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\n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   39C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","source":["from transformers import pipeline, set_seed\n","\n","import matplotlib.pyplot as plt\n","from datasets import load_dataset\n","import pandas as pd\n","from datasets import load_dataset, load_metric\n","\n","from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n","\n","import nltk\n","from nltk.tokenize import sent_tokenize\n","\n","from tqdm import tqdm\n","import torch\n","\n","nltk.download(\"punkt\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7w7bzxR4Yo7X","executionInfo":{"status":"ok","timestamp":1669694654650,"user_tz":-360,"elapsed":8040,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"ed44d555-0846-4a0e-c0d4-d140e41a2d5f"},"execution_count":4,"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":4}]},{"cell_type":"code","source":["from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","\n","model_ckpt = \"google/pegasus-cnn_dailymail\"\n","\n","tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n","\n","model_pegasus = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt).to(device)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["4cb6e7dc51b940e68b07ecad7c855684","c0381ed00b8844caad4b6008ae1e84fc","3ef1feef1fb142a3a135e93bc0195845","26c0bea0a5ca40a093185c90e195dd95","d83c9a4fc186449ebc96ff96eaf3ab92","c028ae460e764927b2eff68d34f529b0","2cd193a78430447d811a7380d0754e76","bc4727d8bba24d3d9830816e755ef5c2","8b06ce2867fd4b4a95ecc227799c2d6e","4e8ad1037f4e4fc7978804b2ea1ec39b","e033d53e94dd4ab8a3b6a7187388d26f","462893d7575147e7b575457763496b7a","6b1b41596da7498fa86f1020033ddd5d","652eee8cc37d4dbba9d485e304ff0b93","349ff7ef5fb246b9bc4f85ae38839d2a","1e8ed649a548421988969af5cd7b238e","7004ac622ac346af9fc9941f36a7c3b4","ea774497bc9d413c8a9a8e688b984b4c","81f6f8ded8f5420f8575606683112e31","0f9b57200373492eb6c5c96eb39f33eb","ff5a5cc006df4f6a8aa52b6879a9417a","a532b90f96f64fafa59415141fb467b3","553a02568b664c8399fe0b4bde237da1","d210942b1ae449528a921e0173dc411a","1c1d1161556248788c891f1b74522209","59735c8df0e04d17b4611209399efc38","8233acd64d9144f0b169cf9360590070","b6e36a2745a94b97bf562cdd3ac8810b","c2de0ae457b546b6a6c1afe40c673c89","84d4bf7cd40d4bfd8fad14d1c596891d","422a4d0cb2ed40f3a44863f0549eef3b","2a01dde2f3794327b6844ee146f5966f","022d75d767414eb7a70b5e4e00e892c1","e1c27df5e06647c489e1c418c84fac18","c365259b592d4b868541bb1959a61c81","17528685004b4a6e8a44457792c4f9b1","8ba9816d8ed94c3aaaf7e723747be4cf","a1b66d20edaf4cdc886e68bcce094645","818bd12f65bd4022b6f1a3cde99a0402","2fc866eb122646f48da5ec94d6515b7c","764701ab9a934d25a74d4f569740dce1","756af11acc314799b228b34445897ca9","5f96fecca7ba48a39bf916d510ff076f","37eb2109dd6341c5b629f55dbc321331","490cf1a0ff6647398e151210dcb142da","01ffc16882c94c0880419cb08d62a68f","823e7e21557143dc83fbc691f6dad04e","f957529ed9f646d5bde425fd4783ac6b","59a81b54f1c14fb795cef8f6ceed845f","489b701025434738a3ab7a07093b8007","e5e247f142eb4b4e9a7afe8d25f24ef1","680b484ba65d4c84a09c36f0675365a9","ee6f37721502434fb3502dbe79a36c58","86a783bafcb348f3acc4f4e6f36aece9","eef329bf1ce8479f9d09aa8f905b1401"]},"id":"Avn2t5WwYo4v","executionInfo":{"status":"ok","timestamp":1669694727702,"user_tz":-360,"elapsed":67974,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"480c2481-db3a-405d-8a1f-2993ab5c1f78"},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/88.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4cb6e7dc51b940e68b07ecad7c855684"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.12k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"462893d7575147e7b575457763496b7a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.91M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"553a02568b664c8399fe0b4bde237da1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/65.0 [00:00<?, 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ineuron","userId":"16851312232179065356"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["def calculate_metric_on_test_ds(dataset, metric, model, tokenizer, \n","                               batch_size=16, device=device, \n","                               column_text=\"article\", \n","                               column_summary=\"highlights\"):\n","    article_batches = list(generate_batch_sized_chunks(dataset[column_text], batch_size))\n","    target_batches = list(generate_batch_sized_chunks(dataset[column_summary], batch_size))\n","\n","    for article_batch, target_batch in tqdm(\n","        zip(article_batches, target_batches), total=len(article_batches)):\n","        \n","        inputs = tokenizer(article_batch, max_length=1024,  truncation=True, \n","                        padding=\"max_length\", return_tensors=\"pt\")\n","        \n","        summaries = model.generate(input_ids=inputs[\"input_ids\"].to(device),\n","                         attention_mask=inputs[\"attention_mask\"].to(device), \n","                         length_penalty=0.8, num_beams=8, max_length=128)\n","        ''' parameter for length penalty ensures that the model does not generate sequences that are too long. '''\n","        \n","        # Finally, we decode the generated texts, \n","        # replace the  token, and add the decoded texts with the references to the metric.\n","        decoded_summaries = [tokenizer.decode(s, skip_special_tokens=True, \n","                                clean_up_tokenization_spaces=True) \n","               for s in summaries]      \n","        \n","        decoded_summaries = [d.replace(\"\", \" \") for d in decoded_summaries]\n","        \n","        \n","        metric.add_batch(predictions=decoded_summaries, references=target_batch)\n","        \n","    #  Finally compute and return the ROUGE scores.\n","    score = metric.compute()\n","    return score"],"metadata":{"id":"xRzbACdqYo2R","executionInfo":{"status":"ok","timestamp":1669694733041,"user_tz":-360,"elapsed":1,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}}},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":["### Load data\n","\n","Link: https://huggingface.co/datasets/samsum"],"metadata":{"id":"1rd7_MtTaot4"}},{"cell_type":"code","source":["dataset_samsum = load_dataset(\"samsum\")\n","\n","split_lengths = [len(dataset_samsum[split])for split in dataset_samsum]\n","\n","print(f\"Split lengths: {split_lengths}\")\n","print(f\"Features: {dataset_samsum['train'].column_names}\")\n","print(\"\\nDialogue:\")\n","\n","print(dataset_samsum[\"test\"][1][\"dialogue\"])\n","\n","print(\"\\nSummary:\")\n","\n","print(dataset_samsum[\"test\"][1][\"summary\"])"],"metadata":{"id":"-BGBwbDdaBMn","colab":{"base_uri":"https://localhost:8080/","height":631,"referenced_widgets":["7ea3fbaf97e146daa8d696700ef93b3a","459e65ae0de5460992373b8e15d11686","045f438693dd4c969bbe42a21bc1c1f9","e046b0f0cfb14cccb6117d1f4c81a063","199a16d074d5481a912c490053e1a120","13ca31c8af29475d8961c3e31d6302f2","98626524220e440784771eb0e4fa6eef","d430fe3b6d20480283846b457b70f2dd","f831099fd5b344ce892366dd16f09d1a","e6b5f258deaa450e834eebbefbe1ac29","87d77fa1fe5d493b83692a596d187a7a","114ae6e0166b40c4a0dfd743996851b7","c7855927a38a48beacde3b5be5185627","4b16c376c92c4cd5a254724d978d619b","e65780d1b70447aebb12b758651727b9","2b8e19190ad04f0d944d6ff62935ef90","172480419ccd4f4680789f9882914cc2","8b67a993e94b447c8af8925f2327f880","09f3f8466d06459694eca77700e7fdbb","6fc0d5d551f3451a9e6ffd112d5e1bc2","94418518216a4c13a5a1720fd022bdc9","40e7f8b61f9d407e9acafe43004617ef","6fd83e3f733b4d79af7d72523f61af4c","0290c839e78f448da5c5796d41effd8d","32f4043e52714e45a0a56cc0857f8009","93ad34c4c97145e7ae6e6e847a38e832","136b7c4d432546acb0f7813be8da0aee","26c8c2b17a744434b309fa0ce8e42a24","5d87ca23ea3f4780bcbb331b481cd20a","1d48b18d85024707bc5c0c99edf00bd4","855935ecf8394f318358c92e5815ac24","8d1d9e7f22b24736a1038858f2dd95c4","22782ebe88df44239043796aefa9436c","3bc5320a6e8d47dfad350fe123bcc360","cbcf77a183934f18a272cc938ccb2310","2316f7eaf7544df7abc1995571429ffa","c7273f6555774b55a3862551fee2f8b8","75771d54438e419abdc43dd3d1994fa2","63137621e32e47d2bc30bcda4c66b595","7655e77b5bdf4a15a78841e6aa68f61a","c90eea386b854d399f08e16231919eea","c4445d1a5d8443b29e0988b33199863b","9fbb53ad47f3435e9a1a4d64b870be0d","c2ec746502f04d7d949ce2086589df14","75b1deb2a0294795b4705e36b6b6aaaf","a273062dcd7a4e3dbea0bfb412554e65","cc26b4cfcfcd435b9e0b7f95299542f9","0ff9dbfb0cbb4a47b2159e8ec49f69a6","e94090f05f194140aa92d67b39137805","ea970e3dbcf9426ba32253275583719b","6fa94a134993493dbdaa7a33077af72b","26c0a3cc725c4452a44ad3f42b9a961a","3f99c0872c3049268490c03022b21391","721249813e1a4b6c94da77ad5c9d71f7","