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torch->transformers[torch])\n", " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n", "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch->transformers[torch])\n", " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n", "Collecting nvidia-nccl-cu12==2.20.5 (from torch->transformers[torch])\n", " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n", "Collecting nvidia-nvtx-cu12==12.1.105 (from torch->transformers[torch])\n", " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n", "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (2.3.0)\n", "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->transformers[torch])\n", " Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n", "\u001b[2K 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sympy->torch->transformers[torch]) (1.3.0)\n", "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n", "Successfully installed accelerate-0.31.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 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", " Successfully uninstalled google-colab-1.0.0\n", "Collecting google-colab\n", " Downloading google-colab-1.0.0.tar.gz (72 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "Collecting google-auth~=1.4.0 (from google-colab)\n", " Downloading google_auth-1.4.2-py2.py3-none-any.whl 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"output_type": "stream", "name": "stdout", "text": [ "Collecting rouge_score\n", " Downloading rouge_score-0.1.2.tar.gz (17 kB)\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.4.0)\n", "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from rouge_score) (3.8.1)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.25.2)\n", "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.16.0)\n", "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (8.1.7)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (1.4.2)\n", "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from 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in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->evaluate) (1.16.0)\n", "Installing collected packages: xxhash, requests, dill, multiprocess, datasets, evaluate\n", " Attempting uninstall: requests\n", " Found existing installation: requests 2.31.0\n", " Uninstalling requests-2.31.0:\n", " Successfully uninstalled requests-2.31.0\n", "Successfully installed datasets-2.19.2 dill-0.3.8 evaluate-0.4.2 multiprocess-0.70.16 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": 5, "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": "1dcc915e-011a-4d1c-b246-e707d270417d" }, "execution_count": 9, "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": "ebf57f73-d2ee-4ae2-b537-4e43c696b558" }, "execution_count": 10, "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": "1e110f79-7da3-4f10-d15e-e05336b79067" }, "execution_count": 11, "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": 11 } ] }, { "cell_type": "code", "source": [ "# loading the model tokenizer\n", "\n", "model_checkpoint = \"google/mt5-small\"\n", "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 183, "referenced_widgets": [ "256e8500be6442a09cda62d476fdd5a7", "2b3049a8f1a44766b3d952f2ed84f0ad", "6ae0720a447c4eaca9be17cc447970cc", "a8a3db016b814fa68a031fe88c2d1767", "746e754af28e4212ab18f69f2339ac18", "7b8cf25f4b3349679f58c88d8af574f9", "6609ad69efe845e3aaeeca1da551795a", "560cb67866e64a40bc70faea391338e7", "42cf1e833a5144ef8afaaa1c8bdcf01d", "4a709fb35ce64098ba742422ab6d8afe", "7370589d130b450497f7121246230feb", "bb0b3219e6824828bcd530ed9429c5b5", "32286fbf08aa48b7aeee445f571628ed", "59dc3a1fdaf44bc4867b917256fe03ec", "4fcc36b67921439b8b8c0d7da249772c", "aee1ac0fa148463fa139ab8b9760b908", "677d8ab766ad4f35bd684c0cab0b087c", "795f100c990c413a99838d62c9954335", "f03294448dba423e9b6a9455d82dd8fb", "b3a0396d2187433cae2dc2365aa78705", "ffeb5e15746f47a883994ca00519ee15", "20a472032e354e69b06441e4a8756c22", "8e4b3a7e82e64fd79be414f6e368ceae", "3a560dcd19aa4c0eacc5a84b92002810", "3796c3ae602c43419958d17c68