f54d24fb975c","121dab0b38bf499d8c9dd8463cf7c425","42a75286cf4c4772a405f03331e2fdca","e2295aba91734a62b809885394ab212c","f5f65a111eca4bd49b9694743e76ff2d","67700ed42c764cf9bca1fa909ab423f6","4c17e588936f429e8b8d590e8a5121d2","30c08e6749754b6abf96136b6c69043c"]},"executionInfo":{"status":"ok","timestamp":1722629951526,"user_tz":240,"elapsed":7056,"user":{"displayName":"Xin C","userId":"03164725939202625018"}},"outputId":"2c9e22d7-59ab-4932-b858-82b7df7a985b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"70d24f492ef8498295ab493ded05a16a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"59d9d7a009424ec7b5a04262105cdd82"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"577e374fa2af4cf3a5e9392aef05e02a"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n","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":["model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"13a45367950e4219bcfbde79e563a2b4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1000c1b4116a4a4cb8cad27217d54621"}},"metadata":{}}],"source":["# Load the T5 tokenizer and model\n","model_checkpoints =  [\"t5-small\",  \"t5-base\", \"t5-large\", \"facebook/bart-large-cnn\", \"facebook/bart-large-xsum\"]\n","model_name = model_checkpoints[1]\n","\n","tokenizer = T5Tokenizer.from_pretrained(model_name)\n","model = T5ForConditionalGeneration.from_pretrained(model_name)\n","'''\n","tokenizer = BartTokenizer.from_pretrained(model_name)\n","model = BartForConditionalGeneration.from_pretrained(model_name)\n","'''\n","#embedding_layer = model.shared #avilable for T5 models\n","max_tok_len = 150 #embedding_layer.weight.shape[1]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NP_MNsFyzIL4"},"outputs":[],"source":["model = model.to(my_device)\n","\n","generated_summaries = []\n","for article in test_dataset['document']:\n","    inputs = tokenizer(PROMPT + article, max_length= SUMMARY_MAX_LEN, truncation=True, return_tensors=\"pt\")\n","    inputs = inputs.to(my_device)\n","    # Generate Summary\n","    summary_ids = model.generate(inputs[\"input_ids\"],\n","                                   min_length= SUMMARY_MIN_LEN,\n","                                   max_length= SUMMARY_MAX_LEN) #,num_beams=4 , max_new_tokens = max_tok_len,\n","    candidate = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n","    generated_summaries.append(candidate)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VONKM_AeasGS","executionInfo":{"status":"ok","timestamp":1722630159609,"user_tz":240,"elapsed":29,"user":{"displayName":"Xin C","userId":"03164725939202625018"}},"outputId":"dd0cb65e-ccb6-4023-b580-d3b0e42f9f10"},"outputs":[{"output_type":"stream","name":"stdout","text":["A woman who was seriously hurt in a fatal hen party motorway crash is now helping other major trauma victims rebuild their lives.\n","minibus driver jailed for six years for causing friend's death . 21 women were heading to hen party when minibus was hit by lorry . minibus driver's friend, 18, was killed in the crash .\n","\n","A Tudor manor house has reopened following a £2.2m makeover.\n","bramall hall in stockport, gm, has been restored after a 2014 renovation . 1,400 tickets have been sold out for the opening weekend of the event . the manor dates back to the reign of William the Conqueror .\n","\n","Walt Disney World has unveiled a lighthouse memorial for a young boy who was killed by an alligator while on holiday at the Florida theme park.\n","two-year-old boy was dragged underwater by an alligator . his parents and sister had been visiting the resort in 2016 . wildlife officials classified the killing as a predatory attack .