└── Pytorch_Tutorial.ipynb
/Pytorch_Tutorial.ipynb:
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2065 | "_model_module_version": "1.5.0",
2066 | "_model_name": "DescriptionStyleModel",
2067 | "_view_count": null,
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2069 | "_view_module_version": "1.2.0",
2070 | "_view_name": "StyleView",
2071 | "description_width": ""
2072 | }
2073 | }
2074 | }
2075 | }
2076 | },
2077 | "cells": [
2078 | {
2079 | "cell_type": "markdown",
2080 | "metadata": {
2081 | "id": "view-in-github",
2082 | "colab_type": "text"
2083 | },
2084 | "source": [
2085 | "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
2086 | ]
2087 | },
2088 | {
2089 | "cell_type": "markdown",
2090 | "source": [
2091 | "# Pytorch Tutorial"
2092 | ],
2093 | "metadata": {
2094 | "id": "G6NJgNUP8LLV"
2095 | }
2096 | },
2097 | {
2098 | "cell_type": "markdown",
2099 | "source": [
2100 | "このチュートリアルは以下の2つの内容を含みます.\n",
2101 | "\n",
2102 | "\n",
2103 | "1. BERTを使った含意分類モデルのfine tuning\n",
2104 | "2. BERT2BERTを用いた含意文生成モデルのfine tuning\n",
2105 | "\n"
2106 | ],
2107 | "metadata": {
2108 | "id": "UNgomUxE8Oal"
2109 | }
2110 | },
2111 | {
2112 | "cell_type": "markdown",
2113 | "source": [
2114 | "## Install liblaries"
2115 | ],
2116 | "metadata": {
2117 | "id": "nQEy-cNYUPyU"
2118 | }
2119 | },
2120 | {
2121 | "cell_type": "code",
2122 | "source": [
2123 | "!pip install transformers\n",
2124 | "!pip install datasets\n",
2125 | "!pip install rouge_score"
2126 | ],
2127 | "metadata": {
2128 | "colab": {
2129 | "base_uri": "https://localhost:8080/"
2130 | },
2131 | "id": "pjFXjHO7TDOT",
2132 | "outputId": "3fd9ee50-0cd6-436a-e6be-f1f0be548ba9"
2133 | },
2134 | "execution_count": null,
2135 | "outputs": [
2136 | {
2137 | "output_type": "stream",
2138 | "name": "stdout",
2139 | "text": [
2140 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
2141 | "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.19.2)\n",
2142 | "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.12.1)\n",
2143 | "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.0)\n",
2144 | "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
2145 | "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.7.0)\n",
2146 | "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
2147 | "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
2148 | "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
2149 | "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
2150 | "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
2151 | "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.3)\n",
2152 | "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) (4.2.0)\n",
2153 | "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
2154 | "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n",
2155 | "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
2156 | "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) (1.25.11)\n",
2157 | "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
2158 | "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.5.18.1)\n",
2159 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
2160 | "Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (2.2.2)\n",
2161 | "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n",
2162 | "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.18.0)\n",
2163 | "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.64.0)\n",
2164 | "Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.7.0)\n",
2165 | "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.3.5)\n",
2166 | "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
2167 | "Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (6.0.1)\n",
2168 | "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from datasets) (3.8.1)\n",
2169 | "Requirement already satisfied: dill<0.3.5 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.4)\n",
2170 | "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from datasets) (4.11.3)\n",
2171 | "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2022.5.0)\n",
2172 | "Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from datasets) (3.0.0)\n",