b26f75", "f4e1f67f9c7d4898ab7e4bd42b16975d", "cf8fe79a70de4158a24d199a1817bb81", "ca97fdae05754a9f88f8cd0a7793464d", "ac01921d90ab45c1a151eed62f3a95f5", "942711e34eff49f0aa0adcc57763b0e3", "36713d46fca84fc5963277a37af5f289", "027b2bbfd28a41089a35df4cdd0ee84e", "8b1027e4fea84672a1b72de5fa278c97", "b7e9636b18044492943cf96c4b7fbd3c", "346e9ded15cb4fa08b017a22537d0c73", "2c7924547b494c20a87f0edd68c795ee", "19b2f6fdf9f247b3bd69988174eddaf9", "c58b16e6923f4317805ad329b8e13f80", "beb017644229490f8e39da5d903e49b8", "39681e52b1c44c17bc1f1f1005e0eb52", "c0904df780764a8a913fe86fcc1eca8c", "f6807a9002724c7a92d25e5c241e437a", "121103bd71094c3883777b0cb0baa9cf", "7c97048bc2bf46019b376ad59ee7079d" ] }, "id": "1rvUirfgLjU-", "outputId": "fd32f97b-5e65-4855-faf1-d4e8fc5366e1" }, "execution_count": 12, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/82.0 [00:00. 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" ] } ] }, { "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": 13, "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": [ "a4287c6eb9ef436e81659e1641cbbada", "d3422ffacd4c4bd3aae945703aaad34e", "ef9b0a547cce42b3a676711c9e7abc25", "2a7a7a14785b46c9aaaa8fdf61afef5f", "25c682f9dfa945e2a798db021fcc52fc", "d21c34c8ae394a0792654c0ec7f1749a", "89ec9d68850a4529811ee7f94420906e", "4d138bd8142144a79f04e97537b99411", "470a8249b4b94e81ba4027a90196c636", "1bc24b5efa244275b5e8f906e6e6bf82", "ca10e281549c4687a79a764e07eafc32", "b3c8c24a8a814b79a3e6347505fc11c5", "645943167ce248b48e107cb318e6d3ec", "708f961b450849cfbfcd66faa5ff45f0", "1500b2d381474922a5839c85a93c1fcb", "703cfb61799f4166b341124502c5faa9", "f64a31e437a34ba5b43f54f0650e73e4", "55ea5a68b67845558e592289ba16c505", "528d7b5086d44bc596989a93e16cb793", "4776914953494eb1bde45bad5c1dee5a", "bb6a10526663432db2cea8a325bf9445", "4203e4ec1bf3456d83493fa614ff353a", "67031d43acfd4f0ea18be36d417d9978", "0851f589933b410cbd183875b030357d", "1034022bba5342b08458f7e4b69989f4", "fe3d5bc580fb4b5bb188220a90409102", "da75209ce58546018bbea436a4afac23", "9951b5729fd744a0b5c1b94433ad5039", "51a4d819d2de470da86e550e4d5c5939", "9c86d2b066c144faab2fc546593fb9f2", "8142244500694c38995f151902dc0ad4", "3a3612db849a49bebe60112098e708d6", "f0cff9671aa8465e9efcf8908694eb6f" ] }, "id": "owifODefLvvU", "outputId": "23180e2d-79f1-4b4e-a4f9-c3fdd2b0c596" }, "execution_count": 14, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/3516 [00:00 " ], "text/html": [ "\n", "
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StepTraining LossValidation LossRouge1Rouge2RougelRougelsum
3519.2632003.70590617.3365005.23070015.43800015.377600
7024.6719003.08961119.5787006.82780018.06370018.025500
10534.1356002.87128022.5668008.28990020.55100020.523200
14043.7852002.77293325.7974009.91580023.23980023.219800
17553.6194002.70384226.25720010.00340024.03260023.995600
21063.4864002.67142326.8149009.90560024.27040024.139900
24573.3965002.63606227.53990010.36090024.82860024.762800
28083.3422002.61944128.02980010.69380025.16780025.092400
31593.2879002.59764828.23240010.64120025.28030025.180400
35103.2391002.58936429.01550011.17400025.99950025.884300
38613.2128002.58537429.32830011.47700026.22350026.127800
42123.2214002.58132029.43220011.43610026.38750026.297000

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=4224, training_loss=4.052591073693651, metrics={'train_runtime': 3439.6826, 'train_samples_per_second': 12.266, 'train_steps_per_second': 1.228, 'total_flos': 2.230002234372096e+16, 'train_loss': 4.052591073693651, 'epoch': 12.0})" ] }, "metadata": {}, "execution_count": 26 } ] }, { "cell_type": "code", "source": [ "# evaluating the model\n", "\n", "trainer.evaluate()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 198 }, "id": "ORMFInr3c_V4", "outputId": "6ef9e94e-3eeb-4cc9-f4b8-3d67956bbb12" }, "execution_count": 27, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [44/44 00:25]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'eval_loss': 2.5813422203063965,\n", " 'eval_rouge1': 29.4322,\n", " 'eval_rouge2': 11.4361,\n", " 'eval_rougeL': 26.3875,\n", " 'eval_rougeLsum': 