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102 KiB
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1 line
102 KiB
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install transformers datasets evaluate"]},{"cell_type":"code","source":["from datasets import load_dataset\n","\n","squad = load_dataset(\"squad\", split=\"train[:5000]\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4g_4i6xTRX5Z","executionInfo":{"status":"ok","timestamp":1712753418042,"user_tz":-330,"elapsed":13509,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"87e83924-0655-44dc-e027-ba76785fa402"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"]}]},{"cell_type":"code","source":["squad = squad.train_test_split(test_size=0.2)"],"metadata":{"id":"nDcLRCflTtWU","executionInfo":{"status":"ok","timestamp":1712753418043,"user_tz":-330,"elapsed":6,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["squad"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X7lk8NQoTMVG","executionInfo":{"status":"ok","timestamp":1712753418043,"user_tz":-330,"elapsed":5,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"f1594b18-4b45-479a-df3d-59c6f2944def"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n"," train: Dataset({\n"," features: ['id', 'title', 'context', 'question', 'answers'],\n"," num_rows: 4000\n"," })\n"," test: Dataset({\n"," features: ['id', 'title', 'context', 'question', 'answers'],\n"," num_rows: 1000\n"," })\n","})"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["from transformers import AutoTokenizer\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"distilbert/distilbert-base-uncased\")"],"metadata":{"id":"TO-Fmvs6TT4o","executionInfo":{"status":"ok","timestamp":1712753436078,"user_tz":-330,"elapsed":18039,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["def preprocess_function(examples):\n"," questions = [q.strip() for q in examples[\"question\"]]\n"," inputs = tokenizer(\n"," questions,\n"," examples[\"context\"],\n"," max_length=384,\n"," truncation=\"only_second\",\n"," return_offsets_mapping=True,\n"," padding=\"max_length\",\n"," )\n","\n","\n"," offset_mapping = inputs.pop(\"offset_mapping\")\n"," answers = examples[\"answers\"]\n"," start_positions = []\n"," end_positions = []\n","\n"," for i, offset in enumerate(offset_mapping):\n"," answer = answers[i]\n"," start_char = answer[\"answer_start\"][0]\n"," end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n"," sequence_ids = inputs.sequence_ids(i)\n","\n"," # Find the start and end of the context\n"," idx = 0\n"," while sequence_ids[idx] != 1:\n"," idx += 1\n"," context_start = idx\n"," while sequence_ids[idx] == 1:\n"," idx += 1\n"," context_end = idx - 1\n","\n"," # If the answer is not fully inside the context, label it (0, 0)\n"," if offset[context_start][0] > end_char or offset[context_end][1] < start_char:\n"," start_positions.append(0)\n"," end_positions.append(0)\n"," else:\n"," # Otherwise it's the start and end token positions\n"," idx = context_start\n"," while idx <= context_end and offset[idx][0] <= start_char:\n"," idx += 1\n"," start_positions.append(idx - 1)\n","\n"," idx = context_end\n"," while idx >= context_start and offset[idx][1] >= end_char:\n"," idx -= 1\n"," end_positions.append(idx + 1)\n","\n"," inputs[\"start_positions\"] = start_positions\n","\n"," inputs[\"end_positions\"] = end_positions\n"," return