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Tim Cook announced this yesterday in San Francisco.\"\n","\n","# Process the text\n","doc = nlp(text)\n","\n","# Display the named entities\n","for ent in doc.ents:\n","    print(ent.text, ent.label_)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fOWjq4mryvtk","executionInfo":{"status":"ok","timestamp":1713123526864,"user_tz":-330,"elapsed":996,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"d28403ed-6151-411c-8ff9-b7145cba0311"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Apple Inc. ORG\n","U.K. GPE\n","$1 billion MONEY\n","Tim Cook PERSON\n","this yesterday DATE\n","San Francisco GPE\n"]}]},{"cell_type":"markdown","source":["Coreference resolution"],"metadata":{"id":"wUUWl05fPDj0"}},{"cell_type":"code","source":["!pip install coreferee"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IxhM8OKcEo2a","executionInfo":{"status":"ok","timestamp":1713123567142,"user_tz":-330,"elapsed":16534,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"1ccfb3c6-50cc-40cc-86be-034b49e1b24f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: coreferee in /usr/local/lib/python3.10/dist-packages (1.4.1)\n","Requirement already satisfied: spacy<3.6.0,>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from coreferee) (3.5.4)\n","Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (3.0.12)\n","Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (1.0.5)\n","Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (1.0.10)\n","Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (2.0.8)\n","Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (3.0.9)\n","Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (8.1.12)\n","Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (1.1.2)\n","Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (2.4.8)\n","Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (2.0.10)\n","Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (0.9.4)\n","Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (0.11.0)\n","Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (6.4.0)\n","Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (4.66.2)\n","Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (1.25.2)\n","Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (2.31.0)\n","Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (1.10.15)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (3.1.3)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (67.7.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (24.0)\n","Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.0.0->coreferee) (3.3.0)\n","Requirement already satisfied: pathlib-abc==0.1.1 in /usr/local/lib/python3.10/dist-packages (from pathy>=0.10.0->spacy<3.6.0,>=3.0.0->coreferee) (0.1.1)\n","Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.6.0,>=3.0.0->coreferee) (4.11.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.0.0->coreferee) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.0.0->coreferee) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.0.0->coreferee) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.0.0->coreferee) (2024.2.2)\n","Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.0.0->coreferee) (0.7.11)\n","Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.0.0->coreferee) (0.1.4)\n","Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.6.0,>=3.0.0->coreferee) (8.1.7)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.6.0,>=3.0.0->coreferee) (2.1.5)\n"]}]},{"cell_type":"code","source":["!python -m coreferee install en"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nQ9McbwJHuGf","executionInfo":{"status":"ok","timestamp":1713123626676,"user_tz":-330,"elapsed":56501,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"e06b4377-62f0-40dd-d880-cb61234a70de"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2024-04-14 19:39:35.244325: 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","2024-04-14 19:39:35.244393: 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","2024-04-14 19:39:35.245853: 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","Collecting https://github.com/richardpaulhudson/coreferee/raw/master/models/coreferee_model_en.zip\n","  Downloading https://github.com/richardpaulhudson/coreferee/raw/master/models/coreferee_model_en.zip (65.4 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.4/65.4 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Building wheels for collected packages: coreferee-model-en\n","  Building wheel for coreferee-model-en (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for coreferee-model-en: filename=coreferee_model_en-1.0.0-py3-none-any.whl size=65422507 sha256=fc8eede031dbf318153f8d5f88ea47cfe1f241f9a6aa0991365e178d8346145b\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-8wohsitg/wheels/87/ca/93/b7c91eabb7b65a4200a75ea842a7009810982015d2dedfdc54\n","Successfully built coreferee-model-en\n","Installing collected packages: coreferee-model-en\n","Successfully installed coreferee-model-en-1.0.0\n"]}]},{"cell_type":"code","source":["!python -m spacy download en_core_web_md\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vJ_rtrZkIS4k","executionInfo":{"status":"ok","timestamp":1713123725159,"user_tz":-330,"elapsed":21603,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"e07d7e10-5a9f-4f82-e29c-58800b53ad33"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2024-04-14 19:41:50.736069: 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","2024-04-14 19:41:50.736151: 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","2024-04-14 19:41:50.738141: 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","Collecting en-core-web-md==3.5.0\n","  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.5.0/en_core_web_md-3.5.0-py3-none-any.whl (42.8 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.8/42.8 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: spacy<3.6.0,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from en-core-web-md==3.5.0) (3.5.4)\n","Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.0.12)\n","Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.0.5)\n","Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.0.10)\n","Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.0.8)\n","Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.0.9)\n","Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (8.1.12)\n","Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.1.2)\n","Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.4.8)\n","Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.0.10)\n","Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.9.4)\n","Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.11.0)\n","Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (6.4.0)\n","Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (4.66.2)\n","Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.25.2)\n","Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.31.0)\n","Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (1.10.15)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.1.3)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (67.7.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (24.0)\n","Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.3.0)\n","Requirement already satisfied: pathlib-abc==0.1.1 in /usr/local/lib/python3.10/dist-packages (from pathy>=0.10.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.1.1)\n","Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (4.11.0)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2024.2.2)\n","Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.7.11)\n","Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.1.4)\n","Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (8.1.7)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.1.5)\n","Installing collected packages: en-core-web-md\n","Successfully installed en-core-web-md-3.5.0\n","\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n","You can now load the package via spacy.load('en_core_web_md')\n"]}]},{"cell_type":"code","source":["import spacy\n","import coreferee\n","\n","nlp = spacy.load('en_core_web_md')\n","nlp.add_pipe('coreferee')\n","doc = nlp(\"Apple is looking at buying a startup. They are interested in its AI technology.\")\n","\n","print(doc._.coref_chains)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"avL7LT0CHy1Q","executionInfo":{"status":"ok","timestamp":1713123729239,"user_tz":-330,"elapsed":4084,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"29392592-24d0-412b-dc91-865fed29d25d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/spacy/util.py:910: UserWarning: [W095] Model 'en_core_web_md' (3.5.0) was trained with spaCy v3.5.0 and may not be 100% compatible with the current version (3.7.4). If you see errors or degraded performance, download a newer compatible model or retrain your custom model with the current spaCy version. For more details and available updates, run: python -m spacy validate\n","  \"\"\"Check if string maps to a package installed via pip.