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"Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.23.2)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.25.2)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (24.0)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2024.5.15)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.31.0)\n", "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.19.1)\n", "Requirement already satisfied: safetensors>=0.4.1 in 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torch->transformers[torch]) (3.3)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (3.1.4)\n", "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->transformers[torch])\n", " Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n", "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->transformers[torch])\n", " Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n", "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->transformers[torch])\n", " Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n", "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch->transformers[torch])\n", " Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n", "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch->transformers[torch])\n", " Using cached 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nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105\n" ] } ], "source": [ "!pip install transformers[torch] accelerate\n" ] }, { "cell_type": "code", "source": [ "# Uninstall conflicting packages\n", "!pip uninstall -y requests google-colab\n", "\n", "# Reinstall google-colab which will bring the compatible requests version\n", "!pip install google-colab" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oTmL0TpYK0vO", "outputId": "2efb5898-1bf9-4936-cb1a-96763c952efd" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found existing installation: requests 2.31.0\n", "Uninstalling requests-2.31.0:\n", " Successfully uninstalled requests-2.31.0\n", "Found existing installation: google-colab 1.0.0\n", "Uninstalling google-colab-1.0.0:\n", " 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requests-2.32.3 xxhash-3.4.1\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "from datasets import Dataset, DatasetDict\n", "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, \\\n", " Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, get_scheduler\n", "import evaluate\n", "import nltk\n", "from nltk.tokenize import sent_tokenize\n", "import warnings\n", "warnings.simplefilter(action='ignore')" ], "metadata": { "id": "sVXABBMkLQMg" }, "execution_count": 34, "outputs": [] }, { "cell_type": "code", "source": [ "data = pd.read_csv('news_summary.csv', encoding='cp437')\n", "data = data.dropna()\n", "data.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n2sbiMvVLah9", "outputId": "82f17c78-0515-4b8c-bf6d-703ea0536ac7" }, "execution_count": 35, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Index: 4396 entries, 0 to 4513\n", "Data columns (total 6 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 author 4396 non-null object\n", " 1 date 4396 non-null object\n", " 2 headlines 4396 non-null object\n", " 3 read_more 4396 non-null object\n", " 4 text 4396 non-null object\n", " 5 ctext 4396 non-null object\n", "dtypes: object(6)\n", "memory usage: 240.4+ KB\n" ] } ] }, { "cell_type": "code", "source": [ "# headlines - column containing headlines which will be used as reference summarizations\n", "# ctext - column containing full texts of news articles\n", "# taking a look at the average lengths of both\n", "\n", "def length(text):\n", " return len(text.split())\n", "\n", "print('Mean headline length (words):', data['headlines'].apply(length).mean())\n", "print('Mean text length (words):', data['ctext'].apply(length).mean())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oWRd74hSLcPm", "outputId": "9c73d4c4-c84b-49a6-cd56-c3db4a8e072b" }, "execution_count": 36, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mean headline length (words): 9.300045495905369\n", "Mean text length (words): 342.9438125568699\n" ] } ] }, { "cell_type": "code", "source": [ "# splitting the data into train, val, and test, and converting it into