5c9e33c3628b46bcad70ab7763df1eb5","59018d7eada541b59c67a4424c97cf7f","aa43806d56174a41a8550fa5db8ebd94","e4e302b2d1764cd2b552c4221b7dbe56","ddd1f8d5b4bc4a8ea03d0b0b9e8e409c","437308daae154db6a6ffc5ac8edda9d2","d5a562f390f14ce1be321f9803540dfa","fc11754fb82e4dbeb4209196551f4d82","296d3ae953424a899f0b428d0b0568d9","72b9828e77b3459c885a55b803415947","e0316bf5dab04bb58293ef384d4d828c","04de14951a8349799f0eb1793bc13ced","ae68f6d4b66048709e462c103fbbf675","d077dc174a1e4d9ba9cc32ecb5a8b26d","3dff7c94312849ffafd8b5e93bce8c30","c33bcd82dded4a88bb0daf9675b370d8","a73ca89193764ab3a37ea36668af7f73","11194254e97641d9ad0a2222922b8878","68c8d4072f584fba91c5ec22fbad8788","885b01e2365940389174d38a275d32c8","d6ed558a4fee4df48f11ac08cceedc02","5867602d74474f85af03c6f4e2305565","53c8975344af4f8eafe690b82351ae4b","b41cf85fe814460aa4770d1ccba9887d","fa3d4f896d0f42629b1af3465cadb614","c526fe195cb7469fa44e05846f69ae03","9d9795dfc4be442085b99b9506057726","30ab68550eef4f1cadfa8c69ebff9447","ffbb7769ad964c37a6366a6b15bf8283","e8770cd7c6534c409741e61cf7ee20be","63cee27cc64d40c2bcf734dfffaba186","4e438ed80eba4d148315b1999f4c5dcb","5f936b16f8514e4880f57597e0eb48a8","a4b9b98ee9f04651a4545bc7b3142a1f"]},"executionInfo":{"status":"ok","timestamp":1669694793543,"user_tz":-360,"elapsed":14308,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"a62d7003-bad7-4986-9a15-1bf55a891888"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.36k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7ea3fbaf97e146daa8d696700ef93b3a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"114ae6e0166b40c4a0dfd743996851b7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/6.88k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6fd83e3f733b4d79af7d72523f61af4c"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset samsum/samsum to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/2.94M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3bc5320a6e8d47dfad350fe123bcc360"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/14732 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"75b1deb2a0294795b4705e36b6b6aaaf"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/819 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"59018d7eada541b59c67a4424c97cf7f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/818 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ae68f6d4b66048709e462c103fbbf675"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset samsum downloaded and prepared to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b41cf85fe814460aa4770d1ccba9887d"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Split lengths: [14732, 819, 818]\n","Features: ['id', 'dialogue', 'summary']\n","\n","Dialogue:\n","Eric: MACHINE!\r\n","Rob: That's so gr8!\r\n","Eric: I know! And shows how Americans see Russian ;)\r\n","Rob: And it's really funny!\r\n","Eric: I know! I especially like the train part!\r\n","Rob: Hahaha! No one talks to the machine like that!\r\n","Eric: Is this his only stand-up?\r\n","Rob: Idk. I'll check.\r\n","Eric: Sure.\r\n","Rob: Turns out no! There are some of his stand-ups on youtube.\r\n","Eric: Gr8! I'll watch them now!\r\n","Rob: Me too!\r\n","Eric: MACHINE!\r\n","Rob: MACHINE!\r\n","Eric: TTYL?\r\n","Rob: Sure :)\n","\n","Summary:\n","Eric and Rob are going to watch a stand-up on youtube.\n"]}]},{"cell_type":"markdown","source":["# Evaluating PEGASUS on SAMSum"],"metadata":{"id":"O3VfNRNCbiiQ"}},{"cell_type":"code","source":["dataset_samsum['test'][0]['dialogue']"],"metadata":{"id":"KS10obRRbuoH","colab":{"base_uri":"https://localhost:8080/","height":87},"executionInfo":{"status":"ok","timestamp":1669694798524,"user_tz":-360,"elapsed":599,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"25ebb83b-16b5-40bf-e21b-209a59445f77"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"Hannah: Hey, do you have Betty's number?\\nAmanda: Lemme check\\nHannah: <file_gif>\\nAmanda: Sorry, can't find it.\\nAmanda: Ask Larry\\nAmanda: He called her last time we were at the park together\\nHannah: I don't know him well\\nHannah: <file_gif>\\nAmanda: Don't be shy, he's very nice\\nHannah: If you say so..