\n"]},{"output_type":"stream","name":"stdout","text":["[0: [0], [8], 1: [6], [12]]\n"]}]},{"cell_type":"markdown","source":["## Early attempts of fine tunning"],"metadata":{"id":"pJtZAbjNC7ZJ"}},{"cell_type":"code","source":["%%capture\n","!pip install datasets torch"],"metadata":{"id":"-c8sv0SNaixI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from datasets import load_dataset\n","\n","dataset = load_dataset(\"xsum\")"],"metadata":{"id":"-rBg1X5la-oR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714447245315,"user_tz":-330,"elapsed":4514,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"01350084-fb62-47f1-a162-46694d5b1ac4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for xsum contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/xsum\n","You can avoid this message in future by passing the argument `trust_remote_code=True`.\n","Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":["%%capture\n","!pip install transformers[torch]\n","!pip install accelerate -U"],"metadata":{"id":"xzCAPQ5WAT0t"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def preprocess_function(examples):\n","    inputs = [\"summarize: \" + doc for doc in examples[\"document\"]]\n","    model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=\"max_length\")\n","\n","    with tokenizer.as_target_tokenizer():\n","        labels = tokenizer(examples[\"summary\"], max_length=128, truncation=True, padding=\"max_length\")\n","\n","    model_inputs[\"labels\"] = labels[\"input_ids\"]\n","    return model_inputs\n","\n","tokenized_datasets = dataset.map(preprocess_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168,"referenced_widgets":["20f0d9c8e7544e869707fa71353aa57a","92da419029e8424da4db7c94be622711","58cb7af253ad407ca1f51d6f91bfcf5a","6fe8c35212694b5a917e45c4de44892c","36fc1576f135445f8400afed03a9f1fc","3d78c88ba0df4d4ba01a7068bace6b25","c28decd0a47744a6acb9bdabf623900d","44598fec7ce64dde93e0174abb5a4a04","3e60a9abfd4b4afeb6f48bff19fe8469","ce29120a2c174bbeaddfb7051f217b26","080a5823c95346d0b7b46b3059718787","74100d5d014949f08731e856e267bfa5","3499f85e93514b2d94ae2bddf5604d26","9cefb2b1279640419904c42c96c43b2f","6a06b43f6f654e8698a0333c30163b5c","60d4db9fcc0c477ca73ec21c92837cdb","9c309b46900d43cd86c29ed1d687c76f","4a262845237d42c0bc98841c5d96f80a","14e1ef2c53574c469b2f65ffa0fa27e8","eaf5ce63075c48dfaaeb9953b089d70a","acd25652be58442b940b3a7446ff446e","f981dda9b1a840e1b8a3b6ce1f2d120c","65bc26c4ccbd43b68fb4bd2cfdf54692","3dbb6af16ef24f0b8790e27d35330867","1f5a816c06874b2b9c7d84913a07159f","bc36fdefd86c4d92a7102f033f4de68c","c8d69247bde444e9a63b071c28996983","384020e96cd743e3b2e5fbb892321861","6898f5781ae8411fa055462a2c158711","b661e5cb36d64d9cb39ebdea470ffa06","b845a387a8ff4be58af9eab1a492085d","b2e808cf358b4b668bf1152b136f2320","e0d9ba4847dc4b0e9f476d949ebe23d9"]},"id":"sfzuoO72BHwg","executionInfo":{"status":"ok","timestamp":1714448270624,"user_tz":-330,"elapsed":860598,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"5de52326-3dd3-4792-9c87-36fe8bfe3e6b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/204045 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"20f0d9c8e7544e869707fa71353aa57a"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3921: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/11332 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"74100d5d014949f08731e856e267bfa5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/11334 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"65bc26c4ccbd43b68fb4bd2cfdf54692"}},"metadata":{}}]},{"cell_type":"code","source":["model_to_be_fine_tuned = T5ForConditionalGeneration.from_pretrained(\"t5-small\")\n","\n","from transformers import Trainer, TrainingArguments\n","\n","training_args = TrainingArguments(\n","    output_dir=\"./results\",\n","    evaluation_strategy=\"epoch\",\n","    learning_rate=2e-5,\n","    per_device_train_batch_size=16,\n","    per_device_eval_batch_size=16,\n","    num_train_epochs=3,\n","    weight_decay=0.01\n",")\n","\n","trainer = Trainer(\n","    model=model_to_be_fine_tuned,\n","    args=training_args,\n","    train_dataset=tokenized_datasets[\"train\"].select(range(1000)),\n","    eval_dataset=tokenized_datasets[\"validation\"].select(range(1000)),\n","    tokenizer=tokenizer_before\n",")\n","\n","trainer.train()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"6-sGaKTNI7Om","executionInfo":{"status":"ok","timestamp":1714285581055,"user_tz":-330,"elapsed":174182,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"0adec535-f692-4b31-e293-1a6eb9a5aeac"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='189' max='189' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [189/189 02:51, Epoch 3/3]\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>3.555805</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>0.859342</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.827318</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=189, training_loss=4.331529405381945, metrics={'train_runtime': 171.8493, 'train_samples_per_second': 17.457, 'train_steps_per_second': 1.1, 'total_flos': 406025404416000.0, 'train_loss': 4.331529405381945, 'epoch': 3.0})"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["fine_tuned_model = model_to_be_fine_tuned"],"metadata":{"id":"2b1hnUF2HmJX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Evaluate the fine-tuned model 1000 examples\n","fine_tuned_scores = evaluate_model(fine_tuned_model, tokenizer_before, dataset)\n","print(\"Fine-tuned Model ROUGE Scores:\", fine_tuned_scores)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wUzGly00Xfnv","executionInfo":{"status":"ok","timestamp":1714285748202,"user_tz":-330,"elapsed":61855,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"8ebf5c5d-3052-4427-c778-e8611cc50382"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 83. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=41)\n","Your max_length is set to 150, but your input_length is only 78. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=39)\n","Your max_length is set to 150, but your input_length is only 93. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=46)\n","Your max_length is set to 150, but your input_length is only 119. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=59)\n","Your max_length is set to 150, but your input_length is only 94. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=47)\n","Your max_length is set to 150, but your input_length is only 101. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)\n"]},{"output_type":"stream","name":"stdout","text":["Fine-tuned Model ROUGE Scores: {'rouge-1': {'r': 0.22063405265145067, 'p': 0.11705052019213884, 'f': 0.15085126782756195}, 'rouge-2': {'r': 0.021389659535311707, 'p': 0.009645475063951777, 'f': 0.012978985934649568}, 'rouge-l': {'r': 0.18195206454936566, 'p': 0.09598073282756586, 'f': 0.12376672617136635}}\n"]}]},{"cell_type":"code","source":["model_to_be_fine_tuned = T5ForConditionalGeneration.from_pretrained(\"t5-small\")\n","\n","from transformers import Trainer, TrainingArguments\n","\n","training_args = TrainingArguments(\n","    output_dir=\"./results\",\n","    evaluation_strategy=\"epoch\",\n","    learning_rate=2e-5,\n","    per_device_train_batch_size=16,\n","    per_device_eval_batch_size=16,\n","    num_train_epochs=3,\n","    weight_decay=0.01,\n",")\n","\n","trainer = Trainer(\n","    model=model_to_be_fine_tuned,\n","    args=training_args,\n","    train_dataset=tokenized_datasets[\"train\"].select(range(10000)),\n","    eval_dataset=tokenized_datasets[\"validation\"].select(range(10000)),\n","    tokenizer=tokenizer_before\n",")\n","\n","trainer.train()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":345},"id":"oT3-i4_hZkp-","executionInfo":{"status":"ok","timestamp":1714287554300,"user_tz":-330,"elapsed":84980,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"ce848273-5890-4493-a1b6-f1b7669e6cc1"},"execution_count":null,"outputs":[{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='1876' max='1875' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1875/1875 25:41, Epoch 3/3]\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>1.891800</td>\n","      <td>0.652877</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.722000</td>\n","      <td>0.638455</td>\n","    </tr>\n","  </tbody>\n","</table><p>\n","    <div>\n","      \n","      <progress value='253' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [253/625 