Dataset format\n", "\n", "train_size = int(0.8 * len(data))\n", "val_size = int(0.1 * len(data))\n", "test_size = len(data) - train_size - val_size\n", "\n", "train_data = data[:train_size]\n", "val_data = data[train_size:train_size+val_size]\n", "test_data = data[train_size+val_size:]\n", "\n", "train_dataset = Dataset.from_pandas(train_data)\n", "val_dataset = Dataset.from_pandas(val_data)\n", "test_dataset = Dataset.from_pandas(test_data)\n", "\n", "dataset = DatasetDict({\n", " \"train\": train_dataset,\n", " \"validation\": val_dataset,\n", " \"test\": test_dataset\n", "})\n", "\n", "dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s-LWUs8dLiUo", "outputId": "f5a37e27-ee03-41a9-a1de-ef936fbe34ac" }, "execution_count": 37, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['author', 'date', 'headlines', 'read_more', 'text', 'ctext', '__index_level_0__'],\n", " num_rows: 3516\n", " })\n", " validation: Dataset({\n", " features: ['author', 'date', 'headlines', 'read_more', 'text', 'ctext', '__index_level_0__'],\n", " num_rows: 439\n", " })\n", " test: Dataset({\n", " features: ['author', 'date', 'headlines', 'read_more', 'text', 'ctext', '__index_level_0__'],\n", " num_rows: 441\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "code", "source": [ "# loading the model tokenizer\n", "\n", "model_checkpoint = \"google/mt5-small\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)" ], "metadata": { "id": "1rvUirfgLjU-" }, "execution_count": 38, "outputs": [] }, { "cell_type": "code", "source": [ "# creating tokenization function with length limits for headlines and texts\n", "\n", "max_input_length = 512\n", "max_target_length = 30\n", "\n", "def preprocess_function(examples):\n", " model_inputs = tokenizer(\n", " examples[\"ctext\"],\n", " max_length=max_input_length,\n", " truncation=True,\n", " )\n", " labels = tokenizer(\n", " examples[\"headlines\"], max_length=max_target_length, truncation=True\n", " )\n", " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", " return model_inputs" ], "metadata": { "id": "tQ-Dq607LtY9" }, "execution_count": 39, "outputs": [] }, { "cell_type": "code", "source": [ "# tokenizing the datasets\n", "\n", "tokenized_datasets = dataset.map(preprocess_function, batched=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "2db284ffc80245cfbfc8d121d6227d42", "68d7b2fce829488cab62e7f600357a49", "1de9b1f58fa94974a8987f0273a5b36a", "53ccd0a764af4e3f845cf0c1c765a3b2", "b2932c9b98ee4121b457b10df7e27970", "5e223182f0b54eadb9dcf6035caf0965", "92da36292c2f40009bfe9fe1c6338fc6", "a3014a2f737b46d2aa566c04dca36d3b", "f0de505bf2c44b2ab3f82c94ffd007a1", "fce8e348df644d1cb71764f87eae28ec", "3e116dcfd9584cd9b2a35d93a936c04e", "37d4b6aa59264311b9f57c2e9bfe059c", "fcbccb17355244d0bb9bc74e19f2f90d", "3a221c605f81431ba4093f3a15f95af2", "e37f69963ffc4b2e8bb2ac43cf3bdedd", "87c2684ff8134580bd2deb92f12ef230", "d00c94ac9eda47fbb6a4b1ed658809f7", "8e1e66c3f1934c4994f47137d4399c3d", "bf504ad8971b411f953099078f440d7b", "0cb3200f2a404a449ef3762cc268e109", "2759c40b7d0c442bb68602022635eedd", "72dc54cf17f042bca68568899c986445", "700e1d1ce5b440219faa2d266740d855", "2a87d0fb002442d5b95d3f7daee63821", "2a671143e87d4a729c6235e39647a1a2", "902990b38b4d4c4db879e197b270706d", "39b216ce5ea9494396872e6bdbf95da3", "e5a0938f2dda472f8939e51b52a03ccc", "69401cc329bd40bda414b41cf89cbcec", "7d9b274769794422ab6fbe5a1775f7bd", "7e7294ce6546457fb4026164a7f7e15c", "83351be01e6d4d80a4ee178cbb61f0bb", "7a225dc304e24ccc8c4a8fa02b551213" ] }, "id": "owifODefLvvU", "outputId": "4e67eadd-7c2e-4c48-f626-26f69c87b704" }, "execution_count": 40, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/3516 [00:00 " ], "text/html": [ "\n", "
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EpochTraining LossValidation LossRouge1Rouge2RougelRougelsum
18.1234003.31229418.1585005.94350016.73640016.764600
24.2107002.84043622.9864008.38150020.83540020.934600
33.7380002.73541026.59840010.08230023.91200023.958500
43.4864002.67562627.14870010.16810024.37880024.467200
53.3642002.62237328.75130011.54160026.21060026.233500
63.2690002.58833629.64610011.80380026.75810026.776400
73.2120002.56766329.80370011.65820026.55320026.545500
83.1860002.56912929.91220011.67840026.81200026.834500