\n","\n"]}],"source":["#check generated summary vs. reference\n","for i in range(len(test_dataset[:3])):\n","\n","  print(test_dataset['summary'][i])\n","  print(generated_summaries[i])\n","  print()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"FwKBV-CEya_I","executionInfo":{"status":"ok","timestamp":1722630159609,"user_tz":240,"elapsed":20,"user":{"displayName":"Xin C","userId":"03164725939202625018"}},"outputId":"0e52e7f3-d10f-4e8b-d69a-88f511647a12"},"outputs":[{"output_type":"stream","name":"stdout","text":["T5ForConditionalGeneration(\n","  (shared): Embedding(32128, 768)\n","  (encoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseActDense(\n","              (wi): Linear(in_features=768, out_features=3072, bias=False)\n","              (wo): Linear(in_features=3072, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): ReLU()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseActDense(\n","              (wi): Linear(in_features=768, out_features=3072, bias=False)\n","              (wo): Linear(in_features=3072, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): ReLU()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (decoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseActDense(\n","              (wi): Linear(in_features=768, out_features=3072, bias=False)\n","              (wo): Linear(in_features=3072, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): ReLU()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseActDense(\n","              (wi): Linear(in_features=768, out_features=3072, bias=False)\n","              (wo): Linear(in_features=3072, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): ReLU()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",")\n"]}],"source":["print(model)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t43keRZlyKWe","executionInfo":{"status":"ok","timestamp":1722630159610,"user_tz":240,"elapsed":17,"user":{"displayName":"Xin C","userId":"03164725939202625018"}},"outputId":"fcf0a24a-7c17-4305-b273-9cd17f8af525"},"outputs":[{"output_type":"stream","name":"stdout","text":["total number of parameters in the model: 222903552\n"]}],"source":["total_params = sum(p.numel() for p in model.parameters())\n","print(f\"total number of parameters in the model: {total_params}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7UZpSMgvdbEA","colab":{"base_uri":"https://localhost:8080/","height":350,"referenced_widgets":["44b3370b998a4a439a54c733d2382309","0c4e624a056142c3b03f924381bf60cb","40c3042284904484ad6c47ae6bf41286","8a35d4feefb74969b802d3edcbe6f4d0","8a8966497aa14daabfa0d323f9fb67ab","1a79b9071d544c5086c055956459fba1","0fc66f1c1a5542f89f95d88b8252380b","ca25d0f1645e4bef84abbf4f9c158aae","f07e6036e8de4777aa165df5fd16f32a","c98ea5aa8c4d4ba1ae271ce7de3ac64e","84587d4401d84598af4d7db8d2383b12","05dc7fb0fef1412280c12282b6e616a4","220b69f167614a2786ea0a7315f8331c","b7ec74d8ce40475d8b86cd0d408aa6a3","32a1d384a8ff45b4853eda48c8a9f426","4cbd6a4e2aa04ccea2a156d3730327fe","dd8986b7b3104ad498d53e29296e904d","59a68082714149deb020ed19706742bb","4658c162a1b04bb698dd44900547a47e","1c5391c6232e44f8a37183e58d040763","f6f926b5b9a3491f99aa47f448232f81","15e11f37769847fb9f093c0b6bc1d53e","a20c0fb574ab4254b34a99e8d8589fd7","d54dfc85a05b4d3a81e242fd29c66652","7b044e985b134489999959a8b4962708","3f9d1c4242ac453cbe58d9ba08e5fd95","8610eb2aeea54edeab4d6044cf0695b2","8e6cdb6f95784a00a93601fe4b59326a","11cd7224f77b45b7bc4cff5aeab5efe6","f28af145f2604fe5b67f251130cdbb97","c1956d8f69d64fde87bfbc8a65f69c32","c1d4e99385e94cd5985f7f5453ea102b","4fa4fe0c352b4f3d9b264b85b7422247","93147e5a10bb46b4bcac73e0d1701dfb","918905fa4aa04f58b035401de876a7ba","a9406f21dbb5425eacb25ee82ffa450c","a10ad10debb4429f92e38f02bac84331","96525b3c0cac456ca0a243b7277bef29","cb8c394402eb4419a18212695bc30ed7","180fd210436749ed91a555e369a837a7","8e7a1a89e7304831a082d22ea19f5084","8a540fbd0c204c7a9636ccaf28400136","4d1091505e344f60ae3f14db59c62ef5","143d7b62e7ae4105bb5de56eb776463d","f37999f053f743599ca4e5a2b2c9e35a","ab9d471d76394f0385e8783e87123551","974c1a48c7894637b9de0eebe79490e5","9e85bc7bb479490794c56c0361f3b60f","b490f00f8141440383775986779232f2","7a7777a871784fd4991deb9b9a8d5437","73a99c84044747389b600de5ad57ed52","60e2d07bf0eb4ffb87427c0fccccfa02","97dd05dd823547adbc076f7db0d2ed4b","ba904d5fdeff42cab31900648cd42e36","13f5c3317d36430c94c5266636b409d1","e1f26b2f6f144e1ba2a84907e2914e7e","3a04f337d29a452d88a3c158efdfbee9","f3594ad202d64f12a420c328fc4935b4","7c0455d9e5f543d1a3ed9a18b8ac627b","78b48180c6194576ad853fbe1fcbd4b3","3ad7d182df254e81b5cef680e6d6903c","0a999f1479fc4f72a48320bf8e79a801","7a521bb952254c54ad4eb7b5e52b88b3","f6e5e5515f264525906e23961fd838b7","f1eb9c9e10404a94a09c6ac11a0205a2","53f26b157da646bca298550932f5626e"]},"executionInfo":{"status":"ok","timestamp":1722630181734,"user_tz":240,"elapsed":22132,"user":{"displayName":"Xin