00:56 < 01:24, 4.42 it/s]\n","    </div>\n","    "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1875' max='1875' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1875/1875 28:03, Epoch 3/3]\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>1.891800</td>\n","      <td>0.652877</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.722000</td>\n","      <td>0.638455</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.701400</td>\n","      <td>0.635526</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=1875, training_loss=1.0237699869791668, metrics={'train_runtime': 1684.0837, 'train_samples_per_second': 17.814, 'train_steps_per_second': 1.113, 'total_flos': 4060254044160000.0, 'train_loss': 1.0237699869791668, 'epoch': 3.0})"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["fine_tuned_model = model_to_be_fine_tuned"],"metadata":{"id":"cLpIT0bmYF-S"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Evaluate the fine-tuned model 1000 examples\n","fine_tuned_scores = evaluate_model(fine_tuned_model, tokenizer_before, dataset)\n","print(\"Fine-tuned Model ROUGE Scores:\", fine_tuned_scores)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714285748202,"user_tz":-330,"elapsed":61855,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"8ebf5c5d-3052-4427-c778-e8611cc50382","id":"Fa8_srDmHiiq"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 83. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=41)\n","Your max_length is set to 150, but your input_length is only 78. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=39)\n","Your max_length is set to 150, but your input_length is only 93. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=46)\n","Your max_length is set to 150, but your input_length is only 119. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=59)\n","Your max_length is set to 150, but your input_length is only 94. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=47)\n","Your max_length is set to 150, but your input_length is only 101. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)\n"]},{"output_type":"stream","name":"stdout","text":["Fine-tuned Model ROUGE Scores: {'rouge-1': {'r': 0.22063405265145067, 'p': 0.11705052019213884, 'f': 0.15085126782756195}, 'rouge-2': {'r': 0.021389659535311707, 'p': 0.009645475063951777, 'f': 0.012978985934649568}, 'rouge-l': {'r': 0.18195206454936566, 'p': 0.09598073282756586, 'f': 0.12376672617136635}}\n"]}]},{"cell_type":"markdown","source":["## Best fine tunning attempt"],"metadata":{"id":"MwNyNbIzKMh1"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"PGMROQyVBYBL"},"outputs":[],"source":["%%capture\n","!pip install transformers\n","!pip install transformers[torch]\n","!pip install datasets\n","!pip install evaluate\n","!pip install rouge_score\n","!pip install nltk"]},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","notebook_login()"],"metadata":{"id":"YowqSG5lCJnV","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["45eead7d933d4bf9a501fd921ac4fdd0","093bf98f065a43f89a8284ed0af96914","d6f05c1fbb264a379d99f545742d8c0f","dac5f1c67298478b810df4ab2be5df79","37ab5c7349864afb9e34259272822b1f","09934396b13a42669a38e89e8e755cf9","8fb91d1082a84b46942f55901609e653","f35022bf04dc4d1e896f384a4883638d","cbfcbbb072c04f68977f066699d2151f","7e0ab09591104477a7b51d5174ccd29d","75f70c05e3154885a584bb738c3e31f4","0fc4363b442d4c9986961a2524240c5d","3d81517133d54d209fb72b3b96876819","5ec2a65becd148378e6a0907a15478af","e04430477ae0481c8fdb74373681d5cc","461de57d8be84bcfbe9495645e672ccc","bdd432b531f2497eb1cd51974e7df7c7","c0d025c12d2648fe8c0fb5fc4689705a","49d4be3eed594a818e295c6b30c70c30","8c3578a25ba64386b6349429f159331a","c83ac5801fe840abadb2950cdc5bc756","ea3820566f9c4e36a699d37122d4f0f6","1421d7c06e8a4c03a208716b8eb57301","e90f0c86d33a4d92a2e955b1ea250e6a","cfa156568c9f44ef900d804b9a7add48","7f74eddeb3ac4e9f806dd4c4bed6f4df","20c3444cf299477fa769dddddf9a30ed","a69af6ce09ea4f738e14c9d6be738ce0","06ac0fb8cb454980ab23eb5ff3abc266","19736287a7b24e4f92dab4e9c34efb1c","008e7cbffc19495fb912f0dc57cca6a9","e880834edaa84d0fa6641f10e2b4563f"]},"outputId":"306e8850-328a-40aa-c8e7-dc57ed9bd5a7","executionInfo":{"status":"ok","timestamp":1714418619278,"user_tz":-330,"elapsed":6,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"45eead7d933d4bf9a501fd921ac4fdd0"}},"metadata":{}}]},{"cell_type":"code","source":["from datasets import load_dataset\n","train_data = load_dataset(\"xsum\", split=\"train[:20%]\")\n","test_data = load_dataset(\"xsum\", split=\"test[:20%]\")"],"metadata":{"id":"spFeYzhoCJlO","colab":{"base_uri":"https://localhost:8080/","height":657,"referenced_widgets":["9ab1db48190746519d7ad576fe3c187b","dcc89b918c6341b39d913f30041ac935","06e764d5ebf246f1952131254bf5bf0b","4631728d684043d8a607705a09f4ad76","9aec769aec314640a1ed92b4d9085978","04db602b8d344090ab850de2d80f6987","7f1bda90adec4cdb89a1588dfb79bd96","9d8dc4ec609d40bb8e265096ebb19a97","d7e9a7141ead4a3ba6c31f65aa9c5450","fed4cb32534f4e04bf1f449549e5a035","528aada1a52d495ea9c38b5771d7db1d","47632a8f3ca64edb9331bd2dce84bb78","cdf645d0223d43ac81b07de9519ccf1d","80bcdac7b27c4115b89d6bebb695bb59","c9becdb02ead4f39897ba577c7ba85c6","376ba312610340a1a95b148b15ae824a","f83ba04248c548198d7240ff325a4ac9","dfd1fe8773164040b6e221e27f74d289","cd64f826dcd24e1b992549e5b7fec763","fbf4313af75d49cd88d2ebe319e7caf7","4fdaad3fc88f46f18f067209eb46315a","0d1c38159e0b4088a4bae7515a3c91f5","8ea59e4dd41f450e97ec9c254d895d2b","a207d569bf8043b9ac27f90cc7e08fee","bf51b51c3ad54571806be45f87fae47f","72a326ca16a74a158602eabac720a1ba","60b0d80de40347078f794e00ac993136","cccfc503d5254c86ae3f2ba8f503bd2e","ce44babff259423ba200065f82ef688d","8286a164d5c54a0ab93a6fef1ceee6f5","0123f4d721a1461898668a79f7d07f8a","dc13748a76de43ccba670cd4da936778","bd66606b73384d09bf04fadd45bb9420","e88a8560a9e94ac0bf997b33a00483d6","213e73e35b84466cbfe213b849310662","c0f2a1942d9043858d1b840cb1d7b4eb","6a25ba69cfa2428bb5c32afbeef5fece","6543dad1dd8c425ea4715e17b432161a","73cdf28081e24012b1423d08b5868ec6","11963162a3b0405ab9c599bd57cce826","3a606f7df4e74745bcfd2403327f273a","67611ef935ca4eb2aa30a62b2bae4a12","6536819633a549c288fb73badfb382d0","90998d9063484003aa1cadaafd5a3574","50a168516eee4f23aa4616b3313d16df","03617076da694370bf83b79a250b6b15","f41060d31cfc40edaa742f35369bde62","5391b405e47c41928954d521f9f4889c","3c9f399d0ab24dac8461ea3199ff7259","42d10e267a584d64a3440ea2e02dbbad","8948f34da9254d2fa2c091e16a166d44","72e8759b1b5b4d228cb209c3637f4467","6d3b4b5acba7493cadc1c87359c4bd3a","ea010f9a414b4c809d92d0fcc48c0d89","b5278e92335b486397d64acfe9355738","94657eb59f874afb91f28bebadc02bd2","fcdb3f8d9fa443daa7141b2a52eff904","3cb3fa0f4aff4a178e2988ed12732cd5","05ab33f515a24f0a84501019f1ef4a3c","2241381a308e46f08ec470d7d9095963","eca0d25f4cbd4e6587ef7274e6ce102b","90ff2b03b01b415ba45ee77968cecf96","279d18075dff45a1848214b6e0323141","68a9b8cce84344baa49382089b5afded","0652ee8c5fa643dd8a3575727aef70dc","5533b8c29fb842f29e7e22b0725fe34d","14880bf583cd43b5b5f4726bc874dc01","95ef44b9284a47548e79bbdfc7524134","9d1e426e35064fd4b2e66e2afa4820f1","ee619bebe340474d94d43c4b7f8039a5","662d6a4b2e5848d1ac14bbed723d4431","71ec18c5201f4458a50cfad89a4ab79a","2f6ba5a4ab6045fa869f5a8dc7d13f60","ccde0807b0f04b7f81f0a6b3bdc3f687","f0c55115833449c3b00f522f31ee4a71","193bd1adb8f643de98dce3143bd33652","da3acdeef4af411b96339d4723c2e068","1e39ad8d30a94d008924f886e07a9765","f15d79338e974c29a9116e260c621feb","5ebbde98d1074fa9901cabc90058e52d","2afeb7fe0c894719822fc6b66c4b98ec","47ebab7c397440dea26c4bc4992a3d4b","4939ce134a12426288974d6a3e2a5e3f","788ad50e7985423a927d3477b26aeeaa","f35689c587a34cb8b9b195c4cec46a21","da03aa4caa154cf0b4a62c5b1145b30c","9d6d360621054570bd163d6f728ca694","9c7a058f515b4587800e260385e10d3d","6f4bad5156ff4dc3a7b6ea5b90b23e8a","f6e3707b2ee846f7a09c846ee7bffefd","85a749f3bf284b6cb4a032ca218524d4","fa0fada8996c4132b9c2baf894f1be7d","3ad7dd936a804e10ad2c7191e2b11bc6","c9d154ccc3ed428c89861f26e14cd2db","8e9b19dca7184523bb7087d3a1131c90","e03cdd6a06aa462ea884c6bf9892e813","ecb9499c79664f9480a79fdc038c39d4","076f491651d64e4cb35ef6294f09dd63","f3ec0086280b46cb9f68952f3f73a389","7545532e55ff43b48272eadc872140b7","04949a49c73e41c997e35690c66ce9b4","9cdb264b77984ed2a7bee01ab3557ef4","8c53d9989c554d708eb3fac70ce739a4","ee50006abccc4a179fd3d2cb2438be6f","f18f7801dedd4e4c8a697f55b0bb7593","4a2db39dbbb944b8b90a23d13ac841b3","92b4e84d3b4548b48b6fde7dd26cb1c6","2d380e7d890345cc8d63d0f2f6c7fd31","14b198e001444f9b8aca9f2bdc2d2da4","7da8e58eb68b47c996eed674f189228c","1ad39e0ad6744e26abd26d37297dbf63","13cde32934ee4e7098b9b7a429759f61","9209ba4d971f401cb56168a881df9efe","bc3dadf326ca4ddd938396c6f1764fa4","832d7a9c4bd14b938e5dbe6bf85b1650","b756fa5ff5384cffb1c8d971f5c70964","e9ae5a7aa3704824b7c6151a413561cf","3230094c8f1e44faa776ef6e7fb2d67f","dc828673fd634a9abec26fa3295453ef","0ee6b2499b684a94a94341b4fb92cbc9","19c0df932d8d49259cab5a3431edaae5","b1dcf063782046e297a5853c72c5f252","b63caf36eb774eca9c333400b1f87d9a","843d2b93783846debe6af67b21a2fdd4","4347a05a7e684ce4b3f84ff66965f4fd","197f19ad82054a298061929369c76fcc","19b5070c356e431cb3574c7b2fffbccf","248fe6838d9f4cf09cce71a38a8f642e","b670e140a722450d9589d94014059d32","baf1ff976b874838bb31e6082417f601","aebc3eba13d748d8b9d6048c5c2e9e05","b09d97d3a18d4f229122a2b9c007acf9","49fe9234aed04db0bc870c7a1fbf150a","14c9cea5c7854e798cbc2c2931e8e296","4c62f85c3cb048dc8e1282ba7ab2dc24","8da2b25cdfc0497fbd2c5b0de94b0b4a","d503d0a29e784afc8aeb29f62a5364a4","79fb65e34f4e49318c1de6ba3e1890a4","0471e4222fac47b381c8aedfc91c8aca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examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"625b76d71a6d481da40fbff4682d8aa0"}},"metadata":{}}]},{"cell_type":"code","source":["prefix = \"summarize: \"\n","\n","def preprocess_function(examples):\n","    inputs = [prefix + doc for doc in examples[\"document\"]]\n","    model_inputs = tokenizer(inputs, max_length=1024, truncation=True)\n","    labels = tokenizer(text_target=examples[\"summary\"], max_length=128, truncation=True)\n","    model_inputs[\"labels\"] = labels[\"input_ids\"]\n","    return model_inputs"],"metadata":{"id":"XOPFiHmalJ8d"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_train = train_data.map(preprocess_function, batched=True)\n","tokenized_test = test_data.map(preprocess_function, batched=True)"],"metadata":{"id":"Yb0_YL1wCJaH","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["b3110ea0c0f443478ade6312fb9b22f7","74d297a0b7b84f8f94112fe5c3b3475a","90e458ecde684a7d87ebe73dcd8535db","14bb7d168b54460e8a36e2295bff6828","af90a744b9d342019a813cd34587c4ba","dea9ba6a92b24e409ea36a74ba618408","530d93705ead4c1d9e9accfb2a51af83","a39c68002ecc4fc78e8ca4ae0b35bd74","31948da67e6040d29f97f979f4955b08","1ababa67a0ee4b679204d59d3d4f6751","09c8a77c888244c297ef5ab58d291f5d","b2a9ccac6c8d42788f8cdb87f3722200","72d6028a5cce4baaa6fff3f55ffcc302","fd409a40a84f433ab2ad860ac6f60176","5e92fe539c8d4e818345c644770488d2","916ddebc56a347019f3ba4857036460b","4425e36cf9b1442699be4f7d843e7d76","b281cdfc3df447eda6f25eb0cbac45b7","1064ddd4fc254f5f9a21d4527bd32c61","7f431c80fb3c4a6c8002c6ab42fdc6a8","fdfd31332802410dbf4c33f8733e8256","2a059ebfd885499d9e05c18f75238b9d"]},"outputId":"65dcd4fe-2a19-485f-8d70-6afe3ebf7d00"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/98232 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b3110ea0c0f443478ade6312fb9b22f7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/4565 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b2a9ccac6c8d42788f8cdb87f3722200"}},"metadata":{}}]},{"cell_type":"code","source":["#\n","%%capture\n","!pip install transformers"],"metadata":{"id":"jwGhuVQqbmIg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#\n","from transformers import T5Tokenizer\n","from transformers import T5ForConditionalGeneration"],"metadata":{"id":"CZfrhCBLhU-0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#\n","model_t5 = T5ForConditionalGeneration.from_pretrained(\"t5-small\")\n","tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168,"referenced_widgets":["52b17e8941294521a897819f6f93c471","186d4b71f58f48a0bbf15a526da72882","06fe8bc495a249b99bbab146a3096b72","dcaf7396fc48455b9e7da76ce4874e16","237fddb339a34bf6b2c51b127d260304","2007883160544384af58a739ee0d2c83","6aa7c1a59b4f4b64a7961ae8320d4184","dcd48e29860e4e5eade9925afdbfae56","cd154830ce674ede8f4dd82dbce32fad","65e2d52387a046b6b92c4581d15e3512","3ad8825a399a40cfb4b880004dd01363","7647acf5f6bb44698ce184f4a66988e8","5815a77ae2684d7c988f97dd4f172a9c","7807b74373e946f1912398f26bc562a6","95790659417849c386c33889993d3630","e02b5c15989a4caba29d2c223ee64ff1","1d4d12b5cc4540119e054baaea1c13e4","f4504de16da142cca9bd7909f9b13367","7fdbbb7abd1043dfad1366411286a090","68518f4128c04f09aacbfc8433423043","43ef57a64bd047b0a8e5b7da1fff3e94","a19e391623f8439990eaa6f213239e6a","9bcd7ddf44764c6aa8f21a77a285bb63","0458f9010eb1412897655218b21446e9","4d32fb837c8f4e6f8ba7dcb86fad387e","3f531303510b46b1bb9c05e8bce5032e","4714a7123695475f992cd9b106551b75","8075298488af435e944defb8c9d0dd0f","c62d0778edeb4c92bcf1645da51683b0","26beb9fbb03b413d9b6dd26b871ab9aa","985568188284458cbdc2650b67d45afc","31bd300df65942e1b6307de6ba37c346","b2e01d86556147d892abd53dc5ca438e"]},"id":"VAPs47ithxAJ","executionInfo":{"status":"ok","timestamp":1714447369512,"user_tz":-330,"elapsed":2012,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"587968c6-9406-4956-a20e-7093abe01afd"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"52b17e8941294521a897819f6f93c471"}},"metadata":{}},{"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":"7647acf5f6bb44698ce184f4a66988e8"}},"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":"9bcd7ddf44764c6aa8f21a77a285bb63"}},"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"]}]},{"cell_type":"code","source":["from transformers import DataCollatorForSeq2Seq\n","\n","data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=\"t5-small\")"],"metadata":{"id":"FZBFmu8BCJX3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import evaluate\n","\n","rouge = evaluate.load(\"rouge\")"],"metadata":{"id":"IM1SKuUBCJVU","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["7c4bf6a93bb1449f9f63dd4ee74205ca","d67e75a985bc490d91aa1e276a0835b5","49a6caf2ee934a3e94eb4fbc1aa979ca","c5a0e908194e4249a6ea78138703dfe1","fe87b18a514b4b96a26a28ba312aec11","de03b52956884818a0586b165eab6c25","174303de0e084bceaeffb3efd508ec8c","d7a645a0c53045a485c6873a1c91dd73","c30852de7da548a097c8b57164967281","7d3e3fd854084968a39da24c3542ccce","94b3c85c69ff43f08590282cc500b8ce"]},"outputId":"f237d6c8-4a5c-4cb2-e859-6b2a7264b071"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c4bf6a93bb1449f9f63dd4ee74205ca"}},"metadata":{}}]},{"cell_type":"code","source":["import numpy as np\n","\n","\n","def compute_metrics(eval_pred):\n","    predictions, labels = eval_pred\n","    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n","    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","\n","    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n","\n","    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n","    result[\"gen_len\"] = np.mean(prediction_lens)\n","\n","    return {k: round(v, 4) for k, v in result.items()}"],"metadata":{"id":"7mFQYQYwCJTN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n","\n","model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")"],"metadata":{"id":"6bpI2E2xCJQ3","colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["3a8697957bc84cc4b15bc46012c3f237","e186e0f1895b4dc6afc941267bc3b630","9dec318a67f44846ad52918ac8148c5d","c3ee956a4e884c3280b7a1d6d7f108c1","7826125d4697411bafcd5d94357b6b1d","2514d11467564b319fac8d01525ba1d3","10f8a07b7b5c44ee8874f2543b6daae5","adf6f8e31f8c4520923ac1324448ae73","b1bd4ad52fcd481dbc5450b96c2a9bb3","3d91a15cff65480aacb5869782e980de","63490e4ce9994c7d847a515b012f7977","0f009bcd94be4c6d9ef245ab905090de","9a3c4e1259be45a692944f9597878325","f5019949418248b192b1cc7b1aae735a","92727780db3a4801ba2c51232be6e6a0","d763d2d76f47461cb057ed2aa9196d39","ae8610bfd1aa4167b8a9eb5bc8dd0a17","0a32974781794e0cb64dc2edaa04b1d6","a9a8552fc47e409dbbc3366ae66f2b3d","f745c15acf4f4f05a1ceecc5820483ad","4f21fa85e4634ec6bc872469b21cdc8d","3525b1b47aa74fd69ab4a62d3c83e619","fce4f4e7f59c4ba8bdaf90930fcd1552","cfc1a52b170c46ee8ab68239de2824bb","6f0aeb2a24144b66b3849fafb239eab6","6aa6b4b72c864af0a5b47917370f77a0","ff05ed7da726469c819a6a4cb9a40cd5","dadafeee15e34d54a6193ef360f1ef5e","1b811c4aa914427aa0550da89f2c5296","b6e3f582f43340d6b2aa2cfdfdeb2aae","d8f31bd2235845cab523337e36ba199d","d525f84dc7dc434380eea2be23bc1538","c41e7118b24d40178d04d2b13dff8c8e"]},"outputId":"33de21dd-0479-46c7-f0f0-13862c8079e9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/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":"3a8697957bc84cc4b15bc46012c3f237"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0f009bcd94be4c6d9ef245ab905090de"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fce4f4e7f59c4ba8bdaf90930fcd1552"}},"metadata":{}}]},{"cell_type":"code","source":["#mount google drive to save the fine tuned model to\n","from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z0OLXh_vTU-B","executionInfo":{"status":"ok","timestamp":1714432122573,"user_tz":-330,"elapsed":3648,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"860d77ae-aca3-4c9b-a967-cf0564e1d5f7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["training_args = Seq2SeqTrainingArguments(\n","    output_dir=\"/content/drive/MyDrive/MyModel\",\n","    evaluation_strategy=\"epoch\",\n","    