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=3520, training_loss=4.071405632387508, metrics={'train_runtime': 2869.8181, 'train_samples_per_second': 9.801, 'train_steps_per_second': 1.227, 'total_flos': 1.485996478906368e+16, 'train_loss': 4.071405632387508, 'epoch': 8.0})" ] }, "metadata": {}, "execution_count": 52 } ] }, { "cell_type": "code", "source": [ "# evaluating the model\n", "\n", "trainer.evaluate()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 193 }, "id": "ORMFInr3c_V4", "outputId": "52b0c8a4-1037-4349-aadf-b5c044499cc1" }, "execution_count": 53, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [55/55 00:33]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'eval_loss': 2.569129467010498,\n", " 'eval_rouge1': 29.9122,\n", " 'eval_rouge2': 11.6784,\n", " 'eval_rougeL': 26.812,\n", " 'eval_rougeLsum': 26.8345,\n", " 'eval_runtime': 34.3434,\n", " 'eval_samples_per_second': 12.783,\n", " 'eval_steps_per_second': 1.601,\n", " 'epoch': 8.0}" ] }, "metadata": {}, "execution_count": 53 } ] }, { "cell_type": "code", "source": [ "# pushing to Hugging Face Hub\n", "\n", "trainer.push_to_hub(commit_message=\"Training complete\", tags=\"summarization\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 180, "referenced_widgets": [ "54e39d831b874299b70fe6e6afb993de", "4855d6d5d02c48ceb51db1d8d14b427c", "74bb9a2ec535443b8175636f8840e43d", "35d3e53c52f84a7f89b5d8ca6542479c", "fa3cc7f2478f4d74a1b9a38e6d32b86d", "ddf450df82634345ab0b8d1506ebd464", "6b82859833ae4a0eb79b5bc70fd72439", "d51185d31daa4b978dee9c91fbf4aa19", "afe2f0739bd948939fd266acdd7ac775", "47ab9a16a044419287593f904abbe9aa", "b0ba36d50a9944a9b745eb8853c77339", "d56da7bc5b8a45e399e72f6fc98a0b45", "cb65d58b09974b4e8f06f52f3482086a", "d6e50a99e41c4b26aa9bf2b1ab2856d5", "cc05ec2d7b724e92a4c44f036892055b", "00ef15ec0c2e4c959628292256beef10", "d20bc36c04574a1fb8e1f4a0ca0864bf", "c9abc7d18fab41d49d53b520d4133cc4", "796a07e575714be0adf38f59044ae32f", "baddd70d90314e61a7e01c4fe4780345", "c7a5798946f2465d9e50eb9301667a4c", "9f45928818f846cd8dd73f82278a72e2", "4239b71ebeb442a99c9f34b5400e001e", "8d529009008346a49191b7d618ab7415", "b39296216b66432aae9f386107dfc580", "feb0380205014eeabd331eabdf757e19", "f67a8d01ce8b4657bab75008cedf90f6", "9a39e0cd4a7c4997a0263f8fd541e3a3", "9f0b78870cca4e4e80febb8d5720901b", "ece76ef9e18e45fcbe980720320f8540", "dd646b144006444fb9e57da6ccbc8e4e", "17f79c2a5c1c4a81ba3caa2d649a820d", "1bfa179b9f1144849ccf6463e2eb4b97", "31428fcaca2343048d88aef90692ca5b", "1a4fa766ff114f1383ebe1b32abbddab", "9a4f597ef00947dc9edd51abb490a05b", "87c60f5645954d3cba168ccdc1e9cab1", "60b398fb49ae4ece8a5f8a9aa3fe6db1", "9cd4b0b34c0647d7be63beda3ec30a64", "a251db54ec8e407eac7231346ef56bfb", "5e48b6f1a8094e9793b43dcf642f5b27", "4947114a1bf44d7b811b43788e5ef4b0", "55845f262cca4d8da2364dc20b4f8d77", "d1dd5d14fd7342b08f22cd100de61bb1" ] }, "id": "ayEJ6vB2dV-6", "outputId": "a281b484-0023-442c-d82e-eca79d88da8d" }, "execution_count": 54, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Upload 3 LFS files: 0%| | 0/3 [00:00>> Article: {review}'\")\n", " print(f\"\\n'>>> Headline: {title}'\")\n", " print(f\"\\n'>>> Summary: {summary}'\")" ], "metadata": { "id": "HVxphmCffAtu" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "print_summary(20)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 263 }, "id": "R0_j0gCGfIs7", "outputId": "ec675e69-5276-4ff1-815c-cf83ed44b819" }, "execution_count": 3, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'dataset' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m\u001b[0m in \u001b[0;36mprint_summary\u001b[0;34m(idx)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprint_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mreview\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ctext\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mtitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"headlines\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0msummary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msummarizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ctext\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"summary_text\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined" ] } ] } ] }