C","userId":"03164725939202625018"}},"outputId":"727a4eea-20b4-4e55-f73f-71acbfa0d690"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.18188465539614695 0.022947052115021786 0.1279343012197626\n","0.005988010635235443\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"44b3370b998a4a439a54c733d2382309"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"05dc7fb0fef1412280c12282b6e616a4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a20c0fb574ab4254b34a99e8d8589fd7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"93147e5a10bb46b4bcac73e0d1701dfb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f37999f053f743599ca4e5a2b2c9e35a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e1f26b2f6f144e1ba2a84907e2914e7e"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["0.8493533194065094\n","0.49085909128189087\n","\n"]}],"source":["#Print performance\n","references = test_dataset['summary']\n","candidates = generated_summaries\n","\n","rouge_outputs = rouge.compute(predictions=candidates, references=references)\n","print(rouge_outputs['rouge1'], rouge_outputs['rouge2'], rouge_outputs['rougeL'])\n","bleu_outputs = bleu.compute(predictions=candidates, references=references)\n","print(bleu_outputs['bleu'])\n","bert_outputs = bertscore.compute(predictions=candidates, references=references, lang=\"en\")\n","print(np.mean(bert_outputs['f1']))\n","print(calculate_vector_similarity(references, candidates))\n","print()"]},{"cell_type":"markdown","metadata":{"id":"apml3PLg0kpl"},"source":[]},{"cell_type":"code","source":["test_dataset[2]['document']"],"metadata":{"id":"Hbyj2YEi0RRA","colab":{"base_uri":"https://localhost:8080/","height":191},"executionInfo":{"status":"ok","timestamp":1722631227059,"user_tz":240,"elapsed":215,"user":{"displayName":"Xin C","userId":"03164725939202625018"}},"outputId":"9f5c9910-f660-490b-e180-006f5c131acd"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Two-year-old Lane Thomas Graves had been playing in the sand near the resort\\'s Seven Seas Lagoon when he was dragged underwater by the creature.\\nHis parents and older sister had been visiting the Grand Floridian resort in June 2016 from the state of Nebraska.\\nThe lighthouse has been installed near to where the attack occurred.\\nWildlife officials classified the killing as a predatory attack, saying the boy did nothing to provoke the alligator.\\n\"He was in the water not more than ankle deep,\" the Florida Fish and Wildlife Conservation Commission said in a report, describing how the boy had been gathering water for a sandcastle.\\nHis father, Matt Graves, jumped in the water to try to pry open the creature\\'s mouth, but \"the alligator thrashed and broke Matt\\'s grasp and went under the water,\" according to the report.\\nA Disney spokesperson said they hoped the monument would spread awareness for the Lane Thomas Foundation, which also uses the lighthouse as its logo.\\nWho is liable for alligator boy\\'s death?\\n\"The lighthouse sculpture has been installed to help spread awareness of the Lane Thomas Foundation, which was established to provide assistance and support to families whose children need organ transplants,\" Walt Disney World said in a statement.\\nAfter the death, Disney was criticised for not having posted signs warning of the danger along the man-made lagoon, which borders Magic Kingdom.\\nPublic notices have now been added to the area, Florida media report.\\nThe Lane family announced a month after the boy\\'s death that they would not sue Disney, and would instead \"solely be focused on the future health of our family\".'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":18}]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 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