diff --git "a/roberta_ja_qa.ipynb" "b/roberta_ja_qa.ipynb"
new file mode 100644--- /dev/null
+++ "b/roberta_ja_qa.ipynb"
@@ -0,0 +1,5436 @@
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+ "cells": [
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+ "source": [
+ "## Import dependencies"
+ ],
+ "metadata": {
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+ }
+ },
+ {
+ "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": [
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+ "name": "stdout",
+ "text": [
+ "Collecting transformers[sentencepiece]\n",
+ " Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n",
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+ "Collecting huggingface-hub<1.0,>=0.1.0\n",
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+ "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
+ " Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
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+ "Collecting sacremoses\n",
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+ "\u001b[K |████████████████████████████████| 895 kB 23.2 MB/s \n",
+ "\u001b[?25hCollecting pyyaml>=5.1\n",
+ " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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+ "Collecting sentencepiece!=0.1.92,>=0.1.91\n",
+ " Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
+ "\u001b[K |████████████████████████████████| 1.2 MB 44.6 MB/s \n",
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+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers[sentencepiece]) (3.10.0.2)\n",
+ "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers[sentencepiece]) (3.0.7)\n",
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+ "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf->transformers[sentencepiece]) (1.15.0)\n",
+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers[sentencepiece]) (1.24.3)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers[sentencepiece]) (2.10)\n",
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+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers[sentencepiece]) (3.0.4)\n",
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+ "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers[sentencepiece]) (7.1.2)\n",
+ "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece\n",
+ " Attempting uninstall: pyyaml\n",
+ " Found existing installation: PyYAML 3.13\n",
+ " Uninstalling PyYAML-3.13:\n",
+ " Successfully uninstalled PyYAML-3.13\n",
+ "Successfully installed huggingface-hub-0.5.1 pyyaml-6.0 sacremoses-0.0.49 sentencepiece-0.1.96 tokenizers-0.11.6 transformers-4.18.0\n",
+ "Collecting datasets\n",
+ " Downloading datasets-2.0.0-py3-none-any.whl (325 kB)\n",
+ "\u001b[K |████████████████████████████████| 325 kB 12.2 MB/s \n",
+ "\u001b[?25hCollecting aiohttp\n",
+ " Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
+ "\u001b[K |████████████████████████████████| 1.1 MB 46.2 MB/s \n",
+ "\u001b[?25hCollecting xxhash\n",
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+ "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.3)\n",
+ "Collecting fsspec[http]>=2021.05.0\n",
+ " Downloading fsspec-2022.3.0-py3-none-any.whl (136 kB)\n",
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+ "Collecting responses<0.19\n",
+ " Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
+ "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
+ " Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
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+ "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
+ " Downloading yarl-1.7.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (271 kB)\n",
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+ "\u001b[?25hCollecting frozenlist>=1.1.1\n",
+ " Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
+ "\u001b[K |████████████████████████████████| 144 kB 55.3 MB/s \n",
+ "\u001b[?25hCollecting asynctest==0.13.0\n",
+ " Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
+ "Collecting multidict<7.0,>=4.5\n",
+ " Downloading multidict-6.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (94 kB)\n",
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+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (21.4.0)\n",
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+ "Collecting aiosignal>=1.1.2\n",
+ " Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n",
+ "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.7.0)\n",
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+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
+ "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, responses, datasets\n",
+ " Attempting uninstall: urllib3\n",
+ " Found existing installation: urllib3 1.24.3\n",
+ " Uninstalling urllib3-1.24.3:\n",
+ " Successfully uninstalled urllib3-1.24.3\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",