\\nHannah: I'd rather you texted him\\nAmanda: Just text him 🙂\\nHannah: Urgh.. Alright\\nHannah: Bye\\nAmanda: Bye bye\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["pipe = pipeline('summarization', model = model_ckpt )\n","\n","pipe_out = pipe(dataset_samsum['test'][0]['dialogue'] )\n","\n","print(pipe_out)"],"metadata":{"id":"kHxXy-i-aBKX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669694821861,"user_tz":-360,"elapsed":21008,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"228b0637-6565-4a4d-fb08-1b82a5dc00c4"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 128, but you input_length is only 122. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=61)\n"]},{"output_type":"stream","name":"stdout","text":["[{'summary_text': \"Amanda: Ask Larry Amanda: He called her last time we were at the park together .<n>Hannah: I'd rather you texted him .<n>Amanda: Just text him .\"}]\n"]}]},{"cell_type":"code","source":["print(pipe_out[0]['summary_text'].replace(\" .<n>\", \".\\n\"))"],"metadata":{"id":"GbGSyAP5aBHn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669694825089,"user_tz":-360,"elapsed":891,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"db570c27-a017-4fd0-9c11-2cf91cd7997d"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Amanda: Ask Larry Amanda: He called her last time we were at the park together.\n","Hannah: I'd rather you texted him.\n","Amanda: Just text him .\n"]}]},{"cell_type":"code","source":["rouge_metric = load_metric('rouge')\n","\n","score = calculate_metric_on_test_ds(dataset_samsum['test'], rouge_metric, model_pegasus, tokenizer, column_text = 'dialogue', column_summary='summary', batch_size=8)"],"metadata":{"id":"EQYsFtl3aBEw","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["39ae2695a40e4dcb8198dfe26dbdda72","ee013000153c47c98baedbf5aff2fd99","2ef608437abc4d778c58d70ea1d27f70","f2e8d79d87ad4628b0467d4636363009","04423bd164ea4bb9ac963f41e3a969e1","0608b8059f274f738915a873292daf1a","32cc28e28170439a88854bbca1198a9c","7696cfdab06e4122b538215e38b79a18","351fe5e60818468a9bc043d6d04ef6c6","79b2940f1b4f405ab356fe82557f6dcc","f8245a11aac74b31bf237c7cf24c063e"]},"executionInfo":{"status":"ok","timestamp":1669696057992,"user_tz":-360,"elapsed":1229742,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"9c2ec3e9-1432-454e-e67c-feeeeca0762a"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n","  after removing the cwd from sys.path.\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"39ae2695a40e4dcb8198dfe26dbdda72"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/103 [00:00<?, ?it/s]\u001b[A\n","  1%|          | 1/103 [00:14<24:31, 14.42s/it]\u001b[A\n","  2%|▏         | 2/103 [00:24<19:39, 11.68s/it]\u001b[A\n","  3%|▎         | 3/103 [00:36<20:10, 12.10s/it]\u001b[A\n","  4%|▍         | 4/103 [00:49<20:34, 12.47s/it]\u001b[A\n","  5%|▍         | 5/103 [00:58<18:24, 11.27s/it]\u001b[A\n","  6%|▌         | 6/103 [01:12<19:15, 11.91s/it]\u001b[A\n","  7%|▋         | 7/103 [01:25<19:40, 12.29s/it]\u001b[A\n","  8%|▊         | 8/103 [01:37<19:28, 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)\n","\n","pd.DataFrame(rouge_dict, index = ['pegasus'])"],"metadata":{"id":"x2yhgYIkYozf","colab":{"base_uri":"https://localhost:8080/","height":81},"executionInfo":{"status":"ok","timestamp":1669696080631,"user_tz":-360,"elapsed":782,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"d5fe43a6-dac9-4c05-8a85-64d4f6c6d578"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           rouge1    rouge2    rougeL  rougeLsum\n","pegasus  0.015558  0.000295  0.015537    0.01554"],"text/html":["\n","  <div id=\"df-78bb323f-54b2-435d-9f32-f36585a16c95\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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4))\n","axes[0].hist(dialogue_token_len, bins = 20, color = 'C0', edgecolor = 'C0' )\n","axes[0].set_title(\"Dialogue Token Length\")\n","axes[0].set_xlabel(\"Length\")\n","axes[0].set_ylabel(\"Count\")\n","\n","axes[1].hist(summary_token_len, bins = 20, color = 'C0', edgecolor = 'C0' )\n","axes[1].set_title(\"Summary Token Length\")\n","axes[1].set_xlabel(\"Length\")\n","plt.tight_layout()\n","plt.show()"],"metadata":{"id":"sbVwPRzSg-eJ","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1669620468853,"user_tz":-360,"elapsed":14449,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"0e55d3d2-bf42-48f4-fa93-9d275757550a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x288 