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"4290514bf5854ded8c6c6266b8c5b073": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "## Import dependencies" ], "metadata": { "id": "26MGed09QaaC" } }, { "cell_type": "code", "source": [ "!pip install transformers[sentencepiece]\n", "!pip install datasets\n", "!pip install fugashi ipadic\n", "!pip install unidic_lite\n", "!apt-get install git-lfs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9CmJYVDz_dyY", "outputId": "57925343-56bf-4f9f-8936-4b3a78e0a73a" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ 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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "***** Running Evaluation *****\n", " Num examples = 200\n", " Batch size = 16\n" ] }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m )\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1420\u001b[0m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1421\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1422\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1424\u001b[0m if (\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2027\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepspeed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2028\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2029\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2031\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 306\u001b[0m inputs=inputs)\n\u001b[0;32m--> 307\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 154\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 155\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "markdown", "source": [ "## Evaluation" ], "metadata": { "id": "qE0gi-Fr75lw" } }, { "cell_type": "code", "source": [ "import torch\n", "import random\n", "\n", "def from_data_to_answer(model, tokenizer, random_data):\n", " model.eval()\n", " question, context = random_data['question'], random_data['context']\n", " input_ids = tokenizer.encode(question, context)\n", " tokens = tokenizer.convert_ids_to_tokens(input_ids)\n", "\n", " output = model(torch.tensor([input_ids]))\n", " argmax_start = torch.argmax(output['start_logits'])\n", " argmax_end = torch.argmax(output['end_logits'])\n", " print(\"Question: \", question)\n", " print(\"Context:\", context)\n", " print(\"Predicted answer: \", \"\".join(tokens[argmax_start:argmax_end]))\n", " print(argmax_start, argmax_end)\n", " print(\"Correct answer: \", \"\".join(random_data['answers']['text']))\n", "\n", "random_data = jaquad_data[\"validation\"][random.randint(0, len(jaquad_data[\"validation\"]))]\n", "from_data_to_answer(model, tokenizer, random_data)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0o17T6bAV3Bm", "outputId": "24a12352-1fc8-418b-e8c3-b5ef963f7e57" }, "execution_count": 212, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Question: この文書で紹介している二つの説の共通点は、どんな人々によるギリシャ本土侵入が問題だったと指摘していることなの?\n", "Context: ドーリア人の侵入による説には紀元前13世紀末から始まる『海の民』による移動に伴い、ドーリア人がバルカン半島を南下してギリシャに至ってギリシャ本土南部、ペロポネソス半島、クレタ、小アジア南西部に定住したことによりミケーネ文化が崩壊、ミケーネ人がアテナイ、小アジアの中部へ移住したとしている。また、別の説ではギリシャ本土はドーリア人の侵入によるもので、小アジア西部ではフリュギア人と『海の民』の侵入があったとしている。\n", "Predicted answer: ドーリア\n", "tensor(26) tensor(28)\n", "Correct answer: ドーリア人\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Push to Hub" ], "metadata": { "id": "VH4t1iDSXZx5" } }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 211, "referenced_widgets": [ "5d8ac64e4b52407aa501e7697e92dbe1", "4837b2ca9ebe4ed5bd42b760055cb131", "96d6b5ac7413458a834012e73a0902c7", "4b7a039946c74fb6ae1bef0a03ed6eaf", "0037f67d45c3498dbef442a9ff6d9842", "4575441584a8458d9f91d40ab1673257", "b69389753f40443da6dc6116900c2738", "0a1da96c58034a0bb91497774838f9c0", "a997ae67630a4a26a346766612e2f624", "45d6967f26844d348e150a7c455ed0b4", "d7f86407359143568c1a571ed8df2921", "51aa239536604929a3c88e2d256c7e86", "fff2d7363c8b4d86b20eb9f550610e2e", "664b9203a1b24598b90205aa1b067ce0", "a7f179237c434f7ca7de206354a074dd", "d0df843b40d448f2a3c45294678e7df6", "30c6fc17255548cda7e464e7578b3189", "481ee1f89eab407db061b764ecfef388", "fd3c948a009d4c6486d9092be4385124", "fe045100bd154e8da5ef67e650a4a67c", "b6382dd883f649358fea49b7014cb3fd", "099082267fac466989bfa3d26b3d7e20", "8bb476bde52147238fba89aa3eb6ae25", "652d38827118455faa46dd1eb8908e5a", "7315a8c9fa54461a88e084ae0b9af928", "c01169f067f74c20ad59eef4d591dfde", "f177d94c9d314cc689b4bf9ed5194818", "87e926f454e542e38e1129ecab2a1964", "a19448a0b12d45dfa4c9f71be5bf6d11", "4495e70f446c4d47b5664aa2e9a9c3b6", "a226b17e12aa42a09a0aa6b8c5f7c256", "4382a2e060964742906d01aaab5c5d39" ] }, "id": "Txb3rXAQXY6l", "outputId": "d2114c2f-fde2-41e4-cba4-3dc019d93009" }, "execution_count": 82, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:510: FutureWarning: HfApi.login: This method is deprecated in favor of `set_access_token` and will be removed in v0.7.