+ "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
+ "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.0.0 frozenlist-1.3.0 fsspec-2022.3.0 multidict-6.0.2 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n",
+ "Collecting fugashi\n",
+ " Downloading fugashi-1.1.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (568 kB)\n",
+ "\u001b[K |████████████████████████████████| 568 kB 12.3 MB/s \n",
+ "\u001b[?25hCollecting ipadic\n",
+ " Downloading ipadic-1.0.0.tar.gz (13.4 MB)\n",
+ "\u001b[K |████████████████████████████████| 13.4 MB 23.5 MB/s \n",
+ "\u001b[?25hBuilding wheels for collected packages: ipadic\n",
+ " Building wheel for ipadic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for ipadic: filename=ipadic-1.0.0-py3-none-any.whl size=13556723 sha256=3bd68055e57f5d8b1bcf273bc0c534f538fcc6c04f52081828204b1aa57ce0f0\n",
+ " Stored in directory: /root/.cache/pip/wheels/33/8b/99/cf0d27191876637cd3639a560f93aa982d7855ce826c94348b\n",
+ "Successfully built ipadic\n",
+ "Installing collected packages: ipadic, fugashi\n",
+ "Successfully installed fugashi-1.1.2 ipadic-1.0.0\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Load the data from the Hub"
+ ],
+ "metadata": {
+ "id": "B6GXPi35Qd8e"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 84,
+ "referenced_widgets": [
+ "91a9857567b9433498726162bf20dc78",
+ "9aff00fd0a8b4d95ae9a89580d9ba3a3",
+ "635d5c04010046a39d541ef5e2e580dc",
+ "6d0a44f878394e55a8f41ab17e9a9e55",
+ "3e233a25c0c24771b39b227fc19c3207",
+ "9d3ab443de844025925b00d8d535af30",
+ "c846bb7ae8e642fa843e071d250d765c",
+ "d3b439187872420683108ddf1d8b8e44",
+ "2826068e02f74455820b53c1254a3688",
+ "eca3cc8d8ea240cf8e6e21026502b158",
+ "29c0313ce3e340a0b524e7af2698152f"
+ ]
+ },
+ "id": "iPl56sUT9-Mr",
+ "outputId": "0445f343-09d4-40ec-b4d7-c51874083bc6"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Using custom data configuration default\n",
+ "Reusing dataset ja_qu_ad (/root/.cache/huggingface/datasets/SkelterLabsInc___ja_qu_ad/default/0.1.0/5847b2e2ab5e02de284395bb15f87f13eae8f6f6ff1f01e4ee9c5c0dcf8ef8eb)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/2 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "91a9857567b9433498726162bf20dc78"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "jaquad_data = load_dataset('SkelterLabsInc/JaQuAD')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModelForQuestionAnswering, AutoTokenizer\n",
+ "\n",
+ "model = AutoModelForQuestionAnswering.from_pretrained(\"rinna/japanese-roberta-base\")\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\"rinna/japanese-roberta-base\", use_fast=True)\n",
+ "tokenizer.do_lower_case = True "
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vK4EFgtw_vCH",
+ "outputId": "4e9c820b-0380-4ca6-c502-b506e6f544dc"
+ },
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at rinna/japanese-roberta-base were not used when initializing RobertaForQuestionAnswering: ['lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.bias', 'lm_head.bias', 'lm_head.dense.weight']\n",
+ "- This IS expected if you are initializing RobertaForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing RobertaForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of RobertaForQuestionAnswering were not initialized from the model checkpoint at rinna/japanese-roberta-base 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": [
+ "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",
+ " 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",
+ " inputs[\"end_positions\"] = end_positions\n",
+ " return inputs"
+ ],
+ "metadata": {
+ "id": "gNvBc2kgA0Sw"
+ },
+ "execution_count": 18,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenized_squad = jaquad_data.map(preprocess_function, batched=True, remove_columns=jaquad_data[\"train\"].column_names)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 86,
+ "referenced_widgets": [
+ "0f9d3bef8dd9410daf45f9f58f630443",
+ "5410c77619184c42b53f57dd049484e6",
+ "904faf18eb03468d8cc2afa283a47cdf",
+ "f94304461047462696bf261f839f11ac",
+ "c677ae94ee1148caa14719ba096705e8",
+ "8ee6e5b218284733bd524c1d9a748b1a",
+ "98170a0fbe6a4347b6efa248125b62a3",
+ "85e91ec6886e4024b819ac4f95a45171",
+ "108f347c23954d0fb1db6e335cd0634c",
+ "f2cb574cd3bb4d6d8278d744b4a078e3",
+ "f9f1b999e5114514961453dcba2b7396"
+ ]
+ },
+ "id": "fg7EEKy7P3Cc",
+ "outputId": "821cbff2-0a0e-4ec5-b563-04ac54069985"
+ },
+ "execution_count": 44,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " 0%| | 0/32 [00:00, ?ba/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0f9d3bef8dd9410daf45f9f58f630443"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Loading cached processed dataset at /root/.cache/huggingface/datasets/SkelterLabsInc___ja_qu_ad/default/0.1.0/5847b2e2ab5e02de284395bb15f87f13eae8f6f6ff1f01e4ee9c5c0dcf8ef8eb/cache-235bec00d2678b60.arrow\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import TrainingArguments, Trainer, DefaultDataCollator\n",