learning_rate=2e-5,\n","    per_device_train_batch_size=8,\n","    per_device_eval_batch_size=8,\n","    weight_decay=0.01,\n","    save_total_limit=3,\n","    num_train_epochs=4,\n","    predict_with_generate=True,\n","    fp16=True,\n","    push_to_hub=False,\n",")\n","\n","trainer = Seq2SeqTrainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=tokenized_train,\n","    eval_dataset=tokenized_test,\n","    tokenizer=tokenizer,\n","    data_collator=data_collator,\n","    compute_metrics=compute_metrics,\n",")\n","\n","trainer.train()"],"metadata":{"id":"IUYXiPzgCJIF","colab":{"base_uri":"https://localhost:8080/","height":412},"outputId":"3bff5ac5-a792-4ebe-ba71-7571aff45a67"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='24560' max='24560' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [24560/24560 5:58:17, Epoch 4/4]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Rouge1</th>\n","      <th>Rouge2</th>\n","      <th>Rougel</th>\n","      <th>Rougelsum</th>\n","      <th>Gen Len</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>2.178900</td>\n","      <td>2.262195</td>\n","      <td>0.250100</td>\n","      <td>0.077700</td>\n","      <td>0.196000</td>\n","      <td>0.196000</td>\n","      <td>18.862400</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>2.149500</td>\n","      <td>2.243245</td>\n","      <td>0.254600</td>\n","      <td>0.079300</td>\n","      <td>0.199300</td>\n","      <td>0.199300</td>\n","      <td>18.868800</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>2.128700</td>\n","      <td>2.236404</td>\n","      <td>0.255500</td>\n","      <td>0.079500</td>\n","      <td>0.199400</td>\n","      <td>0.199400</td>\n","      <td>18.874700</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>2.148500</td>\n","      <td>2.232910</td>\n","      <td>0.256600</td>\n","      <td>0.080400</td>\n","      <td>0.200600</td>\n","      <td>0.200700</td>\n","      <td>18.890300</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=24560, training_loss=2.1661259269093067, metrics={'train_runtime': 21500.6172, 'train_samples_per_second': 18.275, 'train_steps_per_second': 1.142, 'total_flos': 1.0627518714976666e+17, 'train_loss': 2.1661259269093067, 'epoch': 4.0})"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["from transformers import pipeline\n","text = \"summarize: The Inflation Reduction Act lowers prescription drug costs, health care costs, and energy costs. It's the most aggressive action on tackling the climate crisis in American history, which will lift up American workers and create good-paying, union jobs across the country. It'll lower the deficit and ask the ultra-wealthy and corporations to pay their fair share. And no one making under $400,000 per year will pay a penny more in taxes.\"\n","summarizer = pipeline(\"summarization\", model=model)\n","summarizer(text)"],"metadata":{"id":"WjD7iDGFeQA2","colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["d74b6a184de4439a85b0b7ac4229a5b6","6be6ba3bc364411fbda9af686c4f0b4a","d6d1bb07c00347e296d928998fb78a2e","97fa72c068c7481ea45059addcb7bf2c","0a1eb31251544130a760aaf498f45458","8a75ca23bceb4ddbb68a6497e9738f8d","6b1b443074e84bb2ae73e4ba7b70b057","518838a2831043f8afb853fe221172dc","5b31ad16352249f8bf877ec64e070a2e","de56b7a732b042d481823c7941e6aaec","ba88c7f3c5614f2db427efd0abe93169","7f5c42cba35b4713b8b85ae9833a6825","2700b046e8444a7b97868dda78a871b4","42e6fd1ced2545568b882a57847e1ae5","fd76f1ae8490428cb6c789bd8d1d791f","49cabe35c8d042eb9ef30d08ef687983","07ce0283eca84eb79259b0208e7c6118","b1cce2decf7742a1babd3766ee31d3b2","3c8601fe39354857b89088d07da3198e","385d15ccdbee4613a3aac817a0d5e6ee","81c356d9ea7d4eb9b3a2df113da841ff","a7770ca6042d4ed8a1916b43af1d5be5","0677f99d61b4497eb40cfae75208b181","051060dd969144f0900916a9ea98f403","eef449c829c54e728ae759611820704b","469f9be3149744689fca5c11ec377dff","5d6d4449330c47c08d54191263d6483f","13b5223961a34f1a90bcecd57e91a350","3b9efd71d0d14b46a054b85204c5bb70","994420db641542f786874092af61ab63","484513161a8b45009f2abe4d046fd7d0","05def607917e4b738df17ceae3e41def","a7acf17804a4417596867ed7b32abdab","a2b3722946cd4602b314e3e43b227af6","46e70253c97a4b9e89bab722082767bb","0023f312202f4db48fe53680f96f0c77","d6f2b6bde21142788029739e240b08e5","c2603969698a4ea48c440d8370c62c77","d54a4207d0414493b6fac70381946d06","a2325ba1dfaf4a9eab35ebe578802f75","e0f1839579d7479e928bc7c84c57b5fe","0cd446591a9948faa0b650e8de279509","d7d657ef3d9b462eb42cf2ac884aa2e1","f3ddc20b9dc54fbab6caf2a2bffeff7d","d093f1778c644138a8b46ae5d8ab1147","ec6d55c6251544379c64807312914766","04e96a17f56f4ca4b52555d683e9de29","cf2c0d9ee7a1400ca44315b2217aef92","b01f0865cf2f4f86ad32093429ef3298","f5e486253469446f8c717fda99f1b258","8a8248d5e8ef468e939ab31d30664efb","7b6cc59ac78a4c408cb1a9e837442559","358fc2d38d2944fba3d5116c96c19f5a","7d48686bf28d4aea86c0794a6cac876d","e47419ca18264cb6a5ac744fc19c8a5b"]},"outputId":"fa678bb0-327e-45d1-d2ed-a58ee9fbf8b7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/1.50k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d74b6a184de4439a85b0b7ac4229a5b6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/242M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f5c42cba35b4713b8b85ae9833a6825"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/20.7k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0677f99d61b4497eb40cfae75208b181"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a2b3722946cd4602b314e3e43b227af6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d093f1778c644138a8b46ae5d8ab1147"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Your max_length is set to 200, but your input_length is only 103. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=51)\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'summary_text': \"No one making under $400,000 per year will pay a penny more in taxes than any other tax payer in the U.S. it's the most aggressive action on the climate crisis in America.\"}]"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["from transformers import T5Tokenizer, T5ForConditionalGeneration\n","\n","path_to_model = '/content/drive/MyDrive/MyModel'\n","\n","tokenizer = T5Tokenizer.from_pretrained(path_to_model)\n","model_fine_tuned = T5ForConditionalGeneration.from_pretrained(path_to_model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pknEEFCMZBar","executionInfo":{"status":"ok","timestamp":1714432146472,"user_tz":-330,"elapsed":10807,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"b9515254-c8b5-47e7-f1c6-bc855c8ca73b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]}]},{"cell_type":"markdown","source":["# Evaluating models"],"metadata":{"id":"SbloMEVicgpV"}},{"cell_type":"code","source":["#\n","%%capture\n","!pip install rouge"],"metadata":{"id":"x3CGIWqSamVD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#\n","from transformers import pipeline\n","from rouge import Rouge\n","import torch\n","\n","def evaluate_model(model, tokenizer, dataset, sample_size=50):\n","\n","    # If there's a GPU available, move the model to the GPU\n","\n","    if torch.cuda.is_available():\n","        print(\"GPU is available.\")\n","        device = torch.device(\"cuda\")\n","    else:\n","        print(\"GPU not available, using CPU.\")\n","        device = torch.device(\"cpu\")\n","\n","\n","    summarizer = pipeline(\"summarization\", model=model, tokenizer=tokenizer, device=device)\n","\n","    # Randomly select a sample of the dataset\n","    sampled_data = dataset[\"test\"].shuffle(seed=42).select(range(sample_size))\n","\n","    # Generate summaries\n","    generated_summaries = []\n","    for entry in sampled_data:\n","        summarized = summarizer(entry[\"document\"], max_length=150, truncation=True, min_length=40)\n","        generated_summaries.append(summarized[0]['summary_text'])\n","\n","    # Prepare references and candidates for ROUGE\n","    references = [entry[\"summary\"] for entry in sampled_data]\n","    candidates = generated_summaries\n","\n","    rouge = Rouge()\n","    scores = rouge.get_scores(candidates, references, avg=True)\n","\n","    # Round each value in the scores to 4 decimal places\n","    rounded_scores = {key: {metric: round(value, 4) for metric, value in values.items()}\n","                      for key, values in scores.items()}\n","\n","    return rounded_scores\n"],"metadata":{"id":"Fdm1UGXuZPuF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Evaluate the original t5 model\n","model_t5.eval()\n","t5_scores = evaluate_model(model_t5, tokenizer, dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oFnMURv8a_W0","executionInfo":{"status":"ok","timestamp":1714432740371,"user_tz":-330,"elapsed":351935,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"27f70566-c627-4674-95c2-7ff8b63d88a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["GPU not available, using CPU.