\n", " FutureWarning,\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n", "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", "\n", "git config --global credential.helper store\u001b[0m\n" ] } ] }, { "cell_type": "code", "source": [ "model.save_pretrained(\"japanese-roberta-question-answering\")\n", "tokenizer.save_pretrained(\"japanese-roberta-question-answering\")\n", "model.push_to_hub(\"japanese-roberta-question-answering\", use_temp_dir=True)\n", "tokenizer.push_to_hub(\"japanese-roberta-question-answering\", use_temp_dir=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 553, "referenced_widgets": [ "3c882df485214d68826d0ca5a9661ed1", "134b4c8ccc094712b5f8a06959c5b077", "95bd86102fc744feaeed8bb3c1590d92", "3ff19b970c914f2d99507097f4ddaab6", "7bb29998516b4e2fb9c75b5ccb009824", "d5222962aea34ce2aee13df72f93f37b", "dd98c9e5a7cc4ec4bf83e0a91e5855ad", "e7a56c13d4a441e281d136cc0902d031", "398123cc7f5a469f9da31508d3a88480", "e4281e8342f14755a3041d85af8bfb3f", "47bb43d2a39b4c87938574c4bd0e16ec", "377541cb9fbc44aeac097b0b77916fb9", "2df30492a3b7460eafe4edbedb14e750", "8ef3c5d5952f4247b9e1a936b97c0c13", "98fe336e352d412a844162285077e3ab", "6db08c4c9a7944aba3e3b2d5cb248a44", "23ba1ddd2c2b403fbf749163f35fa63e", "fac5114a56a34eea8178075f74e15f69", "77a1dca90dc14208a5013955b126e914", "69df22f2ab644452ac686e2d3fa9ea8d", "e362643dcc6f478f862932b0701764c0", "51f6356591bc49e39715ad41f38e9ee5", "020cf7e33e5940fa9ac914f64119e9c8", "3f868225ea25482f9e6c1d22891b367e", "b7866a94672d4ea3aaf9ee24d8ee79ee", "51f6d04c437644689550aa7e4b4853dc", "09482314f26d452389de4c8a9bfe9f13", "06a0f2c6474b462d9531d240611eab49", "e338057a087045f2b295b30f7ca5a13a", "03f945f5913f409b9a6664c0156818fb", "9e1e95fba8cf456e82479208ce2bee61", "ecb8096e7cc840c683b3d3540485c18b", "0ff9b141082a4da2990cdf51c02ed843", "1a317865e01340cdb031611ec5f989e9", "8aeac591a3384e22a36a412dc806429c", "7c5be4ca7a2545639c7484c2b62018c2", "d55392d8e790493696e7ecc13469b21d", "328466abc7514d88beabb9f9e6929871", "ecab38ab5e3a47809f628c8711d98714", "e6873b2e12ce4b4e9cdd12944915933b", "f24efc258c2f40819e3e1b1517e30524", "e4dfface500c422c9c7d537d0bdcdee8", "4c2f800679f7482cbcd803ffacf2fcf5", "288142475faf4c43b73d0e89cfa32afc", "75e62510cfba412f99244fc4579e2685", "0baccf8335e94578a57cad8dda33f4ad", "5874a75254574aad803523717dcbad06", "962ce7d3d5b546199d00ccb6bb2c7b1b", "d9ed611e7bce4c63b6e40408a9132743", "7b1f83938c0145a8a6c2531de81c62c5", "0c9613cf5b1e496d97d61c21becab67b", "a5ab10e37716476097a844a830ff1fc6", "c9fd45aa751642f7ac597252e0c06d07", "4c081cbaff474044aa963851e34edba4", "068eeaaf96754720bb57f73595dadcbd", "19e5eb0e01ff4e2db7d4d5b797ec62d9", "d3537921dd674616856321f37e76a570", "a3a734372d0f4b66bd30f96125488c42", "7b11b2ec0d6744f883cbe794aecf9d01", "556b81cf2891452c813e63f12560303e", "01adc611a3ab4b2d834d3d7a09de3c03", "0ab188fa42934012bf083dcf40d4cb6f", "8e6ed6bfb92c4fed9d21798cc148bcc6", "c7cfa78f834d4c2b89fc5946468951ab", "7fba05cfcc0d4d659926c849a43a580d", "8dc7fbb9194a4c1b9f417ce58c4ab09f", "0a48be2a5c62464fbb6d3f5dffbbb7eb", "7e728c2bc6c04aa2a09cfbeef548c092", "3cab0fee06cc477faf22642b651c1417", "2a8d45c4597d4162b312a2123e58e769", "aea6307da15e4f36b581a46106b302c4", "76adc725568141ddbf6605e3acf66525", "f30c52ddd1794f0ea8980675827fb4f2", "0c92456fc5c54e0ca9eea5e60261e8ba", "305b8832ee6b4785b62fd3e2a53971e2", "819d850c84734b0fa938d46520da2c0b", "b4d70b9c8f1847ff8a0358e4d9d0dc88", "bcab9f153ccc4f888cef684cebd613e7", "c9c061fcedce43f88725a79d9d6606c3", "d4c02905428f49f68467d0e6111dc7ff", "95b9d87e08974c69879e1db778801b1f", "a5ca77c4d03a41ecb3321349ccf40dce", "b8756de794fc42a7b794a22cd09f3bfe", "eb5ce275f2834f63917072c68e15a252", "9407367c584745cca7f054b4c6aa3f36", "3a447ad2e77144829ce370d575720e08", "6449823bce01400eaf89473f05d6153f", "4290514bf5854ded8c6c6266b8c5b073" ] }, "id": "LCkVu8knX2su", "outputId": "64a98849-9d8d-4c2c-a6f3-040fd34c0ee8" }, "execution_count": 145, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py:43: FutureWarning: Pass token='japanese-roberta-question-answering' as keyword args. From version 0.7 passing these as positional arguments will result in an error\n", " FutureWarning,\n", "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:599: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n", " FutureWarning,\n", "Cloning https://huggingface.co/ybelkada/japanese-roberta-question-answering into local empty directory.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Download file pytorch_model.bin: 0%| | 16.0k/420M [00:00