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satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from 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 nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n", "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch->transformers[torch])\n", " Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n", "Collecting nvidia-curand-cu12==10.3.2.106 (from torch->transformers[torch])\n", " Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n", "Collecting nvidia-cusolver-cu12==11.4.5.107 (from 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 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/usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2024.6.2)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->transformers[torch]) (2.1.5)\n", "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from 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": "69f77c8c-4d0d-4f1d-845b-5e535a489e6c" }, "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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\\\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": "2acbfdff-675f-4b16-e459-fab151d569f3" }, "execution_count": 6, "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": "75a6ed93-adce-4f13-b053-97b64da9d1ee" }, "execution_count": 7, "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": "b372222b-168a-4b91-a52f-70df4a34600f" }, "execution_count": 8, "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": 8 } ] }, { "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": [ "2a20223136b64d2ca061c73eb6b96f42", "f396b9a36ef8405d80a57d2ccba9afca", "12eed3f34b0143ee949d0ba85a6508fd", "e596e1a5bd3e431e96c822639ee20333", "b664108206be4501926522e55baa5bef", "c39676442eca4aa1ae33a44bd732d277", "47cfc71a140440418705395c9f723ca2", "4c93c67f9226465297f45c906a81ac7d", "34d8dd902a5748b9a6c936ad4fb6c0d1", "12e533b4b86f4f19a326ad75adcfa145", "f27627ebb0ae425889694c838601ba19", "369a9a0a255a4d15963bb4fc1d08f318", "69c7d5c454e94ef7bcc497a025caf837", "13d19394cf014bff983f22568d386ab2", "8729de4f5da54b659d21c8caaea339be", "507b988733b147c6962535f419aaa2af", "160a74e158ce4a1ead8f60085ba4cc4c", "709d774aa513419eb62d4b52fc2a0cd3", "6641b9ce00014424ba8fc64857c041bc", "979c3c2d4c9f4ccfb9d101b1dca67f28", "0897cc9bea534708adf0769494aa83bd", "0c60e2af650b429abe7a85f558d9d935", "79a163dadce94959aad769a7cd8ff6e2", "6dd1e9d69c9c4cf6a1e074b50f85c2e2", "d58f9391866847048c6e46fee59bd078", "31f2fb29390d40e6b5d8cb5bf57cedb2", "073ce139dcbb4597ace50ad1bf1f7ba7", "be0e5d29846d48d19eafdeb345d7fb62", "f9b80009665d4eb28abf4a5d1310f6ae", "faf732673da244b6b24ec620c6a30e59", "d613e21502e245e090a9aaaa7b623e70", "4f4aef95955d406faf372791cf44bfa1", "aecf5834bc7c4373b59bed072bfe13d5", "5b6143edab734522b007f6d24fccde1a", "ccad916239c14863a054da4a538822cd", "c4528015e3414ead9c8c28ef158a1755", "474c5bb843c14f449c6b68364b3eba2b", "a3b3dd361fe641eca2e28a71ec1ac93f", "a21d72ae913d4a9f9e741a0ded3f36fc", "60858bf0b0ec4fc2810e26eada87ae3a", "add3af8b793a4b5480e38d4ff7657f91", "e3d6aeaf93a54dfc81ec55a17b204aae", "3d58efa39e9f4c6381db222a1c5530de", "03ddbc23aa32436cab200b5e4996aead" ] }, "id": "1rvUirfgLjU-", "outputId": "dba9023b-5b78-45cd-8f1b-2871184aa17c" }, "execution_count": 9, "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": 10, "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": [ "18e6aa5c47234313ad456a6e544099ed", "e69ca801674b46a689c376caecd49fa1", "32565c1bf86347118c919fbee4ab22b0", "1419ad7fb6864fdf9d5bfb97f3a158e9", "78b979e7d71841ccbb305d74e58e7b67", "c0b8de0cb4b745b19b649b8ea8a62c7b", "34ff48aad522492fae4542e452b5c892", "2ecbc10a51864d7a8fd835b373086f96", "4462f098788042a6baf9c7f479505474", "dabea935439744c1bdd957086f3c07ba", "3c0f58e554bb4b3d98c314ce719ce6ca", "89999eb7d7324048ba8a10f718ae65ca", "4dbb9444452b449c97aca6596afc5dd9", "1d1f6fa2ddcf42e38c49e939f919a964", "b27ab09be6b942babadddba6cde0abe0", "ee4283b856be434c922b49cc765e23cc", "e800037a42a44db983cb4c869a863c44", "3bb5145c28bf4a67b9cd665021a04bff", "1ac5ee871089493ab2f9273a95e5e05a", "74d988ed44c143bea1b6bd8c6eddf057", "84465b2a1cea41f6b8c1efaa3cef2b0b", "1dba95e4ecc24be09d03f01205015fb4", "7af7a2abd58f4322add4082d3ba1d029", "4521890add944d67abe52d7604119fff", "31d7a76d1b5445c3928614fd96067d06", "a7825b4a20584dd3b6a357cfaf563b54", "ea0f7e5917f84d59aaa1070fb9c1afa2", "8cbc8e5ad1404c0ea7d130026110a064", "b10964e6852d477d883dfff1acdd7d98", "ca87d0be9f694aa1abe7c14afab7ce81", "2eb8b3b5a17546269329060d7393a11a", "2b47e63ebd054a5caa1e3bf8f3431887", "14e9695a0f0e4b59a6bcc0ca75f2990a" ] }, "id": "owifODefLvvU", "outputId": "f23dd982-5279-454d-8254-e4cda9538c9a" }, "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/3516 [00:00 " ], "text/html": [ "\n", "