\n"]},{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 83. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=41)\n","Your max_length is set to 150, but your input_length is only 78. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=39)\n","Your max_length is set to 150, but your input_length is only 93. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=46)\n","Your max_length is set to 150, but your input_length is only 119. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=59)\n","Your max_length is set to 150, but your input_length is only 94. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=47)\n","Your max_length is set to 150, but your input_length is only 101. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)\n"]}]},{"cell_type":"code","source":["print(\"Original t5 Model ROUGE Scores:\")\n","for metric, score in t5_scores.items():\n","    print(f\"{metric}: {score}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uS_PZ5mxu7y_","executionInfo":{"status":"ok","timestamp":1714432809160,"user_tz":-330,"elapsed":357,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"9f25a1df-b945-4536-d525-045a48601d1f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Original t5 Model ROUGE Scores:\n","rouge-1: {'r': 0.2134, 'p': 0.1207, 'f': 0.1513}\n","rouge-2: {'r': 0.0214, 'p': 0.01, 'f': 0.0133}\n","rouge-l: {'r': 0.1845, 'p': 0.1037, 'f': 0.1303}\n"]}]},{"cell_type":"code","source":["%%capture\n","# Evaluate the fine tuned t5 model\n","model_fine_tuned.eval()\n","fine_tuned_scores = evaluate_model(model_fine_tuned, tokenizer, dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ympV5K4UbO2G","executionInfo":{"status":"ok","timestamp":1714433078973,"user_tz":-330,"elapsed":254147,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"b984bfb3-86b3-452b-a9ea-d6ed61dfb8d9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 83. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=41)\n","Your max_length is set to 150, but your input_length is only 78. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=39)\n","Your max_length is set to 150, but your input_length is only 93. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=46)\n","Your max_length is set to 150, but your input_length is only 119. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=59)\n","Your max_length is set to 150, but your input_length is only 94. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=47)\n","Your max_length is set to 150, but your input_length is only 101. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)\n"]}]},{"cell_type":"code","source":["print(\"Fine-tuned Model ROUGE Scores:\")\n","for metric, score in fine_tuned_scores.items():\n","    print(f\"{metric}: {score}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UlgtvOnfnzz2","executionInfo":{"status":"ok","timestamp":1714433078974,"user_tz":-330,"elapsed":6,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"498e7b1e-2988-457d-9e69-dd758ade64dc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Fine-tuned Model ROUGE Scores:\n","rouge-1: {'r': 0.3132, 'p': 0.2035, 'f': 0.2408}\n","rouge-2: {'r': 0.0708, 'p': 0.0398, 'f': 0.0497}\n","rouge-l: {'r': 0.2577, 'p': 0.166, 'f': 0.1971}\n"]}]},{"cell_type":"code","source":["from transformers import BartTokenizer, BartForConditionalGeneration"],"metadata":{"id":"PtNX5Hrfva_N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenizer_bart = BartTokenizer.from_pretrained('facebook/bart-large-cnn')\n","model_bart = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')"],"metadata":{"id":"_wjOZwI1wHoO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Evaluate the BART model\n","model_bart.eval()\n","tokenizer_bart.model_max_length = 1024\n","bart_scores = evaluate_model(model_bart, tokenizer_bart, dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y5rV3aj1wOND","executionInfo":{"status":"ok","timestamp":1714434282590,"user_tz":-330,"elapsed":1148480,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"3f90897e-1663-4af1-dcca-c1923e9a384e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["GPU not available, using CPU.\n"]},{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 85. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=42)\n","Your max_length is set to 150, but your input_length is only 70. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=35)\n","Your max_length is set to 150, but your input_length is only 145. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=72)\n","Your max_length is set to 150, but your input_length is only 91. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=45)\n","Your max_length is set to 150, but your input_length is only 112. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=56)\n","Your max_length is set to 150, but your input_length is only 87. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=43)\n","Your max_length is set to 150, but your input_length is only 97. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=48)\n"]}]},{"cell_type":"code","source":["print(\"BART Model ROUGE Scores:\")\n","for metric, score in bart_scores.items():\n","    print(f\"{metric}: {score}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"skmtnmMhwnX4","executionInfo":{"status":"ok","timestamp":1714434282591,"user_tz":-330,"elapsed":8,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"6441c108-b9a7-40e8-9e32-64bdceab37c1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["BART Model ROUGE Scores:\n","rouge-1: {'r': 0.2496, 'p': 0.1398, 'f': 0.1766}\n","rouge-2: {'r': 0.0353, 'p': 0.0168, 'f': 0.0223}\n","rouge-l: {'r': 0.2234, 'p': 0.1245, 'f': 0.1575}\n"]}]},{"cell_type":"code","source":["from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration\n","\n","model_name = 'facebook/blenderbot-400M-distill'\n","tokenizer_blenderbot = BlenderbotTokenizer.from_pretrained(model_name)\n","model_blenderbot = BlenderbotForConditionalGeneration.from_pretrained(model_name)\n"],"metadata":{"id":"gRBRefnV0ZaC","executionInfo":{"status":"ok","timestamp":1714445189721,"user_tz":-330,"elapsed":16699,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"colab":{"base_uri":"https://localhost:8080/","height":305,"referenced_widgets":["945c804b6d814ecd80207f3f23833357","579b11023dd14de8915d13dbf3c06dff","35a10ba47d6146728a360575abc04a7c","a8c6926461d34710b2cd86ec1d4ec98c","230d190da90f4535b04921d5a3231547","b4638ec49301415b8aff6073221dc1a9","e947d8ef7bb8455ab688f86c97ca3e10","0535f73426c3474fa2430999daeb4cb2","785bd1c027a842f093f05b253b7e7479","4bd3cb61129c4b87b0e9d26f58edd23c","41af722218ce42cea5011e25855d5481","e3e43ece1add481a8f509f291c2bedc9","9a0366bb9b16435982c99976237cc1dc","53b9c9684ef447c7adb2735372491896","f6e321212f684c5d828ec566676d6896","0f7b0b99321a4036b840895ead970bc9","09964d57dae34319a7484ed2433ee8f4","cfeff85a874a4f6681b61a1e8e93ed4d","7ca5b2a7d2eb4e6fbd22bcda10d43b73","873dac27e5664ab5a6d833aacb784c36","8766d3655ffc4f6fb4d80eb89084e81c","46d2d589e5794ddead3c43c3e419c0d5","2ba50362ac5d4df5ae8305ac34817c70","43196e155626403995239379a3bb9ed0","398d0f78e5af40af840d7fc2e0ba218a","b5428b603137475db68a1f8a0ce0d87d","2f41f9d5191544b58c2cefee3c950bf4","c668b46a0c7f42c587b206b4ce38f700","c1b12b71d0fa47d684d7ff87d1fecf1a","8af4ecd90d854be29b9471df2fdf33b4","debfb4b5f3be44b09c656fe3cd8df488","0fc3f2f9b2374c5f9b29544de66dbfab","c348214819a74f0eb7a0749871030d77","a1852c97b5944d2b80319ea1095a9f46","61e42b43409a435ca242e28ebf67338a","8999cd374e8049e6b20d0d0fa40b2ccc","26b168ff00df46a3af49e131382e61ac","f7a1edf5c4454dd3b693463e3075e0e2","c1b78af05ff942cb8970c3ff20ab524e","5061eec49f434a00b6f258b684fa4118","7e4717960916457898c6e2fd37a7af0b","4a7ed430db0b41fc8819229280c695ba","e55584f1178f4cb8af8f68c1a91ee4e7","02aa3e18de954d3ba7c95081bd926fe4","a2e6b5853fed42cfaed94f59b0907d03","66d19006a4d4469199b6d250790b6304","856cdff8ac2d4624a71f629872abb5d1","3fcd0f3b72d84d94aba59dfb8e311a52","c02fdde433a544359246cf246a270300","8cd9cf1ac4324a4982abc9cac308d135","38761f3c506a45a28427ff07899fcda0","c38c89febd564fb7a653c6502a731752","ad755c4411274c6d9b09d79a36210f5e","4a82beae5bb9454d9f04ef8a1dfcdd7f","deba73628fba458ead7146580f9a9090","be42719328274b5e911119b392a35bff","dd081c5a0b70451d9db102cfb86fb859","65b7d8f1dd1b4503b057861be8f36567","eec57e5badb5470389281d252ca8fbae","80f3e5f52c7b405993deaa110b328593","0dc5370942aa4c65995f6ca9cf1bb716","2480a0e57d514cc2bd502ea482b03e06","0129dc412dd2432dbcacb20d51489f29","4a46eaebc24f4dc3af5534e6932f800a","94ec0f5791db4fd8926533ad2c75b34c","d69ee81b568b4f898af95fa49003fd36","5f892ba5c3a647e9b73359850de9f543","a1b9018f692147f1a87c633613b94cd2","089df4013c234a4aaff9a78bce86c9ac","a785488af532460db56c5ac704b4d677","d15f3b65c