inputs"],"metadata":{"id":"6Np4AduGTf70","executionInfo":{"status":"ok","timestamp":1712753436079,"user_tz":-330,"elapsed":36,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad[\"train\"].column_names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["295c93032f3343ee826d4a3a86237952","c9a2e468aad946d68a80ba0a5886235f","fe9d9a1aadc8493ea9ad926250b44d24","47424743b755468fa9e69edff319c14e","9237ae488a644ae49d063bf3c61fead2","8aadefaa10b24bc68a381c432a389b13","08239e1bc1dc45bc833f55955279f796","1557b5b5f3a641c3b41df702713480ca","dbe6daeb7da749318f93ac9ad221c117","394add5e84014ff9bb4374643108f947","bc3aaa181dbe461ca20bbe2d56a84ef9","ac82892a40cb4c328e482fd13bb37f40","afba6ba867cc48a4a5bbdb5eb61dc19c","52fe28c43704463e9514ff0b22dd7a66","471867300737479187534e6827fc65a0","8ebf51191da84986af7b9b5ade0b67c6","2bafac2166ca4a04b59dce286fd37443","801389bbc4cb4e64b46b98fa2c94e4ee","aff3b8a50ddb456e94a50e6603811a76","0cebbcc7b0f74ba0a5036dc7d532f74c","7483e5d6f6f94a6eacd75f6307c8221d","32a8e76713284c73aef4400256ba7566"]},"id":"zPKmo0uETkHS","executionInfo":{"status":"ok","timestamp":1712753453628,"user_tz":-330,"elapsed":17582,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"32a40f05-d3db-444b-d5d9-b438ef020a64"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/4000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"295c93032f3343ee826d4a3a86237952"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/1000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac82892a40cb4c328e482fd13bb37f40"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_squad['train']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c9EntbE6CEHg","executionInfo":{"status":"ok","timestamp":1712753453629,"user_tz":-330,"elapsed":23,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"ec72eb64-df3c-4611-eeb6-4149a013c145"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n"," features: ['input_ids', 'attention_mask', 'start_positions', 'end_positions'],\n"," num_rows: 4000\n","})"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["from transformers import DefaultDataCollator\n","\n","data_collator = DefaultDataCollator()"],"metadata":{"id":"xMzdJikpCl5x","executionInfo":{"status":"ok","timestamp":1712753466054,"user_tz":-330,"elapsed":12446,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer\n","\n","model = AutoModelForQuestionAnswering.from_pretrained(\"distilbert/distilbert-base-uncased\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jYn7MTlhC82a","executionInfo":{"status":"ok","timestamp":1712753473579,"user_tz":-330,"elapsed":7531,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"e4b6000d-49e8-42d7-ca50-9143a1d92536"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of DistilBertForQuestionAnswering were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","source":["training_args = TrainingArguments(\n"," output_dir=\"textPara_qa_model\",\n"," evaluation_strategy=\"epoch\",\n"," learning_rate=2e-5,\n"," per_device_train_batch_size=16,\n"," per_device_eval_batch_size=16,\n"," num_train_epochs=1,\n"," weight_decay=0.01,\n"," push_to_hub=True,\n",")\n"],"metadata":{"id":"_cXp0l2MCnmv","executionInfo":{"status":"ok","timestamp":1712753762968,"user_tz":-330,"elapsed":526,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["trainer = Trainer(\n"," model=model,\n"," args=training_args,\n"," train_dataset=tokenized_squad[\"train\"],\n"," eval_dataset=tokenized_squad[\"test\"],\n"," tokenizer=tokenizer,\n"," data_collator=data_collator,\n",")"],"metadata":{"id":"XVeTsrnOCu3d","executionInfo":{"status":"ok","timestamp":1712753766455,"user_tz":-330,"elapsed":525,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","notebook_login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["794015fecc5b411ca8597d6cfa92ba56","c8a51c4fd9af4b00b7e9fb9bb4b3235c","cbd030ce915f46e8b448025c5a9ec78a","2f6e87d7f03443d0b1e188926230c01c","7c593b80c7d941b7bbc2d41e7279ab71","1f95ab46cba841ecb0e9a6a2246c0a25","58e7b132316a42c6aac935e735476e6b","7d57740a4ec64b7d8bce57b6b47bb190","637c2383f081409aaba01d5517e997d3","ad8e4a79aa22463fb77cb797d392a294","c0db0fbab7c34d84be2e90098d90c9b3","0cdefb0751f24864bf243f6fa78f0ec1","1f31f73b2a4340e5b3298bf21a75e773","430f85972ecf49d2bb6c051c69dd959f","07c7c995033b479dba0687bf59d41759","1d118840f50440c58ff76b0d5d8bce14","54a77ec1926e4799a26c1447761d1f0e","e0fba01e114b4debbf737267a48cda89","d4d1ae82bd7a4fbdb1410fa4fcd0b710","92a2a952bbbc40ec9a11b724a046fea9","a5a8eb5c20a64ede9decfe6bd4b21714","3194970aaf4b456092820bdf4dd30118","68e586bbdf964f5e8f87f41a2ae857fc","8a72cbad10e34c60a7088d29822fa4cd","391e532136514391be16b6259592e806","de44b2e9474e457bb142d54454d271b9","b58d1e6a038642cc96c6e42ff6fdbd1e","ccbfeefca2054525aca4dcace4b7d0b6","459d2d4235cb43708af39174c9b5ef51","d78490f5df62431c89643f70ec8b4b80","798a73384276420c9584d2aedbef740e","16a9c769268f4d44a947c3840fa661bd"]},"id":"gl1ETyAmEDrn","executionInfo":{"status":"ok","timestamp":1712765563743,"user_tz":-330,"elapsed":575,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"639aa2ef-4bdd-4840-c847-91391ea0fbf5"},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"794015fecc5b411ca8597d6cfa92ba56"}},"metadata":{}}]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":199},"id":"MnGP2EJXFBjP","executionInfo":{"status":"ok","timestamp":1712762919854,"user_tz":-330,"elapsed":4874754,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"65ed2b2a-1589-485f-bebf-3b50aa3bcbec"},"execution_count":18,"outputs":[{"data":{"text/html":["\n"," <div>\n"," \n"," <progress value='125' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [125/250 1:10:05 < 1:11:13, 0.03 it/s, Epoch 0.50/1]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Epoch</th>\n"," <th>Training Loss</th>\n"," <th>Validation Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [250/250 2:31:27, Epoch 1/1]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Epoch</th>\n"," <th>Training Loss</th>\n"," <th>Validation Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>1</td>\n"," <td>No log</td>\n"," <td>2.981502</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=250, training_loss=3.617652587890625, metrics={'train_runtime': 9142.5363, 'train_samples_per_second': 0.438, 'train_steps_per_second': 0.027, 'total_flos': 391959300096000.0, 'train_loss': 3.617652587890625, 'epoch': 1.0})"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["# Use a pipeline as a high-level helper\n","from transformers import pipeline\n","\n","pipe = pipeline(\"question-answering\", model=\"vishal2002/textPara_qa_model\")"],"metadata":{"id":"s72-AC6JodLi","executionInfo":{"status":"ok","timestamp":1712763068029,"user_tz":-330,"elapsed":2139,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["question = {\"question\": \"who goes to school\", \"context\": \"harsh goes to school at 9 pm sharp\"}\n","answer = pipe(question)['answer']\n","print(answer)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mAHIc7YDomcw","executionInfo":{"status":"ok","timestamp":1712765874052,"user_tz":-330,"elapsed":541,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"572384c0-a0de-4d7e-ea37-791845108849"},"execution_count":56,"outputs":[{"output_type":"stream","name":"stdout","text":["harsh\n"]}]},{"cell_type":"code","source":["# Use a pipeline as a high-level helper\n","from transformers import pipeline\n","\n","pipe = pipeline(\"question-answering\", model=\"deepset/roberta-base-squad2\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":209,"referenced_widgets":["1cc98adcb1c54954badf4226e7f127f5","3ca4986c46ca4abeaa99935171a8507b","9ac55c24746049c0b38bc2a3b3a50c42","39fb89e2e225467b8aed6f84b9c2f17a","0ba11117631a43efaa0e978c9b10d00c","d51c198ad85e41c6bb2ac5a66aefa230","fdb8575886ee4a6983587a82eb3d7be1","d070ada08a794d9d8eb202f08f597ed2","ec71575520724224820e8d0fb50a0d96","d55759d34f1645edba5fcc7db59a1b86","d2ffd026a0d149c2b8adf87271f987a8","671e5ccbd87442f0885ad23de568716b","1b2d9056c99c4f95b2eba2a6d2c9d90a","c983747220c44df09f8ffe8e53f967c8","b1cc99b0a8d244b4ac384a1d01582381","ed6619503325470a821a137457713446","338b13a10f564690a37a00f8ce1ab584","bfec6da9966048bd8b790c82168f9c39","abd95f6978944bbaa83bed403aa3a870","cd400dd5ec1541a4aeea31d53f428665","f097e83a21d14f9a85414fde49bfb59a","b9dd4b91ff7246c8b62ae2750123c858","0f7ba9fe87af4ba7967876cc2e21e8d9","c4d5025889e94d05b868115b20b8e112","7f8e831bfd8a45939e47b336535361be","f73644dd80ae4bbea3e70bfbe6bbb01e","ecd17c8065cf48eaa2ca81d8f9747723","d1c2b4c6058c48a28925f50e8ce0844f","f913efbcbdb1426bb13b9b53464e2ba6","bd3787ec4f2c4c61bc65703da4751ab8","1b1315c2e70448868fa178492221792f","acb3acc8d5944ea39aed1b1a0be719a0","0bd2aa124716411bb0f7459c0f103d2f","9fc921fcb0b74ee59d3132f1c748ba35","18f2e256ffe94a03832750c776a75ccd","96620b43e70f45208eb6f828ba272498","561014153c9e461ab8b12ceca4173866","2199bd2261294092a8cf78b73ce9e472","3bd1734b7b2b4866881bef67ddec813c","38dd982ae6ac4c858bddc2e51a8066a3","244e13edfdda4e0da94cc404a2f855b8","0cde6b4390674604a5ad29eff959f2cf","3b435f68bd294cbdb1b3bd14a1e0e0c3","e347d25a33c2412991465b84be23489e","8c054ce4120d4b30b6512c00291aafc8","2185f238448e440682cb810cc492a3f9","682c998cd32d45b0af85e90931046886","46117fa3e6d64d9e8be92352f41527d8","653538ae75c54f4fbca4d4b187ea93ee","956f53666d1f48dd9fc88b0f5b5f0161","c6902b3ebad249509afc7968376bf919","e22681f2168444d0b769e7200fa37ce8","9d1745721f374de3a9c3babd1f2a1377","a23ab47fd73b4e8799c645bad392baf1","731c455bfba44dd9b7496888b939a3f9","dc453bbf7c5c455d80c30d3358d381d7","8713c9e03b2c49ac810568ad94d9baed","72c3d1b53ff4454684fc952e11d75a23","aa57b5153cad4298b947b7d76a033d5d","3fc04f570d904e30905469e13d512b5d","7a389ab351e94f66b9a40d21e0ec6b91","2a343fa57d0747ac8f31ce37b6b2012c","ee2ef7dff028435a9f1b004b006bd231","26d65598c9c2418bb0c5fc4b95f5d389","d405bcb8aab542c8b463ee701ecc5d1a","ccd28ab37c9a4a3382e92db09df69ad7"]},"id":"N4dBYkSuy5n2","executionInfo":{"status":"ok","timestamp":1712765784382,"user_tz":-330,"elapsed":10541,"user":{"displayName":"Vishal Tiwari","userId":"01086604444438900698"}},"outputId":"cd6ee093-6f02-4d28-fe2d-7193446224d7"},"execution_count":53,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/571 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1cc98adcb1c54954badf4226e7f127f5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/496M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"671e5ccbd87442f0885ad23de568716b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0%| | 0.00/79.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0f7ba9fe87af4ba7967876cc2e21e8d9"}},"metadata":{}},{"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":"9fc921fcb0b74ee59d3132f1c748ba35"}},"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":"8c054ce4120d4b30b6512c00291aafc8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json: 0%| | 0.00/772 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dc453bbf7c5c455d80c30d3358d381d7"}},"metadata":{}}]},{"cell_type":"code","source":["!pip install transformers[torch]===4.39.3"],"metadata":{"id":"WfaWpCOHDEJ6"},"execution_count":null,"outputs":[]}]} |