26.297,\n", " 'eval_runtime': 26.3868,\n", " 'eval_samples_per_second': 16.637,\n", " 'eval_steps_per_second': 1.668,\n", " 'epoch': 12.0}" ] }, "metadata": {}, "execution_count": 27 } ] }, { "cell_type": "code", "source": [ "trainer.args.output_dir = \"mt5-small-finetuned-news-summary-model-2\"" ], "metadata": { "id": "TcSmvzio-PKs" }, "execution_count": 28, "outputs": [] }, { "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": 167, "referenced_widgets": [ "523d92b2f32d469e8b3950539a2d95c1", "556efece8bb446c8945cfbeedcdcff97", "18b6c82b5a054cd39e2d1a882d291fab", "016396f17d4b42d49965a471f202a313", "e153409020434b58934d75bebe72c0e5", "1ff51a26657b4b37ba78d463cb096bda", "e85b7c4800c3458e8752eeb50d59074a", "847afb2882d44da2a2913b0439626c12", "dadc612c0de64cdfb47161ad70ff7e91", "0452bdfb828f40ea99a1907e227db1de", "2e7022f231ff47bd919b1a15479e5773", "9ac20c767b674f39b217f917d5e2564a", "931d57a3c36443f6811e307e8ad6a142", "806e49efaf7642798cd4ca592ab950ed", "6578f8076a4743cd84b112730dac15f4", "cd4b72d1c81b49599388478d2a75f6b3", "e1f68563289946bb898b1de32aba3dc8", "0715c1d9492c4b848a79fbf9fcc0b132", "96070333c6994a329590a2b9646b4a80", "3b396edf5407427886495ff41b792b54", "a511d3343a8a41a28f8b0b0f0fd987c9", "15094d51a651410b8a37402b928dd0e7", "1d0ed761b2fb4a889f642cdd26a19285", "b0546418d1a34c50b3c21716cdeb650c", "0f558e3100b245d1b0e4ccab818dc25a", "9185c953bb21447faa171b9a8598dde4", "3648805251134353bcd184528baa4f97", "08e70bf4ba0c4a698f69248bcfd744c1", "c2b2c363225c45eba51e76e958a69cba", "2a4ae962a9c74c42afe0c99b9856cc30", "83771b25f5334f169a68af31ff830105", "a3ecec748a8e4b0d82c09318850cb1c3", "9bf4ee5f5749468382b42a31502d721d" ] }, "id": "ayEJ6vB2dV-6", "outputId": "93f49ea4-f137-4d86-8cc5-a4f31aa2b119" }, "execution_count": 29, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Upload 2 LFS files: 0%| | 0/2 [00:00>> Article: {review}'\")\n", " print(f\"\\n'>>> Headline: {title}'\")\n", " print(f\"\\n'>>> Summary: {summary}'\")" ], "metadata": { "id": "HVxphmCffAtu" }, "execution_count": 32, "outputs": [] }, { "cell_type": "code", "source": [ "print_summary(20)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R0_j0gCGfIs7", "outputId": "155e2498-7772-4dc7-adc0-cdb79fa56be4" }, "execution_count": 33, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "'>>> Article: The Indian Army, after consultations with the Defence Ministry, is considering cutting down the numbers of its sahayaks or 'buddies' by 25 percent. This would translate to around 10,000 jawans as there are currently about 40,000 sahayaks in the Indian Army. The move would come in the wake of the controversy over the tasks Indian Army sahayaks perform for officers and junior commissioned officers.Under the army's 'buddy's system, sahayak jawans are attached to officers and junior commissioned officers. A sahayak's tasks include working with the officer or JCO for army-related duties. According to top army sources, the 10,000 sahayak jawans will be replaced by civilians. The civilian substitutes will be employed for officers in static formations such as the Army Headquarters or units in the Delhi area and not operational locations like battalions, brigades and division and corps headquarters. Once their civilian replacements are in the place, the 10,000 sahayak jawans will be moved to army formations across the country. This is perhaps the first step in the dismantling of the army's sahayak or 'buddy' system. The Indian Navy and the Indian Air Force do not have such a system in place.The age-old system recently came into the spotlight after a few jawans posted grievance-filled videos on social media. The videos criticised the buddy system, with the soldiers alleging that they were often made to perform menial tasks for the officers whom they were assigned toáALSO READ: Buddy no more? Army chief supports restricting 'sahayak' system'\n", "\n", "'>>> Headline: Indian Army considering to reduce its 'sahayaks' by 10,000'\n", "\n", "'>>> Summary: Indian Army considers cutting down numbers of sahayaks by 25 percent'\n" ] } ] } ] }