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torch\n","import evaluate\n","from sklearn.model_selection import train_test_split\n","import pytorch_lightning as pl\n","from torch.utils.data import Dataset, DataLoader\n","\n","from nltk.translate.bleu_score import sentence_bleu\n","from transformers import BartTokenizer, BartForConditionalGeneration\n","from transformers import AdamW, get_linear_schedule_with_warmup\n","from transformers import BartTokenizer"],"metadata":{"id":"o_o1ymgLQqAl","executionInfo":{"status":"ok","timestamp":1727444170289,"user_tz":240,"elapsed":11128,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["# Load the data\n","subset_path= '/content/drive/My Drive/BART_Abstract_Text_Summarization/curated_data_subset.csv'\n","full_data_path= '/content/drive/My Drive/BART_Abstract_Text_Summarization/curated_data.csv'\n","\n","# Read the CSV files\n","df_subset= pd.read_csv(subset_path)\n","df_full= pd.read_csv(full_data_path)\n","\n","# Display the first few rows to explore the structure\n","print('Subset Data:')\n","print(df_subset.head())\n","\n","print('\\nFull Data:')\n","print(df_full.head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"khsz_y_MQC_J","executionInfo":{"status":"ok","timestamp":1727444183006,"user_tz":240,"elapsed":12720,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"6e77af41-10bd-48ee-c0a2-ebfdc3bdcd62"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Subset Data:\n","   Unnamed: 0                                              title  \\\n","0           0  Tencent gains approval to sell mutual funds to...   \n","1           1  India testing blockchains in education, health...   \n","2           2  Higher living wage risks robot takeover of low...   \n","3           3  Regus WeWork may seek stock-market flotation t...   \n","4           4  AMD poised to gain market share as Intel pound...   \n","\n","                                             summary  \\\n","0  Tencent has been granted a licence from the Ch...   \n","1  India is testing blockchain applications in ed...   \n","2  The UK Institute for Fiscal Studies has warned...   \n","3  Co-working start-up WeWork may go public this ...   \n","4  Intel has seen more than $11bn wiped off its m...   \n","\n","                                                 url  \\\n","0  http://www.scmp.com/business/companies/article...   \n","1  https://www.vccircle.com/niti-aayog-explores-b...   \n","2  https://news.sky.com/story/ifs-living-wage-inc...   \n","3  https://www.fool.com/investing/2018/01/03/will...   \n","4  https://www.cnbc.com/2018/01/05/amd-is-big-win...   \n","\n","                         date  \\\n","0  2018-01-04 11:49:58.900000   \n","1  2018-01-04 11:33:08.257000   \n","2  2018-01-04 11:32:10.223000   \n","3  2018-01-04 11:31:59.617000   \n","4  2018-01-04 11:17:13.277000   \n","\n","                                     article_content  \n","0  Traditional finance houses now being seriously...  \n","1  The Indian government's policy think tank, Nit...  \n","2  Increases in minimum wage levels risk raising ...  \n","3  Depending on whom you ask, WeWork is either a ...  \n","4  AMD is big winner from chip flaw fiasco as mor...  \n","\n","Full Data:\n","                                               title  \\\n","0  Tencent gains approval to sell mutual funds to...   \n","1  India testing blockchains in education, health...   \n","2  Higher living wage risks robot takeover of low...   \n","3  Regus WeWork may seek stock-market flotation t...   \n","4  AMD poised to gain market share as Intel pound...   \n","\n","                                             summary  \\\n","0  Tencent has been granted a licence from the Ch...   \n","1  India is testing blockchain applications in ed...   \n","2  The UK Institute for Fiscal Studies has warned...   \n","3  Co-working start-up WeWork may go public this ...   \n","4  Intel has seen more than $11bn wiped off its m...   \n","\n","                                                 url  \\\n","0  http://www.scmp.com/business/companies/article...   \n","1  https://www.vccircle.com/niti-aayog-explores-b...   \n","2  https://news.sky.com/story/ifs-living-wage-inc...   \n","3  https://www.fool.com/investing/2018/01/03/will...   \n","4  https://www.cnbc.com/2018/01/05/amd-is-big-win...   \n","\n","                         date  \\\n","0  2018-01-04 11:49:58.900000   \n","1  2018-01-04 11:33:08.257000   \n","2  2018-01-04 11:32:10.223000   \n","3  2018-01-04 11:31:59.617000   \n","4  2018-01-04 11:17:13.277000   \n","\n","                                     article_content  \n","0  Traditional finance houses now being seriously...  \n","1  The Indian government's policy think tank, Nit...  \n","2  Increases in minimum wage levels risk raising ...  \n","3  Depending on whom you ask, WeWork is either a ...  \n","4  AMD is big winner from chip flaw fiasco as mor...  \n"]}]},{"cell_type":"code","source":["# Load the tokenizer\n","tokenizer= BartTokenizer.from_pretrained('facebook/bart-large-cnn')\n","\n","# Ensure all content is string type and handle missing values\n","df_full['article_content']= df_full['article_content'].fillna('').astype(str)\n","df_full['summary']= df_full['summary'].fillna('').astype(str)\n","\n","# Now tokenize the article content and summary\n","df_full['article_content']= df_full['article_content'].apply(lambda x: tokenizer.batch_encode_plus(\n","    [x], max_length= 512, padding= 'max_length', truncation= True, return_tensors= 'pt'))\n","\n","df_full['summary']= df_full['summary'].apply(lambda x: tokenizer.batch_encode_plus(\n","    [x], max_length= 150, padding= 'max_length', truncation= True, return_tensors= 'pt'))\n","\n","# View tokenized data\n","print(df_full[['article_content', 'summary']].head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":415,"referenced_widgets":["cba3297d2efd4fbeafbd5625f5d8ce28","c901fdca68b7461a868ef9092fd4123b","8d9b87abc8ce4e7993f5b575e69a4418","6ed1f04d5d8949fbb06f90fe3128758d","8462bbf6c65742f5bcffe7851339c8a8","d86b5f51bb834499aa7e750c3f18bd53","d3c41cdfee164507aeff203627d80dc5","d0d1ef411d54435288bf2770c4ca649b","eb7e6ab3443b4802bd06aac9628d0943","96284d07f4e040b4afcfb504fe8963fc","d608be993c024714a424153c930052a4","ed2df02a07c64c75bc459e4dc217074f","1bacf4b6dacf4a9e984e96275f35b440","67c0b449afd04306b965322c927a5e41","90a9db7eee114c9fa050329a42d7a148","973c104bfd3849a2b3870ad1ce89d4c7","95ab2dd6f16b4ba8968dcc66d6064a67","f395ed5d42e4457f83ba9992670218de","08ae9805958a4478893a2a1c1535fcb3","4d8147fcb0024073b0f9f751d20d11a3","e365398cd7f44b5a8dcda7fe2d5871c6","d10111f0cc3745bdbd4c225700dc14eb","b24ce2b952f94439a63611621dec1059","9d0705fda1be4a5fb3e38d14f294b3d3","96c681f429fc4cfd96ebec39d4a51558","d4081753761e44b2b5eefbbf02d693be","59a6208187034c96be72f66c6c170e68","ccacac222e7640f4bd2ae412f9e84ab4","34a60aabf9a94377931eda1e54177e0a","d594f0d37cff45e79090ac4efaeaf03c","56abf6f408d04221a1de9f851e3340c7","d970c0d6042d4a4799d75aa4b85bff4f","ab79ee703fc442eea2e99da6bcac076b","f6853c44d6a84809b772514c9e89dd13","8a7951ea680040e9b4a7b1e3f8b2ae65","9954e1e4854848fcbde34b891f164378","18500cbd71944fc8b7115f7cc63a09a9","d16b95fe8c0745c1a8a81c90708ac161","e2998c5feafa4efdb32c75add1a79622","cebcaad95d3f4d96af007460847609a3","02a6cc9c2380475e9cc79d1b46559211","cb94387cd5cd4143af8c2ec962bf88b0","3c0cc0184e294063abf2c3053e37566a","31c96461ee40403eb001f6f847c503f4"]},"id":"Mpjn_O5_Q0D1","executionInfo":{"status":"ok","timestamp":1727444358371,"user_tz":240,"elapsed":175370,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"1b858c1f-ad78-4751-fead-7c896c909c97"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cba3297d2efd4fbeafbd5625f5d8ce28"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ed2df02a07c64c75bc459e4dc217074f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b24ce2b952f94439a63611621dec1059"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f6853c44d6a84809b772514c9e89dd13"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["               