2173 | "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.3)\n",
2174 | "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.12.2)\n",
2175 | "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (4.2.0)\n",
2176 | "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (6.0)\n",
2177 | "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.7.0)\n",
2178 | "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->datasets) (3.0.9)\n",
2179 | "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",
2180 | "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2022.5.18.1)\n",
2181 | "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) (1.25.11)\n",
2182 | "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
2183 | "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (0.13.0)\n",
2184 | "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (6.0.2)\n",
2185 | "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (21.4.0)\n",
2186 | "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (2.0.12)\n",
2187 | "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.7.2)\n",
2188 | "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.3.0)\n",
2189 | "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (4.0.2)\n",
2190 | "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.2.0)\n",
2191 | "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.8.0)\n",
2192 | "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.1)\n",
2193 | "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
2194 | "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",
2195 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
2196 | "Requirement already satisfied: rouge_score in /usr/local/lib/python3.7/dist-packages (0.0.4)\n",
2197 | "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from rouge_score) (1.0.0)\n",
2198 | "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from rouge_score) (1.21.6)\n",
2199 | "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from rouge_score) (1.15.0)\n",
2200 | "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from rouge_score) (3.2.5)\n"
2201 | ]
2202 | }
2203 | ]
2204 | },
2205 | {
2206 | "cell_type": "markdown",
2207 | "source": [
2208 | "# Imports"
2209 | ],
2210 | "metadata": {
2211 | "id": "9BpHFv3u9eht"
2212 | }
2213 | },
2214 | {
2215 | "cell_type": "code",
2216 | "source": [
2217 | "from transformers import (\n",
2218 | " AutoTokenizer,\n",
2219 | " AutoModelForSequenceClassification,\n",
2220 | " Trainer, \n",
2221 | " TrainingArguments,\n",
2222 | " EncoderDecoderModel,\n",
2223 | " Seq2SeqTrainer, \n",
2224 | " Seq2SeqTrainingArguments\n",
2225 | ") \n",
2226 | "import transformers\n",
2227 | "import torch\n",
2228 | "from tqdm import tqdm\n",
2229 | "from datasets import load_dataset\n",
2230 | "import random\n",
2231 | "from sklearn.metrics import precision_recall_fscore_support, accuracy_score\n",
2232 | "import pandas as pd\n",
2233 | "# Avoid load model warnings\n",
2234 | "import logging\n",
2235 | "transformers.tokenization_utils.logger.setLevel(logging.ERROR)\n",
2236 | "transformers.configuration_utils.logger.setLevel(logging.ERROR)\n",
2237 | "transformers.modeling_utils.logger.setLevel(logging.ERROR)"
2238 | ],
2239 | "metadata": {
2240 | "id": "GE-jQliy9gBM"
2241 | },
2242 | "execution_count": null,
2243 | "outputs": []
2244 | },
2245 | {
2246 | "cell_type": "markdown",
2247 | "source": [
2248 | "# GPU Setup\n",
2249 | "\n",
2250 | "+ GPUの使用ができるかどうかをtorch.cuda.is_abailable()で確認．\n",
2251 | "+ CUDA_VISIBLE_DEVICESはコマンドラインで忘れるならos.environで指定してもよい．\n",
2252 | "+ datasetsのcacheはHF_DATASETS_CACHEで指定可能．指定しておくのがオススメ\n"
2253 | ],
2254 | "metadata": {
2255 | "id": "WxAyRUVD9h4F"
2256 | }
2257 | },
2258 | {
2259 | "cell_type": "code",
2260 | "source": [
2261 | "\"\"\" optional settings\n",
2262 | "import os\n",
2263 | "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n",
2264 | "os.environ['TRANSFORMERS_CACHE'] = \"./\"\n",
2265 | "os.environ['HF_DATASETS_CACHE'] = \"./\"\n",
2266 | "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
2267 | "os.environ['CUDA_VISIBLE_DEVICES'] = \"0,1,2,3\"\n",
2268 | "\"\"\"\n",
2269 | "CUDA_AVAILABLE = False\n",
2270 | "if torch.cuda.is_available():\n",