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EpochTraining LossValidation LossRouge1Rouge2RougelRougelsum
18.1234003.31229418.1738005.98110016.74570016.712600
24.2107002.84043623.0090008.38240020.90740020.896200
33.7380002.73541026.56960010.10590023.93210024.021400
43.4864002.67562627.19300010.19710024.47630024.493300
53.3642002.62237328.78420011.53230026.31700026.321100
63.2690002.58833629.65790011.80430026.88240026.869200
73.2120002.56766329.75130011.66390026.60420026.640000
83.1860002.56912929.86330011.69800026.87390026.853600

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=3520, training_loss=4.071405632387508, metrics={'train_runtime': 2619.9929, 'train_samples_per_second': 10.736, 'train_steps_per_second': 1.344, 'total_flos': 1.485996478906368e+16, 'train_loss': 4.071405632387508, 'epoch': 8.0})" ] }, "metadata": {}, "execution_count": 24 } ] }, { "cell_type": "code", "source": [ "# evaluating the model\n", "\n", "trainer.evaluate()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 198 }, "id": "ORMFInr3c_V4", "outputId": "4ca0c2f4-d37c-47d8-de63-db929e2f1e4d" }, "execution_count": 25, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [55/55 00:31]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "{'eval_loss': 2.569129467010498,\n", " 'eval_rouge1': 29.8633,\n", " 'eval_rouge2': 11.698,\n", " 'eval_rougeL': 26.8739,\n", " 'eval_rougeLsum': 26.8536,\n", " 'eval_runtime': 33.3633,\n", " 'eval_samples_per_second': 13.158,\n", " 'eval_steps_per_second': 1.649,\n", " 'epoch': 8.0}" ] }, "metadata": {}, "execution_count": 25 } ] }, { "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": 199, "referenced_widgets": [ "875801746d72442a81816a8358c0f87d", "9b5822f776e242df9b0c4fae22e79864", "2f965df1d1bb4fd9a371c8e9a83eaf5d", "d9745c9e1ec74d598cfd33e2e7ebe549", "b4d7fd2245914ff7ab9c075fd7b1cd35", "10f77f9743974c4fb81291f53ba49605", "0f2cefb44b044473bedebb000fb47379", "1606a9b44a5849f68ac37bdd70c664ca", "b8ebbb04d55142d0a4176e82eab8caa9", "6c37401defd94a0d8578a5ddd4ec071b", "3145384e8a6e4f0a8d6aa571823f536a", "907c667a1fd642b1bf12078fae695ecb", "3b8314b84ea74d1f8b942acfea0f5c74", "1d3b3b0d6d6948e6adc2efb9936c1648", "0a764c6056fb480abe524ebaf2ca977f", "cf60710c2d1b40438c6db4592ce984af", "43c6a69946104b069dfaf9e58fc11f68", "bd5302cf9cb14963969496f16dba7896", "7c752a02cb3649e289c6cfd1f9a746ee", "5044dbb62d0544a1ad4b7d2304827cc4", "1b21b7d98c5040099dbfd44853577c75", "e8a5f64c7b1642f48605357526c1a298", "f05bde641825460b82954f80dbfc2f62", "594f07da011e41ac90a6aa1fa525935a", "54b585f662af47a1917d2ee6be9e2dcf", "adf951c6d547421a829e046d02169733", "bdd05951633a468fbdf643991bc46623", "3f615c5ec8aa484eb12cd0f96f8d11fb", "e0e7ce367b1f4d6aad6d0c7f8c49cbef", "2e7dca7036a94f47800fb6ca1337531f", "0b9f2b86daf447bb8f32b16beb31137b", "eca9001f6e124c3f8f15e2e23e75252a", "18a2dd190ce04c8ca628b4d762c03823", "3696f0b746b340aab318697994f8ff87", "86dcb82a013e4f2bade7d05df25ca2e7", "0b464a29ef2c4886b29054e09de8cd3b", "5dfb6bd7ec134a4fb2a16e6e39b1127e", "dca305f4c3144d8a904f34a9193924c3", "dcd940938a7544b2b38250d2ed147b31", "f00d1dfa37024358b0d25eb5d764e244", "339b5d4f081c49b2ac94243b319c42ba", "c6de85d23e0d4e32b95dc25ebe043b6f", "3b1778d78f09465ba2479fafeb726109", "b4aaaaf238e04f3492b3414221531764" ] }, "id": "ayEJ6vB2dV-6", "outputId": "a09a0f6b-308d-4059-d18f-396a781f53be" }, "execution_count": 26, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/1.20G [00:00>> Article: {review}'\")\n", " print(f\"\\n'>>> Headline: {title}'\")\n", " print(f\"\\n'>>> Summary: {summary}'\")" ], "metadata": { "id": "HVxphmCffAtu" }, "execution_count": 28, "outputs": [] }, { "cell_type": "code", "source": [ "print_summary(20)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R0_j0gCGfIs7", "outputId": "55dff230-e4a8-481c-acdd-ad4110cad4cc" }, "execution_count": 29, "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 cuts down numbers of sahayaks by 25 percent'\n" ] } ] } ] }