article_content                      summary\n","0  [input_ids, attention_mask]  [input_ids, attention_mask]\n","1  [input_ids, attention_mask]  [input_ids, attention_mask]\n","2  [input_ids, attention_mask]  [input_ids, attention_mask]\n","3  [input_ids, attention_mask]  [input_ids, attention_mask]\n","4  [input_ids, attention_mask]  [input_ids, attention_mask]\n"]}]},{"cell_type":"code","source":["# Perform train-test split (80% training, 20% validation)\n","train_df, val_df= train_test_split(df_full, test_size=0.2, random_state=42)\n","\n","# Print the size of each set\n","print(f'Training set size: {len(train_df)}')\n","print(f'Validation set size: {len(val_df)}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Stp0fU10Vwnr","executionInfo":{"status":"ok","timestamp":1727444358372,"user_tz":240,"elapsed":8,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"b5d841f4-9109-4f7e-f50f-28d0ddb9f22d"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Training set size: 30213\n","Validation set size: 7554\n"]}]},{"cell_type":"code","source":["# Define a class that helps load batches of tokenized data\n","class CustomDataset(torch.utils.data.Dataset):\n","    def __init__(self, data, tokenizer, source_len, target_len):\n","        self.tokenizer= tokenizer\n","        self.data= data\n","        self.source_len= source_len\n","        self.target_len= target_len\n","        self.source_text= data['article_content']\n","        self.target_text= data['summary']\n","\n","    def __len__(self):\n","\n","        # Return the number of examples\n","        return len(self.target_text)\n","\n","    def __getitem__(self, index):\n","\n","        # Get the source (article content) and target (summary) text\n","        source_text= str(self.source_text[index])\n","        target_text= str(self.target_text[index])\n","\n","        # Tokenize the source and target text\n","        source= self.tokenizer.batch_encode_plus(\n","            [source_text],\n","            max_length= self.source_len,\n","            padding= 'max_length',\n","            truncation= True,\n","            return_tensors= 'pt'\n","        )\n","\n","        target= self.tokenizer.batch_encode_plus(\n","            [target_text],\n","            max_length= self.target_len,\n","            padding= 'max_length',\n","            truncation= True,\n","            return_tensors= 'pt'\n","        )\n","\n","        # Return the tokenized input and target\n","        return {\n","            'source_ids': source['input_ids'].squeeze(),\n","            'source_mask': source['attention_mask'].squeeze(),\n","            'target_ids': target['input_ids'].squeeze(),\n","            'target_mask': target['attention_mask'].squeeze()\n","        }"],"metadata":{"id":"vNuGJ99uWESl","executionInfo":{"status":"ok","timestamp":1727444358372,"user_tz":240,"elapsed":5,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# Define a function to create data loaders for the training and validation sets\n","def create_data_loader(dataframe, tokenizer, source_len, target_len, batch_size):\n","    dataset= CustomDataset(\n","        data= dataframe,\n","        tokenizer= tokenizer,\n","        source_len= source_len,\n","        target_len= target_len\n","    )\n","\n","    return DataLoader(dataset, batch_size= batch_size, shuffle= True, num_workers= 4)\n","\n","# Define parameters for the DataLoader\n","BATCH_SIZE= 8\n","SOURCE_LEN= 512\n","TARGET_LEN= 150\n","\n","# Reset the indices of both training and validation sets\n","train_df= train_df.reset_index(drop= True)\n","val_df= val_df.reset_index(drop= True)\n","\n","# Create training and validation data loaders\n","train_data_loader= create_data_loader(train_df, tokenizer, SOURCE_LEN, TARGET_LEN, BATCH_SIZE)\n","val_data_loader= create_data_loader(val_df, tokenizer, SOURCE_LEN, TARGET_LEN, BATCH_SIZE)\n","\n","# Check the DataLoader output\n","data= next(iter(train_data_loader))\n","print(data.keys())\n","print(f\"Source_ids