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markdown-it-py>=2.2.0->rich<14,>=10.14.0->streamlit->simpletransformers) (0.1.2)\n", "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.5.1)\n", "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard->simpletransformers) (3.2.2)\n", "Installing collected packages: typing-extensions\n", " Attempting uninstall: typing-extensions\n", " Found existing installation: typing-extensions 3.10.0.0\n", " Uninstalling typing-extensions-3.10.0.0:\n", " Successfully uninstalled typing-extensions-3.10.0.0\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lida 0.0.10 requires kaleido, which is not installed.\n", "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed typing-extensions-4.9.0\n", "Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.16.2)\n", "Requirement already satisfied: Click!=8.0.0,>=7.1 in /usr/local/lib/python3.10/dist-packages (from wandb) (8.1.7)\n", "Requirement already satisfied: GitPython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.1.41)\n", "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.31.0)\n", "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (5.9.5)\n", "Requirement already satisfied: sentry-sdk>=1.0.0 in 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GitPython!=3.1.29,>=1.0.0->wandb) (4.0.11)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.6)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2023.11.17)\n", "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb) (5.0.1)\n" ] } ] }, { "cell_type": "code", "source": [ "import json\n", "import logging\n", "from simpletransformers.question_answering import QuestionAnsweringModel, QuestionAnsweringArgs" ], "metadata": { "id": "hPvBCBHqglXO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model_type = \"bert\"\n", "model_name = \"bert-base-cased\"" ], "metadata": { "id": "zLTEjM65_4Gh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "with open(r\"/content/test.json\", \"r\") as read_file:\n", " test = json.load(read_file)" ], "metadata": { "id": "XpQyL8hJXeTi" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "with open(\"/content/konbert-export-a07a2fb8c3174.json\", \"r\") as json_file:\n", " train_data = json.load(json_file)\n", "\n", "\n", "adapted_data = []\n", "for paragraph in train_data:\n", " qas_list = []\n", " if \"answers\" in paragraph and \"text\" in paragraph[\"answers\"] and \"answer_start\" in paragraph[\"answers\"]:\n", " for i in range(len(paragraph[\"answers\"][\"text\"])):\n", " answer_text = paragraph[\"answers\"][\"text\"][i].strip()\n", " if answer_text:\n", " qa_dict = {\n", " \"id\": f\"{paragraph['id']}_{i}\",\n", " \"question\": paragraph.get(\"question\", \"\"),\n", " \"answers\": [{\"text\": answer_text, \"answer_start\": paragraph[\"answers\"][\"answer_start\"][i]}]\n", " }\n", " qas_list.append(qa_dict)\n", "\n", " if qas_list:\n", " adapted_data.append({\n", " \"context\": paragraph.get(\"context\", \"\"),\n", " \"qas\": qas_list\n", " })" ], "metadata": { "id": "QmhRsW8Da9d1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "model_args = QuestionAnsweringArgs()\n", "model_args.train_batch_size = 16\n", "model_args.evaluate_during_training = True\n", "model_args.n_best_size = 3\n", "model_args.num_train_epochs = 5\n", "\n", "train_args = {\n", " \"reprocess_input_data\": True,\n", " \"overwrite_output_dir\": True,\n", " \"use_cached_eval_features\": True,\n", " \"output_dir\": \"/content/models/bert-base-uncased\", # Specify the directory to save the model\n", " \"best_model_dir\": \"/content/models/bert-base-uncased/best_model\",\n", " \"evaluate_during_training\": True,\n", " \"max_seq_length\": 128,\n", " \"num_train_epochs\": 5,\n", " \"evaluate_during_training_steps\": 1000,\n", " \"wandb_project\": \"Question Answer Application\",\n", " \"wandb_kwargs\": {\"name\": model_name},\n", " \"save_model_every_epoch\": False,\n", " \"save_eval_checkpoints\": False,\n", " \"n_best_size\": 3,\n", " \"train_batch_size\": 128,\n", " \"eval_batch_size\": 64,\n", "}\n", "\n", "\n", "model = QuestionAnsweringModel('bert', 'bert-base-uncased', args={'overwrite_output_dir': True, 'num_train_epochs': 20})\n", "model.train_model(adapted_data, num_train_epochs=20)\n", "model.save_model(f\"outputs/{model_type}/final_model\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 883, "referenced_widgets": [ "f263a0c4c3b140c3882c9e2fd2de9f42", "f3defb145078432ba0bc5d381e605f58", "a3b36ec35f51482ebb429ac048a9554c", "7d0839d2d0c148e1bbcbd4051fce7434", 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that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n", "Some weights of BertForQuestionAnswering were not initialized from the model checkpoint at bert-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", "convert squad examples to features: 100%|██████████| 38/38 [00:00<00:00, 164.26it/s]\n", "add example index and unique id: 100%|██████████| 38/38 [00:00<00:00, 254200.24it/s]\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Epoch: 0%| | 0/20 [00:00" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "6ueXBMvcie79" }, "execution_count": null, "outputs": [] } ] }