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["def convert_examples_to_features(example_batch):\n","    input_encodings = tokenizer(example_batch['dialogue'] , max_length = 1024, truncation = True )\n","    \n","    with tokenizer.as_target_tokenizer():\n","        target_encodings = tokenizer(example_batch['summary'], max_length = 128, truncation = True )\n","        \n","    return {\n","        'input_ids' : input_encodings['input_ids'],\n","        'attention_mask': input_encodings['attention_mask'],\n","        'labels': target_encodings['input_ids']\n","    }\n","    \n","dataset_samsum_pt = dataset_samsum.map(convert_examples_to_features, batched = True)"],"metadata":{"id":"eKSxsVCGg-bg","colab":{"base_uri":"https://localhost:8080/","height":168,"referenced_widgets":["da54bf3c66fe469b9e938a077249c955","edf5f1317e744539a6db88bdc32550ee","1cd22040b21a40d8aa83c4a7cca71135","4814fe7f71594ffd80001a7c05ba41cd","4aa79542ebf84cdfa0013ed843475133","9afbada805684709a5df47fd99d91d0f","72a52b149c464fb99a512ca39faaad99","7b095c42806541c0a089d5a2fe440e0e","6295706a2af247588bc0bce54f6af17a","064aa70e10924ccb8c6a21b733efa7bf","9c29f86d7ea34f1c879ab14a90108437","bf92054e6e7948a99b630169b6d7f956","0d46ad33b5e54362b49bf07e0a424dca","4148a6b3b9c741fb9b44cdf5ff7adde0","94c2f287d75e42df892668b09fcc53e6","ab1520ccd2ba45a6ae82b88e27de8e10","2e11cd2e33d146be94efa294a744c0ce","bb677604af624f4b9041b03f9ef85a24","16315676cd7b43fca9704c2dcd280a17","7e5046a7e2b54dc3a0d986f0e9e81d39","ff66442dfe9344dba107f106c9602f0d","afa4712772884283bb77ca73d9fa787b","936bdde51a774edf8781ffd5defc7d95","41a8d9c31f9f4bb4a86fb04407a6e946","64f92577f11c4b4cac4583671220daec","90cead1c84ba4c25b65e31f6dcf40e55","88b5ed005af047d2b086e0af26e4d139","6ec6d06953584847a5748028a140155a","08854143620e4ccb9e1b14d429944d75","3332689a3ad742698152644286a5f346","aac48eec416749a7a67d06d22477ed54","b31ccdae25e64ea5954412ac53daafe0","1099e8c8e99d447fba16f2a9b391c071"]},"executionInfo":{"status":"ok","timestamp":1669696122188,"user_tz":-360,"elapsed":2579,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"193eacdf-535f-4011-90a3-320c05287c92"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/15 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"da54bf3c66fe469b9e938a077249c955"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3547: 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","  \"`as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your \"\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf92054e6e7948a99b630169b6d7f956"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"936bdde51a774edf8781ffd5defc7d95"}},"metadata":{}}]},{"cell_type":"code","source":["from transformers import DataCollatorForSeq2Seq\n","\n","seq2seq_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model_pegasus)"],"metadata":{"id":"9b0_45Neg-Y4","executionInfo":{"status":"ok","timestamp":1669696129426,"user_tz":-360,"elapsed":699,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CEkFXxyhpan7","executionInfo":{"status":"ok","timestamp":1669696246079,"user_tz":-360,"elapsed":70345,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"f6331fcc-40a7-4756-f0c0-10d0d0dc63ac"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["%cd /content/drive/MyDrive/005_BOKTIAR_AHMED_BAPPY/My_classes/FSDS_NOV_10_AM"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Sbe5ahpmpu9B","executionInfo":{"status":"ok","timestamp":1669696291982,"user_tz":-360,"elapsed":659,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"971eebf7-ff3f-4e31-8c92-7b2dff09c027"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/005_BOKTIAR_AHMED_BAPPY/My_classes/FSDS_NOV_10_AM\n"]}]},{"cell_type":"code","source":["from transformers import TrainingArguments, Trainer\n","\n","trainer_args = TrainingArguments(\n","    output_dir='pegasus-samsum', num_train_epochs=1, warmup_steps=500,\n","    per_device_train_batch_size=1, per_device_eval_batch_size=1,\n","    weight_decay=0.01, logging_steps=10,\n","    evaluation_strategy='steps', eval_steps=500, save_steps=1e6,\n","    gradient_accumulation_steps=16\n",") "],"metadata":{"id":"cMYgv4lNg-Vw","executionInfo":{"status":"ok","timestamp":1669696296676,"user_tz":-360,"elapsed":808,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["\n","trainer = Trainer(model=model_pegasus, args=trainer_args,\n","                  