shape: {data['source_ids'].shape}\")\n","print(f\"Target_ids shape: {data['target_ids'].shape}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NBDNxrq-W9V4","executionInfo":{"status":"ok","timestamp":1727444358713,"user_tz":240,"elapsed":346,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"acfa1ba6-c45c-49f6-db6e-9e019d17c81d"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["dict_keys(['source_ids', 'source_mask', 'target_ids', 'target_mask'])\n","Source_ids shape: torch.Size([8, 512])\n","Target_ids shape: torch.Size([8, 150])\n"]}]},{"cell_type":"code","source":["# Define the Abstractive Summarization Model using PyTorch Lightning\n","class AbstractiveSummarizationModel(pl.LightningModule):\n","\n","    # Initialize the model and tokenizer\n","    def __init__(self, model, tokenizer):\n","        super(AbstractiveSummarizationModel, self).__init__()\n","        self.model= model  # BART model for summarization\n","        self.tokenizer= tokenizer  # Tokenizer used to preprocess text data\n","\n","    # Forward pass - how the model processes input and generates output\n","    def forward(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask=None):\n","        outputs= self.model(\n","            input_ids= input_ids,  # Tokenized article content\n","            attention_mask= attention_mask,  # Mask to focus on real tokens (ignore padding)\n","            decoder_input_ids= decoder_input_ids,  # Tokenized summary (target text)\n","            decoder_attention_mask= decoder_attention_mask  # Mask to ignore padding in summary\n","        )\n","        return outputs.loss, outputs.logits  # Return loss and model output (logits)\n","\n","    # Define one step of training - how the model learns from the data\n","    def training_step(self, batch, batch_idx):\n","        input_ids= batch['source_ids']  # Tokenized articles (input)\n","        attention_mask= batch['source_mask']  # Attention mask for the input\n","        decoder_input_ids= batch['target_ids']  # Tokenized summaries (target)\n","        decoder_attention_mask= batch['target_mask']  # Attention mask for the summaries\n","\n","        # Perform forward pass for training\n","        loss, outputs= self(\n","            input_ids= input_ids,\n","            attention_mask= attention_mask,\n","            decoder_input_ids= decoder_input_ids,\n","            decoder_attention_mask= decoder_attention_mask\n","        )\n","\n","        # Ensure that the loss is not None before logging\n","        if loss is not None:\n","            self.log(\"train_loss\", loss)\n","        return loss\n","\n","    # Define one step of validation - how the model evaluates its performance\n","    def validation_step(self, batch, batch_idx):\n","        input_ids= batch['source_ids']  # Tokenized articles (input)\n","        attention_mask= batch['source_mask']  # Attention mask for the input\n","        decoder_input_ids= batch['target_ids']  # Tokenized summaries (target)\n","        decoder_attention_mask= batch['target_mask']  # Attention mask for the summaries\n","\n","        # Perform forward pass for validation\n","        loss, outputs= self(\n","            input_ids=input_ids,\n","            attention_mask= attention_mask,\n","            decoder_input_ids= decoder_input_ids,\n","            decoder_attention_mask= decoder_attention_mask\n","        )\n","\n","        # Ensure that loss is not None before logging\n","        if loss is not None:\n","            self.log('val_loss', loss)\n","        return loss\n","\n","    # Define the optimizer - AdamW optimizer with learning rate of 3e-5\n","    def configure_optimizers(self):\n","        return torch.optim.AdamW(self.parameters(), lr= 3e-5)"],"metadata":{"id":"c9doOxH9Ygcy","executionInfo":{"status":"ok","timestamp":1727444358713,"user_tz":240,"elapsed":4,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["# Load the pretrained BART model for conditional