068481ea08eb4b149cc31ec","1e439c78ad4a40ae8a460900e1a70117","2cb3fd70c3bf43bfa17855cbfe2f8824","d03c1990ffae400da687047228536d13","9560c37503de4061a5554fcf4c3bac16","ba07c97486d4498399baf6899e894201","3af0dc1246164913b4b1a0e1cf1788c9","3e82eb22b6c8496aaff7e5e98e862133","5b612371efc747208174ec9e69d30592","b21be11e65894aa799c83687bc2aded5","a5983b84be864b22aba7ad6efeeee370","b250c203e5e24e83914d515a833b1d91","aa0fdd3521384faeb9ef35d5ebae9ed8","5bbc304320b147c39b334fbd588afc30","0bdb8a464f104418983ef88ca7cddcea","b5e1a08cebf44a9fb7d9077aef72f2d4","63d259e3cb24443caa312df744e24ad2","3b718c73c1a14cc58bfa98ce86182219","b7ea648460f643719ce3fdb33a3e1dec","4b0c943eced0455ca96613b6f5032b32","a4c8b745e6f5402fa5059078d6f35dfe","440e564722a94807a7119d472113fbe9","5062e8b8c93f48b29e44461439f3f800","4281f211afe248cca4015e2083d29d1c","4dae3453680144a09ea1373289eac06c","77f4c10dba5e473b90a5647de1456957","3443a1429d65462982eb5b2f09d8786f","99676bec01dd4a4bae60f936f7092143","367d8112df434251967b62d71826b191"]},"outputId":"ece671c9-7f41-4ba5-ceed-c946e3b7af98"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"945c804b6d814ecd80207f3f23833357"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/127k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e3e43ece1add481a8f509f291c2bedc9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/62.9k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2ba50362ac5d4df5ae8305ac34817c70"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json:   0%|          | 0.00/16.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1852c97b5944d2b80319ea1095a9f46"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/772 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a2e6b5853fed42cfaed94f59b0907d03"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/310k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"be42719328274b5e911119b392a35bff"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.57k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f892ba5c3a647e9b73359850de9f543"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/730M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3e82eb22b6c8496aaff7e5e98e862133"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/347 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b7ea648460f643719ce3fdb33a3e1dec"}},"metadata":{}}]},{"cell_type":"code","source":["# Evaluate the BART model\n","model_blenderbot.eval()\n","blenderbot_scores = evaluate_model(model_blenderbot, tokenizer_blenderbot, dataset)"],"metadata":{"id":"CNNt0B_TBGqk","executionInfo":{"status":"ok","timestamp":1714446453124,"user_tz":-330,"elapsed":958449,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b1bc09fa-69b9-4ea3-f63f-a493f08b347d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n"]},{"output_type":"stream","name":"stdout","text":["GPU not available, using CPU.\n"]},{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 103. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=51)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 89. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=44)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 121. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=60)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 123. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=61)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n","Your max_length is set to 150, but your input_length is only 109. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=54)\n","Your max_length is set to 150, but your input_length is only 128. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=64)\n"]}]},{"cell_type":"code","source":["print(\"BlenderBot Model ROUGE Scores:\")\n","for metric, score in blenderbot_scores.items():\n","    print(f\"{metric}: {score}\")"],"metadata":{"id":"dtmpOQxJBe3F","executionInfo":{"status":"ok","timestamp":1714446453125,"user_tz":-330,"elapsed":18,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"e4ff3902-11ac-4cc2-b069-a476191adc33"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["BlenderBot Model ROUGE Scores:\n","rouge-1: {'r': 0.1155, 'p': 0.0929, 'f': 0.1016}\n","rouge-2: {'r': 0.0042, 'p': 0.0034, 'f': 0.0037}\n","rouge-l: {'r': 0.1021, 'p': 0.0819, 'f': 0.0896}\n"]}]},{"cell_type":"markdown","source":["# For demo"],"metadata":{"id":"YbAf1HllRPUV"}},{"cell_type":"code","source":["from transformers import pipeline\n","from rouge import Rouge\n","import torch\n","\n","def summarize(model,tokenizer, text):\n","  if torch.cuda.is_available():\n","        print(\"GPU is available.\")\n","        device = torch.device(\"cuda\")\n","  else:\n","      print(\"GPU not available, using CPU.\")\n","      device = torch.device(\"cpu\")\n","\n","  summarizer = pipeline(\"summarization\", model=model, tokenizer=tokenizer, device=device)\n","\n","  summary = summarizer(text, max_length=150, truncation=True, min_length=40)\n","\n","  return summary"],"metadata":{"id":"IvE4CVcORRvc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Original t5"],"metadata":{"id":"y4naEYKeWNwP"}},{"cell_type":"code","source":["#\n","from transformers import T5Tokenizer\n","from transformers import T5ForConditionalGeneration"],"metadata":{"id":"Os2EJ9gzR_eN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#\n","model_t5 = T5ForConditionalGeneration.from_pretrained(\"t5-small\")\n","tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714451731414,"user_tz":-330,"elapsed":3824,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"b056f0df-cc80-41b5-e3e5-a42c85eb418b","id":"aTqMeDGWR_eN"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]}]},{"cell_type":"code","source":["text = '''Hello my name is kartik. I am studying in IIITB. My name is kartik. Currently staying in banglore. pursuing my MTech specialization.'''"],"metadata":{"id":"QKVgwOPmTDnl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["summarize(model_t5, tokenizer, text)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wQt3gk5jRw8w","executionInfo":{"status":"ok","timestamp":1714452369942,"user_tz":-330,"elapsed":3609,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"a563a1c9-e095-42be-95c4-73b6b18c513b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 41. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=20)\n"]},{"output_type":"stream","name":"stdout","text":["GPU not available, using CPU.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'summary_text': 'my name is kartik. I am studying in IIITB. Currently staying in banglore. pursuing my MTech specializationization . if you have any questions, please contact me .'}]"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["fine tuned t5"],"metadata":{"id":"4gWebrOCWRHf"}},{"cell_type":"code","source":["#mount google drive to save the fine tuned model to\n","from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714452516214,"user_tz":-330,"elapsed":18608,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"962636c1-56f6-432e-c040-4dd44ab75b7e","id":"v1J6SYyyVPTN"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["from transformers import T5Tokenizer, T5ForConditionalGeneration\n","\n","path_to_model = '/content/drive/MyDrive/MyModel'\n","\n","tokenizer = T5Tokenizer.from_pretrained(path_to_model)\n","model_fine_tuned = T5ForConditionalGeneration.from_pretrained(path_to_model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zG062B2lSV0A","executionInfo":{"status":"ok","timestamp":1714452547191,"user_tz":-330,"elapsed":15869,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"c0beea86-0059-4e65-dfd0-cfbc9317cdb7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]}]},{"cell_type":"code","source":["text2 =  \"Summarize the following: \\\"\"+text+\"\\\"\"\n","summarize(model_fine_tuned, tokenizer, text2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5g18eQ-LVYTx","executionInfo":{"status":"ok","timestamp":1714453984992,"user_tz":-330,"elapsed":3056,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"03a75979-3eab-4a7f-ef8c-d6bdb41e8cda"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 49. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=24)\n"]},{"output_type":"stream","name":"stdout","text":["GPU not available, using CPU.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'summary_text': 'My name is kartik. I am studying in IIITB and I am currently staying in banglore. I have a MTech specialization in the field of MTech in the UK.'