tokenizer=tokenizer, data_collator=seq2seq_data_collator,\n","                  train_dataset=dataset_samsum_pt[\"train\"], \n","                  eval_dataset=dataset_samsum_pt[\"validation\"])"],"metadata":{"id":"wCXvnV_4xrTR","executionInfo":{"status":"ok","timestamp":1669696303831,"user_tz":-360,"elapsed":921,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["trainer.train()\n"],"metadata":{"id":"qZRakE4ixuVx","colab":{"base_uri":"https://localhost:8080/","height":543},"executionInfo":{"status":"ok","timestamp":1669699018894,"user_tz":-360,"elapsed":2708532,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"fa996af0-4ab1-4ae1-a006-c19b5eedfea1"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stderr","text":["The following columns in the training set don't have a corresponding argument in `PegasusForConditionalGeneration.forward` and have been ignored: summary, dialogue, id. If summary, dialogue, id are not expected by `PegasusForConditionalGeneration.forward`,  you can safely ignore this message.\n","/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  FutureWarning,\n","***** Running training *****\n","  Num examples = 14732\n","  Num Epochs = 1\n","  Instantaneous batch size per device = 1\n","  Total train batch size (w. parallel, distributed & accumulation) = 16\n","  Gradient Accumulation steps = 16\n","  Total optimization steps = 920\n","  Number of trainable parameters = 568699904\n","You're using a PegasusTokenizerFast 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='920' max='920' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [920/920 45:04, Epoch 0/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.690200</td>\n","      <td>1.488429</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set don't have a corresponding argument in `PegasusForConditionalGeneration.forward` and have been ignored: summary, dialogue, id. If summary, dialogue, id are not expected by `PegasusForConditionalGeneration.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 818\n","  Batch size = 1\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=920, training_loss=1.828826087454091, metrics={'train_runtime': 2707.8434, 'train_samples_per_second': 5.44, 'train_steps_per_second': 0.34, 'total_flos': 5526961323663360.0, 'train_loss': 1.828826087454091, 'epoch': 1.0})"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["score = calculate_metric_on_test_ds(\n","    dataset_samsum['test'], rouge_metric, trainer.model, tokenizer, batch_size = 2, column_text = 'dialogue', column_summary= 'summary'\n",")\n","\n","rouge_dict = dict((rn, score[rn].mid.fmeasure ) for rn in rouge_names )\n","\n","pd.DataFrame(rouge_dict, index = [f'pegasus'] )"],"metadata":{"id":"JDkHo-dWxrPB","colab":{"base_uri":"https://localhost:8080/","height":98},"executionInfo":{"status":"ok","timestamp":1669700192711,"user_tz":-360,"elapsed":791756,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"e9b1ab71-429a-442a-b454-818be4f35a3c"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 410/410 [13:08<00:00,  1.92s/it]\n"]},{"output_type":"execute_result","data":{"text/plain":["           rouge1    rouge2    rougeL  rougeLsum\n","pegasus  0.018618  0.000297  0.018493   0.018518"],"text/html":["\n","  <div id=\"df-ec7f477c-d42d-4633-ac49-0fe86000f045\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>rouge1</th>\n","      <th>rouge2</th>\n","      <th>rougeL</th>\n","      <th>rougeLsum</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>pegasus</th>\n","      <td>0.018618</td>\n","      <td>0.000297</td>\n","      <td>0.018493</td>\n","      <td>0.018518</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec7f477c-d42d-4633-ac49-0fe86000f045')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-ec7f477c-d42d-4633-ac49-0fe86000f045 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-ec7f477c-d42d-4633-ac49-0fe86000f045');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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ineuron","userId":"16851312232179065356"}},"outputId":"54eb1c0b-fbbe-4061-cec5-eb67a63f7b4f"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["Configuration saved in pegasus-samsum-model/config.json\n","Model weights saved in pegasus-samsum-model/pytorch_model.bin\n"]}]},{"cell_type":"code","source":["## Save