generation\n","bart_model= BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')\n","\n","# Instantiate the summarization model we defined earlier\n","model= AbstractiveSummarizationModel(model= bart_model, tokenizer= tokenizer)\n","\n","# Determine whether a GPU is available\n","accelerator= 'gpu' if torch.cuda.is_available() else 'cpu'\n","devices= 1\n","\n","# Initialize the PyTorch Lightning trainer\n","trainer= pl.Trainer(\n","    max_epochs= 3,\n","    devices= devices,\n","    accelerator= accelerator,\n","    enable_progress_bar= True\n",")\n","\n","# Start the training process\n","trainer.fit(model, train_data_loader, val_data_loader)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":454,"referenced_widgets":["d01c4aa9e58142eea43e4345227ff74a","b39ef13807a24caf9c16d6f1dfd2e39c","33cbe61b853749b3afcfb299001d896e","6c06c9ad7e8a4aa2a7b377cdd3ce0dcc","eec84a9b14d04465801f167564436abc","ff9c2f44bf9c41fba9c130b998939d0c","ade8f5e2062f4f1faf5fea4d296af7c7","7aea19a601354e349e2fa703ecb04b97","ec4432a28635400aaae464afc3b61eb4","75f938a6c06741d8b817e912cf63d8a6","ded89cb5fa0a4e1187cc8bc980f4f129","934e10cbdd53407f9ecf3f5e840d25e2","a7145113153a4a7a9bc36be6b49c66cf","a657ee2a96f6446ab923c15a0ba14330","ea8f44e75d744bce923cedcd12e31acd","7d297c4f54664717948f79a39e984765","d8d0e34ba06c41b79fdd01590a5ec314","f7ace4c6b9974f5ab25679e3f7c110c0","01a469aaa05a4182acb102dec621e346","29bdb386a1174f64bb8d70362a87a0b6","27c00565ac57418c8fe4c0afec2f0dbf","489ffdd6fcf34295a92caa1de52acd65","c0db1779b78645e8a2bdfc32072fea18","09cdda76c98b4730b2a4e5bedff7a2d6","e979d412e01a40a09080519138d52edd","38b107d2642e4d43b2809f104a6f8515","7d003324d35445eba58c9ac646410309","6d243999308944a6b0f8a55444659566","cdbab1484c8b4a629bce32669533f81b","adc4663826654ea78f5a8ec5c5acf03e","da0b3d62c4f742088b958321d1435f04","ff1617494d464504acd9becc4ec4ab21","2e2f8c6c77f94e34b103c314b399c087","85258d89a85242ce8a57c19bb53a0d82","1550b5254df947e7a506675a91f5e8ff","e2590be74bba4b819313c4902c1661d0","b06a5c529fc348718a3041528c0d0356","b5d867181ea741b08f2b20aa8c642ec7","b7d9ad20f0e64f7a8cbb6ac8a07388df","944171a6f27146f78babfc9318a62213","1d84d341a35f46c1b6c58d7512804c04","432c08c1e77c45abb0d5d3b02f67781c","5396d5f74aa246209e111fbc4f97633d","a91ea84b792e4fad8d8f0de16b33e28c","259e2816dd2241e8bcce54794e67b067","548733db28bf42e3a70d4065768183c4","127f44c15e124055a0a0934b7f1d6395","838b47f6a3404afca8f7efdd7fdc9057","708fc32e4dfa444f99e5fd7e004460fa","5522438f17554b44aef681031aa593a4","497a1321c3c54414a8b495dc36399a57","ac50e2fb10274e2d82855bdb4a61ac96","1c0f1157f6d24e188e926464612d5496","4044d64a97234a4eb59ae1aeba961b64","3bffdee6340244e28c561f5b21e38a22","13a6a54b56824c58896954df75e314c5","ed35139dfbe240578b4b0048856e713f","8ccfa1852d2f4b998ee021b3ff605ad1","1c0240399f4147c78990fbb10d484a2f","ae576c87426e4a868842a26222ca692f","2c9fa00789c2447c9cd41e5d588c51a9","afaae7e1c537458b82ef40a59ee215f2","e35283f49d254787bf63f04db60e8ebd","c1a47bf64f8f4332b21c190d96df2d8e","e40f754d2341417e97bd84451141e45c","13c0856b95084e308c6daf84da91c78a","a2aec386e5344602bb13ae01a0516b45","e72d650c935a44dfb26150f33a186de4","9c0544c3483d4449a8eaeef39faa3a11","197c48a489c0489fb66932716f71178f","afe67a8fbb724d38a5d1af43b43e06c4","8f27be6727c8438ba050c8adf7036b20","0cb9d577e79e4de68fd922e3dd4220bb","256b1185315647348ffe38b21b095b35","e0c0ea64d4304a4a89aa8424704e7996","80d5199802ab4868be184459aaeb567f","6f678dcd496f4db984709d5378bc6169"]},"id":"pn7ddPFRZqHB","executionInfo":{"status":"ok","timestamp":1727452714580,"user_tz":240,"elapsed":8355870,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"be3b4aeb-4a04-4e99-b5f5-6ed5b4642438"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d01c4aa9e58142eea43e4345227ff74a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"934e10cbdd53407f9ecf3f5e840d25e2"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n","INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n","INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n","INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n","INFO:pytorch_lightning.callbacks.model_summary:\n","  | Name  | Type                         | Params | Mode\n","--------------------------------------------------------------\n","0 | model | BartForConditionalGeneration | 406 M  | eval\n","--------------------------------------------------------------\n","406 M     Trainable params\n","0         Non-trainable params\n","406 M     Total params\n","1,625.162 Total estimated model params size (MB)\n","0         Modules in train mode\n","350       Modules in eval mode\n"]},{"output_type":"display_data","data":{"text/plain":["Sanity Checking: |          | 0/? [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c0db1779b78645e8a2bdfc32072fea18"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:475: Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n"]},{"output_type":"display_data","data":{"text/plain":["Training: |          | 0/? [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"85258d89a85242ce8a57c19bb53a0d82"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/optimization/automatic.py:132: `training_step` returned `None`. If this was on purpose, ignore this warning...\n"]},{"output_type":"display_data","data":{"text/plain":["Validation: |          | 0/? [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"259e2816dd2241e8bcce54794e67b067"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Validation: |          | 0/? [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"13a6a54b56824c58896954df75e314c5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Validation: |          | 0/? [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a2aec386e5344602bb13ae01a0516b45"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=3` reached.\n"]}]},{"cell_type":"code","source":["# Define the summarization function based on ProjectPro\n","def summarize_article(article):\n","\n","    # Load BART model and tokenizer\n","    model_name= 'facebook/bart-large-cnn'\n","    tokenizer= BartTokenizer.from_pretrained(model_name)\n","    model= BartForConditionalGeneration.from_pretrained(model_name)\n","\n","    # Tokenize and encode the article\n","    inputs= tokenizer.encode(article, return_tensors= 'pt', max_length= 1024, truncation= True)\n","\n","    # Generate summary\n","    summary_ids= model.generate(inputs, num_beams= 4, max_length= 250, early_stopping= True)\n","\n","    # Decode the summary with explicit clean_up_tokenization_spaces set\n","    summary= tokenizer.decode(summary_ids[0], skip_special_tokens= True, clean_up_tokenization_spaces= True)\n","\n","    return summary\n","\n","# Example usage\n","article= '''\n","My friends are cool but they eat too many carbs.\n","'''\n","\n","summary= summarize_article(article)\n","print('Summary:')\n","print(summary)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_1unsX3hPI4Z","executionInfo":{"status":"ok","timestamp":1727452721739,"user_tz":240,"elapsed":7185,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"69d996b0-333e-4e51-ebdd-31ebad3b5b11"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["Summary:\n","My friends are cool but they eat too many carbs. That's what this is all about. I don't want you to think I'm a bad person. I'm not. I just don't like to be around people who eat too much carbs. This is my way of telling you that.\n"]}]},{"cell_type":"code","source":["# Define reference summary for comparison (human-written summary)\n","reference_summary= 'My friends are nice, but they consume too many carbohydrates.'