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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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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\")" + ], + 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"id": "mv-cUpvZPlTM", + "outputId": "b999c6e5-200f-4785-baf0-bcee196d7730" + }, + "execution_count": null, + "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", + "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": [] + } + ] +} \ No newline at end of file