tokenizer\n","tokenizer.save_pretrained(\"tokenizer\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HfHbqWQoUJ1O","executionInfo":{"status":"ok","timestamp":1669699197925,"user_tz":-360,"elapsed":678,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"de3deb62-17b2-4316-ee0b-9abe75db9e5a"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stderr","text":["tokenizer config file saved in tokenizer/tokenizer_config.json\n","Special tokens file saved in tokenizer/special_tokens_map.json\n"]},{"output_type":"execute_result","data":{"text/plain":["('tokenizer/tokenizer_config.json',\n"," 'tokenizer/special_tokens_map.json',\n"," 'tokenizer/spiece.model',\n"," 'tokenizer/added_tokens.json',\n"," 'tokenizer/tokenizer.json')"]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","source":["# Test"],"metadata":{"id":"jAxQeNWlxzjy"}},{"cell_type":"code","source":["dataset_samsum = load_dataset(\"samsum\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":86,"referenced_widgets":["f606d29dd50b4de0aa13dc58adcf2a85","bdbaaacfdbfe4b67ad2a195f71746f78","7e45d449dec84527bb1f052dccaf1422","990a0bd372bd4eb8a7c994750aa5cc42","649c891901ee4a849de396205d42a92a","1da9d26f57ad4db79b7e3c891656ff36","cf77e11c80714a398c3c89bacb535ab9","b1f1cb8d52e143cf82827f89edb7354f","e515bfcc553c48b69ab6de979eef6320","a7c85c4d3ffa43b0a0bc5d0beade628d","f0657f0f13e34a00bd3bba0319af7af6"]},"id":"FkkMcgGKUy7V","executionInfo":{"status":"ok","timestamp":1669699221213,"user_tz":-360,"elapsed":5500,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"a0dc1892-0dc8-4d71-fe49-b22a1cf4f992"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Found cached dataset samsum (/root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e)\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f606d29dd50b4de0aa13dc58adcf2a85"}},"metadata":{}}]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained(\"tokenizer\")"],"metadata":{"id":"06o0uV9DVJhQ","executionInfo":{"status":"ok","timestamp":1669699244137,"user_tz":-360,"elapsed":817,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9af5a4b3-5c73-4b6b-9705-79324786d7b6"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["loading file spiece.model\n","loading file tokenizer.json\n","loading file added_tokens.json\n","loading file special_tokens_map.json\n","loading file tokenizer_config.json\n"]}]},{"cell_type":"code","source":["sample_text = dataset_samsum[\"test\"][0][\"dialogue\"]\n","\n","reference = dataset_samsum[\"test\"][0][\"summary\"]"],"metadata":{"id":"JT8yWCAB1WAZ","executionInfo":{"status":"ok","timestamp":1669699303314,"user_tz":-360,"elapsed":624,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["gen_kwargs = {\"length_penalty\": 0.8, \"num_beams\":8, \"max_length\": 128}\n","\n","pipe = pipeline(\"summarization\", model=\"pegasus-samsum-model\",tokenizer=tokenizer)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rSOKE9fC1eUh","executionInfo":{"status":"ok","timestamp":1669699370847,"user_tz":-360,"elapsed":12079,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"99410010-dbb5-4149-a7f5-ded13aa0b151"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stderr","text":["loading configuration file pegasus-samsum-model/config.json\n","Model config PegasusConfig {\n","  \"_name_or_path\": \"pegasus-samsum-model\",\n","  \"activation_dropout\": 0.1,\n","  \"activation_function\": \"relu\",\n","  \"add_bias_logits\": false,\n","  \"add_final_layer_norm\": true,\n","  \"architectures\": [\n","    \"PegasusForConditionalGeneration\"\n","  ],\n","  \"attention_dropout\": 0.1,\n","  \"bos_token_id\": 0,\n","  \"classif_dropout\": 0.0,\n","  \"classifier_dropout\": 0.0,\n","  \"d_model\": 1024,\n","  \"decoder_attention_heads\": 16,\n","  \"decoder_ffn_dim\": 4096,\n","  \"decoder_layerdrop\": 0.0,\n","  \"decoder_layers\": 16,\n","  \"decoder_start_token_id\": 0,\n","  \"dropout\": 0.1,\n","  \"encoder_attention_heads\": 16,\n","  \"encoder_ffn_dim\": 4096,\n","  \"encoder_layerdrop\": 0.0,\n","  \"encoder_layers\": 16,\n","  \"eos_token_id\": 1,\n","  \"extra_pos_embeddings\": 1,\n","  \"forced_eos_token_id\": 1,\n","  \"id2label\": {\n","    \"0\": \"LABEL_0\",\n","    \"1\": \"LABEL_1\",\n","    \"2\": \"LABEL_2\"\n","  },\n","  \"init_std\": 0.02,\n","  \"is_encoder_decoder\": true,\n","  \"label2id\": {\n","    \"LABEL_0\": 