\n","\n","# Generate model summary\n","generated_summary= summarize_article(article)\n","\n","print('Generated Summary:')\n","print(generated_summary)\n","\n","# Load the ROUGE metric using the evaluate library\n","rouge= evaluate.load('rouge')\n","\n","# Calculate the ROUGE score\n","scores= rouge.compute(predictions=[generated_summary], references=[reference_summary])\n","\n","# Print ROUGE scores\n","print('\\nROUGE scores:')\n","print(scores)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":158,"referenced_widgets":["b013da48328d4a23b130b0bce523c17f","bee28edafae34fbb99cf3d89a18bde6f","508c8cfbfeca458d97f0a0064172b691","1deffed8e91749eca6acd6a7b96c5b9e","a16aca54c20843e789e5dd7d7c63ecc9","6ca376c3f0d349a696e6b6a908ef3e7a","1bb86b907fcc4b609413848ac427de42","4b4a5de4984446f5a80e771242c70f4d","fa67eb98b1ae43a5a4d15cf99962a716","a2c88d7fc8bd428698c1f299c1b113b1","6c3180ff91d6462c91e1d675bef7835e"]},"id":"fusInJnQRUy5","executionInfo":{"status":"ok","timestamp":1727452729511,"user_tz":240,"elapsed":7790,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"7f116924-664e-42f0-82c2-8210541e94a3"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Generated Summary:\n","My friends are cool but they eat too many carbs. That's what this is all about. I don't want you to think I'm a bad person. I'm not. I just don't like to be around people who eat too much carbs. This is my way of telling you that.\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b013da48328d4a23b130b0bce523c17f"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","ROUGE scores:\n","{'rouge1': 0.21875, 'rouge2': 0.12903225806451613, 'rougeL': 0.21875, 'rougeLsum': 0.21875}\n"]}]},{"cell_type":"code","source":["# Improve model summary generation\n","def summarize_article_with_tweaks(article):\n","\n","    # Load BART model and tokenizer\n","    model_name= 'facebook/bart-large-cnn'\n","    tokenizer= BartTokenizer.from_pretrained(model_name)\n","    model= BartForConditionalGeneration.from_pretrained(model_name)\n","\n","    # Tokenize and encode the article\n","    inputs= tokenizer.encode(article, return_tensors= 'pt', max_length= 1024, truncation= True)\n","\n","    # Generate summary with tweaked parameters\n","    summary_ids= model.generate(\n","        inputs,\n","        num_beams= 6,               # Increased beams for better quality\n","        max_length= 150,\n","        repetition_penalty= 1.2,    # Add repetition penalty to avoid repeated phrases\n","        early_stopping= True\n","    )\n","\n","    # Decode the summary\n","    summary= tokenizer.decode(summary_ids[0], skip_special_tokens= True, clean_up_tokenization_spaces= True)\n","\n","    return summary\n","\n","# Example usage\n","article= '''\n","My friends are cool but they eat too many carbs.\n","'''\n","\n","# Generate model summary with the new function\n","generated_summary= summarize_article_with_tweaks(article)\n","\n","print('Generated Summary:')\n","print(generated_summary)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PZosMmGMsClA","executionInfo":{"status":"ok","timestamp":1727452737031,"user_tz":240,"elapsed":7526,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"e24ee3fe-3d87-42d4-b6b6-b5374e0b9246"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Generated Summary:\n","My friends are cool but they eat too many carbs. That's what this is all about. I don't want you to think I'm a bad person. I'm not. I just don't like to be around people who eat too much carbs. This is my way of dealing with it.\n"]}]},{"cell_type":"code","source":["# Define reference summary for comparison (human-written summary)\n","reference_summary= 'My friends are nice, but they consume too many carbohydrates.'\n","\n","# Load the ROUGE metric using the evaluate library\n","rouge = evaluate.load('rouge')\n","\n","# Calculate the ROUGE score\n","scores= rouge.compute(predictions=[generated_summary], references=[reference_summary])\n","\n","# Print ROUGE scores\n","print('\\nROUGE scores:')\n","print(scores)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ECSdZeJ-swp3","executionInfo":{"status":"ok","timestamp":1727453607632,"user_tz":240,"elapsed":1781,"user":{"displayName":"Carlos Rodriguez","userId":"04567056169439968244"}},"outputId":"83a1b65d-613c-4448-effa-6f6ffcb281c5"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","ROUGE scores:\n","{'rouge1': 0.21875, 'rouge2': 0.12903225806451613, 'rougeL': 0.21875, 'rougeLsum': 0.21875}\n"]}]}]}