+ "\n",
+ "data_collator = DefaultDataCollator()\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"./results\",\n",
+ " evaluation_strategy=\"steps\",\n",
+ " learning_rate=2e-5,\n",
+ " per_device_train_batch_size=16,\n",
+ " per_device_eval_batch_size=16,\n",
+ " num_train_epochs=3,\n",
+ " weight_decay=0.01,\n",
+ " eval_steps=30\n",
+ ")\n",
+ "\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=tokenized_squad[\"train\"].select(range(6000)),\n",
+ " eval_dataset=tokenized_squad[\"validation\"].select(range(200)),\n",
+ " tokenizer=tokenizer,\n",
+ " data_collator=data_collator,\n",
+ ")\n",
+ "\n",
+ "trainer.train()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 698
+ },
+ "id": "Plt2oF9rK21b",
+ "outputId": "c8c270e7-2d80-47cf-9078-394225d4b682"
+ },
+ "execution_count": 154,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "PyTorch: setting up devices\n",
+ "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
+ "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+ " FutureWarning,\n",
+ "***** Running training *****\n",
+ " Num examples = 6000\n",
+ " Num Epochs = 3\n",
+ " Instantaneous batch size per device = 16\n",
+ " Total train batch size (w. parallel, distributed & accumulation) = 16\n",
+ " Gradient Accumulation steps = 1\n",
+ " Total optimization steps = 1125\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
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+ " [ 34/1125 01:41 < 57:46, 0.31 it/s, Epoch 0.09/3]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 30 | \n",
+ " No log | \n",
+ " 1.008251 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "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",
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+ ]
+ },
+ "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": [
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+ ]
+ },
+ "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, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "3c882df485214d68826d0ca5a9661ed1"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Download file spiece.model: 2%|2 | 16.0k/787k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "377541cb9fbc44aeac097b0b77916fb9"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Clean file spiece.model: 0%| | 1.00k/787k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "020cf7e33e5940fa9ac914f64119e9c8"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Clean file pytorch_model.bin: 0%| | 1.00k/420M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1a317865e01340cdb031611ec5f989e9"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Configuration saved in /tmp/tmps1qaw_re/config.json\n",
+ "Model weights saved in /tmp/tmps1qaw_re/pytorch_model.bin\n",
+ "/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, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "75e62510cfba412f99244fc4579e2685"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Download file spiece.model: 2%|1 | 15.6k/787k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "19e5eb0e01ff4e2db7d4d5b797ec62d9"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Clean file spiece.model: 0%| | 1.00k/787k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "0a48be2a5c62464fbb6d3f5dffbbb7eb"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Clean file pytorch_model.bin: 0%| | 1.00k/420M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "bcab9f153ccc4f888cef684cebd613e7"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "tokenizer config file saved in /tmp/tmp79y_t4pf/tokenizer_config.json\n",
+ "Special tokens file saved in /tmp/tmp79y_t4pf/special_tokens_map.json\n",
+ "Copy vocab file to /tmp/tmp79y_t4pf/spiece.model\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Error? To investigate"
+ ],
+ "metadata": {
+ "id": "sOxEaWS87y-I"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "random_data = jaquad_data[\"validation\"][random.randint(0, len(jaquad_data[\"validation\"]))]\n",
+ "from_data_to_answer(random_data)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "pvcAHBDL0lr7",
+ "outputId": "d01c6eea-d38e-4b10-9004-8e5f5f6ca7ba"
+ },
+ "execution_count": 189,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Question: 3月12日における国公立大学後期日程の受験者は前年度と比べ、どれだけ減りましたか?\n",
+ "Context: 宮城県教育委員会は、3月14日から18日までを休校とし、15日に予定していた高等学校一般入学試験の合格者発表を22日以降に延期することを決めた。\n",
+ "\n",
+ "地震発生直後の2011年3月12日、13日には国公立大学の入学試験後期日程が予定されていたが、12日は被災地にある32校の大学(私立大学含む)で試験が中止されることになった。国立36大学・公立15大学・私立7大学が12日の試験の開始時間繰り下げや、地震の影響を受けた受験生の個別対応を行い、追試の対応をした大学もある。文部科学省は、3月12日における国公立大学後期日程の受験者は6万5667人(昨年から2万604人減少)であり、この減少は地震の影響によるものであるとしている。東北地方で同日に試験が実施された国公立大学は、全12校のうち秋田大学・秋田県立大学のみであった。翌13日においても6校で試験を中止し、5校で繰り下げ実施または個別対応を行った。\n",