}]"]},"metadata":{},"execution_count":48}]},{"cell_type":"markdown","source":["BART"],"metadata":{"id":"s2nGQS-pWUGj"}},{"cell_type":"code","source":["from transformers import BartTokenizer, BartForConditionalGeneration\n","tokenizer_bart = BartTokenizer.from_pretrained('facebook/bart-large-cnn')\n","model_bart = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":209,"referenced_widgets":["3edcc2ebd3d8491d8eb5465849e9cb50","55324cb642be4458b3ea8702da493593","c65e2d446b004d7cb593c5eca06dc919","29958400f3f34ba4a8faca7888ff8cb3","9b0b15c469b34cf8ad331bc6441fe958","4822931da88548adb2d59e8f9921097b","0603cdb4012e4ad4a8d86d4baa7e1711","d2f4a2aa697347459ac897173c6bd4c1","7f238139c614464ababd69566315707f","c9419b088c694c18b5b5f364bf319e19","89c474fcc381454aa9366dda683417aa","7e9464a848db4cb684718388c555666c","065322ed793a41c491c26831d4cd904c","b8f26ca0527f4d0eb2768be2934f9dc1","83ec2eb9fdb6442e8bc78519449a260c","5cf19535189b402b8f1d1b5f9438dcd4","847a5f2a5e4043e38ca900fcffa854aa","114b0e50e74045499ed17ad1ec91158a","48ed7577956b4ee9ad5a4eb6f4ec181e","ab254a4178c942d197e484f6a5b99c30","804f27a58b0649968487cbc546e7695f","07f76750d074493fa5b9122fb729cd36","f4ed12a1823642e2b69e5de55457efec","e7e51d8e59ab4b658bceddbce7c00d3c","14bcce64e04e44119bcbb44b85353aed","9e470f91cee84f37bf6221c69f7c60c1","c1a0e558ce5e4088bf2bf7f3302995b3","2e11cbebe53948a0938226c3ee8a0725","c8e4449b2bba4bc9bf8dd510d10beda4","b7626f02a5a041a0a9ed12ec68e3d43c","eb392ecd6edd4ceda0ec8f0a866aaba6","823f092ff96646c0abd46198d1197df6","20251423215d443b9d924a831feaff68","36e40ea7ba864217b960853e6e84165f","8c81955a742a45ed8e6c9597a2a9b582","2d8f8092043747d980ab13a1492cce91","569ed6d46ae04c77ad6fab0a6344d7a9","2b0e75ac285e489082999a566ff7f4b6","86d259bca52f4f8eb65cb7523bdf054d","0a1ac56991ed4e389047590ef3eb0a63","cbf533904e984a1a8f1b05b4b464c003","69f37efd951744dcb157268d737a8519","e937423e3e3741849d0603cd482d1074","3441215da10e4b8eb42d8d7517beddfe","86c013254fc34d4b934cbeae4732f181","1090545bec8346ab9a7e16604be7e7aa","c7b6303228994fd3b35784a75022d118","3d40d1bf230542fb86238eb2832fdb5e","34363119f2c745c785b3ce008f6938e5","c32ec00a48fd4ec48b798afb887182aa","0f71607451d94edc84379c7da816cd81","3eea9c9dd9fa48c18d4f0cf7c1a47bfa","b3019c96924b49faba1663c0c354d336","a141b6b2616849599430e538b6d594ac","93f7bb80ea114c908db7228b5df40a3f","f92b2d2f65b54e42956d611c0d34e9f2","14f23c7ad7df4977944f3b2475327beb","4a3a27d2b860407a80583b9a9e17d7ab","a19c170be2ac478d832d7356307153dc","19e40939e4584480a8255a08ff019483","a6deeb9afbfe495999193252480eacee","65fb5c5e7f9a4a5480a097ad065ff363","64467b2b3545448f88734477e3f9e6dd","c7b7440e2e964a2d86fbeb3a78eba9f6","4749c291c88a481287e439c902248576","ee04070040d1464c938b43769ff8c104"]},"id":"7XwdyPxLWWpF","executionInfo":{"status":"ok","timestamp":1714452909526,"user_tz":-330,"elapsed":65027,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"d5f791ef-4d94-4efb-d69c-f01c8d00c375"},"execution_count":null,"outputs":[{"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":"3edcc2ebd3d8491d8eb5465849e9cb50"}},"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":"7e9464a848db4cb684718388c555666c"}},"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":"f4ed12a1823642e2b69e5de55457efec"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"36e40ea7ba864217b960853e6e84165f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"86c013254fc34d4b934cbeae4732f181"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f92b2d2f65b54e42956d611c0d34e9f2"}},"metadata":{}}]},{"cell_type":"code","source":["summarize(model_bart, tokenizer_bart, text)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-kTy5W0jWfpZ","executionInfo":{"status":"ok","timestamp":1714454058164,"user_tz":-330,"elapsed":9471,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"e480a6b6-cc7c-423f-cfab-7600128419ca"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 37. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=18)\n"]},{"output_type":"stream","name":"stdout","text":["GPU not available, using CPU.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'summary_text': 'My name is kartik. I am studying in IIITB. Currently staying in banglore. pursuing my MTech specialization. I would like to share some of my experiences with you.'}]"]},"metadata":{},"execution_count":49}]},{"cell_type":"markdown","source":["BlenderBot"],"metadata":{"id":"qAE6llvxYDmz"}},{"cell_type":"code","source":["from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration\n","\n","model_name = 'facebook/blenderbot-400M-distill'\n","tokenizer_blenderbot = BlenderbotTokenizer.from_pretrained(model_name)\n","model_blenderbot = BlenderbotForConditionalGeneration.from_pretrained(model_name)"],"metadata":{"id":"eRiA8_4OYJyH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["text2 =  \"Summarize the following: \\\"\"+text+\"\\\"\"\n","summarize(model_blenderbot, tokenizer_blenderbot, text2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u0Muo0-BYKRB","executionInfo":{"status":"ok","timestamp":1714453609293,"user_tz":-330,"elapsed":17873,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"cfcba31f-c160-4280-ebb4-c75ebb038db0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 150, but your input_length is only 51. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=25)\n"]},{"output_type":"stream","name":"stdout","text":["GPU not available, using CPU.\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'summary_text': \" Nice to meet you Kartik! I'm studying to be an engineer. What do you want to do with MT. Specialization? I'm interested in robotics.\"}]"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["text2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":53},"id":"N-F60ya8YOHu","executionInfo":{"status":"ok","timestamp":1714453609293,"user_tz":-330,"elapsed":14,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"ec65ce67-402d-4db4-8318-b9a897b603ca"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Summarize the following: \"Hello my name is kartik. I am studying in IIITB. My name is kartik. Currently staying in banglore. pursuing my MTech specialization.\"'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["PageRank"],"metadata":{"id":"gpC2kscoZHPc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import nltk\n","from nltk.tokenize import sent_tokenize\n","nltk.download('punkt')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0_7zBIb6_92d","executionInfo":{"status":"ok","timestamp":1714463862789,"user_tz":-330,"elapsed":1001,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"1502b37f-1cff-4973-ddf2-c20154c19629"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["def sent_tokenize_example(example):\n","    example = sent_tokenize(example)\n","    return example"],"metadata":{"id":"dwbn3ICT_-Ov"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sent_token_text = sent_tokenize_example(text)"],"metadata":{"id":"WqLK25vyAEfQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sent_token_text"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eH4iLht5ASgY","executionInfo":{"status":"ok","timestamp":1714463878209,"user_tz":-330,"elapsed":523,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"4cba7396-9b44-499c-acdb-0d342cdf23a8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Hello my name is kartik.',\n"," 'I am studying in IIITB.',\n"," 'My name is kartik.',\n"," 'Currently staying in banglore.',\n"," 'pursuing my MTech specialization.']"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["import numpy as np\n","import networkx as nx\n","from sklearn.metrics.pairwise import cosine_similarity\n","from nltk.tokenize.treebank import TreebankWordDetokenizer\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","def summarize_by_page_rank(sentences):\n","\n","  # Vectorize the sentences using TF-IDF\n","  vectorizer = TfidfVectorizer()\n","  sentence_vectors = vectorizer.fit_transform(sentences).toarray()\n","\n","  # Compute similarity matrix\n","  similarity_matrix = cosine_similarity(sentence_vectors)\n","\n","  # Build the graph\n","  nx_graph = nx.from_numpy_array(similarity_matrix)\n","  # Apply PageRank\n","  scores = nx.pagerank(nx_graph)\n","\n","  # Rank sentences based on scores\n","  ranked_sentences = sorted(((scores[i],s) for i,s in enumerate(sentences)), reverse=True)\n","\n","  # Extract the top N sentences as the summary\n","  N = 1  # Number of sentences in the summary\n","  summary = ' '.join([ranked_sentences[i][1] for i in range(N)])\n","  return summary"],"metadata":{"id":"4skH9OvOAqLf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["summarize_by_page_rank(sent_token_text)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"oz_mk3EhAvuH","executionInfo":{"status":"ok","timestamp":1714464151961,"user_tz":-330,"elapsed":653,"user":{"displayName":"Kartik Gawande","userId":"12373185382785816463"}},"outputId":"7204040f-d616-4413-b96e-8e324377ebef"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'My name is kartik.'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":[],"metadata":{"id":"7xZedTdCA2G4"},"execution_count":null,"outputs":[]}]}