0,\n","    \"LABEL_1\": 1,\n","    \"LABEL_2\": 2\n","  },\n","  \"length_penalty\": 0.8,\n","  \"max_length\": 128,\n","  \"max_position_embeddings\": 1024,\n","  \"min_length\": 32,\n","  \"model_type\": \"pegasus\",\n","  \"normalize_before\": true,\n","  \"normalize_embedding\": false,\n","  \"num_beams\": 8,\n","  \"num_hidden_layers\": 16,\n","  \"pad_token_id\": 0,\n","  \"scale_embedding\": true,\n","  \"static_position_embeddings\": true,\n","  \"torch_dtype\": \"float32\",\n","  \"transformers_version\": \"4.24.0\",\n","  \"use_cache\": true,\n","  \"vocab_size\": 96103\n","}\n","\n","loading configuration file pegasus-samsum-model/config.json\n","Model config PegasusConfig {\n","  \"_name_or_path\": \"pegasus-samsum-model\",\n","  \"activation_dropout\": 0.1,\n","  \"activation_function\": \"relu\",\n","  \"add_bias_logits\": false,\n","  \"add_final_layer_norm\": true,\n","  \"architectures\": [\n","    \"PegasusForConditionalGeneration\"\n","  ],\n","  \"attention_dropout\": 0.1,\n","  \"bos_token_id\": 0,\n","  \"classif_dropout\": 0.0,\n","  \"classifier_dropout\": 0.0,\n","  \"d_model\": 1024,\n","  \"decoder_attention_heads\": 16,\n","  \"decoder_ffn_dim\": 4096,\n","  \"decoder_layerdrop\": 0.0,\n","  \"decoder_layers\": 16,\n","  \"decoder_start_token_id\": 0,\n","  \"dropout\": 0.1,\n","  \"encoder_attention_heads\": 16,\n","  \"encoder_ffn_dim\": 4096,\n","  \"encoder_layerdrop\": 0.0,\n","  \"encoder_layers\": 16,\n","  \"eos_token_id\": 1,\n","  \"extra_pos_embeddings\": 1,\n","  \"forced_eos_token_id\": 1,\n","  \"id2label\": {\n","    \"0\": \"LABEL_0\",\n","    \"1\": \"LABEL_1\",\n","    \"2\": \"LABEL_2\"\n","  },\n","  \"init_std\": 0.02,\n","  \"is_encoder_decoder\": true,\n","  \"label2id\": {\n","    \"LABEL_0\": 0,\n","    \"LABEL_1\": 1,\n","    \"LABEL_2\": 2\n","  },\n","  \"length_penalty\": 0.8,\n","  \"max_length\": 128,\n","  \"max_position_embeddings\": 1024,\n","  \"min_length\": 32,\n","  \"model_type\": \"pegasus\",\n","  \"normalize_before\": true,\n","  \"normalize_embedding\": false,\n","  \"num_beams\": 8,\n","  \"num_hidden_layers\": 16,\n","  \"pad_token_id\": 0,\n","  \"scale_embedding\": true,\n","  \"static_position_embeddings\": true,\n","  \"torch_dtype\": \"float32\",\n","  \"transformers_version\": \"4.24.0\",\n","  \"use_cache\": true,\n","  \"vocab_size\": 96103\n","}\n","\n","loading weights file pegasus-samsum-model/pytorch_model.bin\n","All model checkpoint weights were used when initializing PegasusForConditionalGeneration.\n","\n","All the weights of PegasusForConditionalGeneration were initialized from the model checkpoint at pegasus-samsum-model.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use PegasusForConditionalGeneration for predictions without further training.\n"]}]},{"cell_type":"code","source":["print(\"Dialogue:\")\n","print(sample_text)\n","\n","\n","print(\"\\nReference Summary:\")\n","print(reference)\n","\n","\n","print(\"\\nModel Summary:\")\n","print(pipe(sample_text, **gen_kwargs)[0][\"summary_text\"])"],"metadata":{"id":"RZPa0R4wxrMo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669699380905,"user_tz":-360,"elapsed":5904,"user":{"displayName":"colab0 ineuron","userId":"16851312232179065356"}},"outputId":"365ae136-fb17-4d10-f753-bfcc28668283"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stderr","text":["Your max_length is set to 128, but you input_length is only 122. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=61)\n"]},{"output_type":"stream","name":"stdout","text":["Dialogue:\n","Hannah: Hey, do you have Betty's number?\n","Amanda: Lemme check\n","Hannah: <file_gif>\n","Amanda: Sorry, can't find it.\n","Amanda: Ask Larry\n","Amanda: He called her last time we were at the park together\n","Hannah: I don't know him well\n","Hannah: <file_gif>\n","Amanda: Don't be shy, he's very nice\n","Hannah: If you say so..\n","Hannah: I'd rather you texted him\n","Amanda: Just text him 🙂\n","Hannah: Urgh.. Alright\n","Hannah: Bye\n","Amanda: Bye bye\n","\n","Reference Summary:\n","Hannah needs Betty's number but Amanda doesn't have it. She needs to contact Larry.\n","\n","Model Summary:\n","Amanda can't find Betty's number. Larry called Betty last time they were at the park together. Hannah wants Amanda to text Larry. Amanda will text Larry.\n"]}]}]}