+ "\n",
+ "また、家屋の損壊した状況に応じて入学金や授業料の免除を行う大学や、震災や計画停電の影響を考慮して授業開始を5月以降とする大学も増えている。\n",
+ "Predicted answer: 6万5667\n",
+ "Correct answer: 2万604人\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline\n",
+ "\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",
+ "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",
+ "mode_name = 'ybelkada/japanese-roberta-question-answering'\n",
+ "model = AutoModelForQuestionAnswering.from_pretrained(mode_name)\n",
+ "tokenizer = AutoTokenizer.from_pretrained(mode_name)\n",
+ "QA = pipeline('question-answering', model=model, tokenizer=tokenizer)\n",
+ "QA_input = {'question': question,'context':context}\n",
+ "QA(QA_input)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kxMPJqyp3Wd8",
+ "outputId": "13383aaf-e820-42a6-ee3d-85f67fb47567"
+ },
+ "execution_count": 213,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "loading configuration file https://huggingface.co/ybelkada/japanese-roberta-question-answering/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/8c665325243570331908cdaac83cde055a8274f79488d012f82cbebdfaf3e49c.2e7e8f53fe123481242b617e35509d18b40bbf52597e6ea8f3ac3dd3f748a98d\n",
+ "Model config RobertaConfig {\n",
+ " \"_name_or_path\": \"ybelkada/japanese-roberta-question-answering\",\n",
+ " \"architectures\": [\n",
+ " \"RobertaForQuestionAnswering\"\n",
+ " ],\n",
+ " \"attention_probs_dropout_prob\": 0.1,\n",
+ " \"bos_token_id\": 1,\n",
+ " \"classifier_dropout\": null,\n",
+ " \"eos_token_id\": 2,\n",
+ " \"gradient_checkpointing\": false,\n",
+ " \"hidden_act\": \"gelu\",\n",
+ " \"hidden_dropout_prob\": 0.1,\n",
+ " \"hidden_size\": 768,\n",
+ " \"initializer_range\": 0.02,\n",
+ " \"intermediate_size\": 3072,\n",
+ " \"layer_norm_eps\": 1e-05,\n",
+ " \"max_position_embeddings\": 514,\n",
+ " \"model_type\": \"roberta\",\n",
+ " \"num_attention_heads\": 12,\n",
+ " \"num_hidden_layers\": 12,\n",
+ " \"pad_token_id\": 3,\n",
+ " \"position_embedding_type\": \"absolute\",\n",
+ " \"torch_dtype\": \"float32\",\n",
+ " \"transformers_version\": \"4.18.0\",\n",
+ " \"type_vocab_size\": 2,\n",
+ " \"use_cache\": true,\n",
+ " \"vocab_size\": 32000\n",
+ "}\n",
+ "\n",
+ "loading weights file https://huggingface.co/ybelkada/japanese-roberta-question-answering/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/4eb11f1603c2ffb076dea19e5c209bf5c2adb71958bf93ad25b7855eac97137c.2e4e2cfa1b372d124466d1f3eeab8cc646e27a1c4ab63cc5cf1fd066a86070c4\n",
+ "All model checkpoint weights were used when initializing RobertaForQuestionAnswering.\n",
+ "\n",
+ "All the weights of RobertaForQuestionAnswering were initialized from the model checkpoint at ybelkada/japanese-roberta-question-answering.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForQuestionAnswering for predictions without further training.\n",
+ "loading file https://huggingface.co/ybelkada/japanese-roberta-question-answering/resolve/main/spiece.model from cache at /root/.cache/huggingface/transformers/cd3ba7a0b76953986b447e834570b7b09f6f4ed8b8169d72214c5f8efc2ebdde.c0b735c65f40dff8596b5f699043bb29048036242443fea32b79a9dd8510ea96\n",
+ "loading file https://huggingface.co/ybelkada/japanese-roberta-question-answering/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/79f5d58b653969809fd19e32e9effe06a99070d9949ccc5f94e246ef4d7b0fde.e90e134f293eed9834c202303b18d6fd4358457943692fe0732d182e18b401cb\n",
+ "loading file https://huggingface.co/ybelkada/japanese-roberta-question-answering/resolve/main/added_tokens.json from cache at None\n",
+ "loading file https://huggingface.co/ybelkada/japanese-roberta-question-answering/resolve/main/special_tokens_map.json from cache at /root/.cache/huggingface/transformers/927528289736b3a849d1b03c2fb5dd25aba3c14b712204bc0a6e295fb8daaae1.9049458ebcd1cf666b7b0a046aa394597f12e611077571cfc86e0938f8675d82\n",
+ "loading file https://huggingface.co/ybelkada/japanese-roberta-question-answering/resolve/main/tokenizer_config.json from cache at /root/.cache/huggingface/transformers/75b15ca01bd99cd23de18180d1c2a8f568718b53d00afbf6ff4b7ddbc7d588e4.72ad3abd9856282e94c09912a69a0ad1285d6d917c661f830c289e130b19b568\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'answer': '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",
+ " 'end': 207,\n",
+ " 'score': 0.38383907079696655,\n",
+ " 'start': 0}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 213
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from_data_to_answer(model, tokenizer, {\"context\":context, \"question\":question, \"answers\":{\"text\":['']}})"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fXu2rcIW5uS0",
+ "outputId": "ce592129-ea9a-48b5-ea05-56ed7b35ee0a"
+ },
+ "execution_count": 214,
+ "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: \n"
+ ]
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file