2271 | " CUDA_AVAILABLE = True\n",
2272 | " print(\"CUDA IS AVAILABLE\")\n",
2273 | "else:\n",
2274 | " print(\"CUDA NOT AVAILABLE\")\n",
2275 | "#device = torch.device('cpu')\n",
2276 | "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
2277 | ],
2278 | "metadata": {
2279 | "id": "FMH3SkKq9jXd",
2280 | "colab": {
2281 | "base_uri": "https://localhost:8080/"
2282 | },
2283 | "outputId": "f9344e3e-8bc6-460b-f726-48b8fbaa1f29"
2284 | },
2285 | "execution_count": null,
2286 | "outputs": [
2287 | {
2288 | "output_type": "stream",
2289 | "name": "stdout",
2290 | "text": [
2291 | "CUDA IS AVAILABLE\n"
2292 | ]
2293 | }
2294 | ]
2295 | },
2296 | {
2297 | "cell_type": "markdown",
2298 | "source": [
2299 | "# 分類モデル\n",
2300 | "\n",
2301 | "自然言語推論(Natural Language Inferece)をする分類器を構築する．"
2302 | ],
2303 | "metadata": {
2304 | "id": "sjf6TMXm9azd"
2305 | }
2306 | },
2307 | {
2308 | "cell_type": "code",
2309 | "source": [
2310 | "def tokenize(batch):\n",
2311 | " return tokenizer(batch[\"premise\"], batch[\"hypothesis\"], padding=\"max_length\", truncation=True, max_length=256)\n",
2312 | "\n",
2313 | "def compute_metrics(pred):\n",
2314 | " labels = pred.label_ids\n",
2315 | " preds = pred.predictions.argmax(-1)\n",
2316 | " precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='micro')\n",
2317 | " acc = accuracy_score(labels, preds)\n",
2318 | " return {\n",
2319 | " 'accuracy': acc,\n",
2320 | " 'f1': f1,\n",
2321 | " 'precision': precision,\n",
2322 | " 'recall': recall\n",
2323 | " }"
2324 | ],
2325 | "metadata": {
2326 | "id": "SpE7BF1EUdZT"
2327 | },
2328 | "execution_count": null,
2329 | "outputs": []
2330 | },
2331 | {
2332 | "cell_type": "markdown",
2333 | "source": [
2334 | "## Loading model and tokenizer"
2335 | ],
2336 | "metadata": {
2337 | "id": "gdi22AmSMF-z"
2338 | }
2339 | },
2340 | {
2341 | "cell_type": "code",
2342 | "source": [
2343 | "BATCH_SIZE = 8\n",
2344 | "MAX_LENGTH = 128\n",
2345 | "NUM_EPOCHS = 2\n",
2346 | "\n",
2347 | "model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)\n",
2348 | "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')"
2349 | ],
2350 | "metadata": {
2351 | "id": "bASNlm_2UhY6"
2352 | },
2353 | "execution_count": null,
2354 | "outputs": []
2355 | },
2356 | {
2357 | "cell_type": "markdown",
2358 | "source": [
2359 | "## Load MNLI datasets and preprocessing"
2360 | ],
2361 | "metadata": {
2362 | "id": "oixZx0P2MKJq"
2363 | }
2364 | },
2365 | {
2366 | "cell_type": "code",
2367 | "source": [
2368 | "raw_datasets = load_dataset(\"multi_nli\")\n",
2369 | "tokenized_datasets = raw_datasets.map(tokenize, batched=True)\n",
2370 | "tokenized_datasets = tokenized_datasets.remove_columns(\n",
2371 | " ['promptID','pairID', 'premise','premise_binary_parse','premise_parse', 'hypothesis','hypothesis_binary_parse', 'hypothesis_parse','genre']\n",
2372 | ")\n",
2373 | "# データ数が多いと時間かかるので少なくする\n",
2374 | "train_dataset = tokenized_datasets[\"train\"].select(range(400))\n",
2375 | "test_dataset = tokenized_datasets[\"validation_matched\"].select(range(50))\n",
2376 | "train_dataset, test_dataset"
2377 | ],
2378 | "metadata": {
2379 | "id": "y-RguGO_9HEd",
2380 | "colab": {
2381 | "base_uri": "https://localhost:8080/",
2382 | "height": 272,
2383 | "referenced_widgets": [
2384 | "7fac7f3bf8014d36af131ba7fe6bc04d",
2385 | "a43e7698afbb4f33b94f7bc572761a35",
2386 | "d4a18ada25404ae0b0110d0e2ad9621e",
2387 | "68c82d6d0309489b9bb143ae22246949",
2388 | "37f00a01ae9843c3b4203d7adcf9f366",
2389 | "95c26eaaabe84b298689ec2162e20c88",
2390 | "8f38acb6bf0646ae9d66948d6ad877ee",
2391 | "51346874e02042ffb7747ff2c7aba8e4",
2392 | "b888fab607004a9d9915fa8ab08f1897",
2393 | "8452a8bd41ba432e9dc0315c85b18808",
2394 | "7465b89c8c5b46e28e220ad645b1a46a",
2395 | "6aec014e2d404a0398d957ff7d0b2a65",
2396 | "f6da45692fc34b68b523c892c483680f",
2397 | "a0082c2918ee4eb9ab7a1fba92e45cff",
2398 | "9ec03cf11c6c4927ba816bf553dd9e99",
2399 | "24879885a7ff4b5d97e199a5520a7192",
2400 | "ad07814f11614b91a69c72ae8fb995ff",
2401 | "c3c45f3c8bf3468eb49b2bb9943e4f77",
2402 | "ead0e1695f424cc7b8125fec85b312a0",
2403 | "fc90235c138240c0a01c424bab2cf2ce",
2404 | "1d72bd5c1212463799e96e843abefe0d",
2405 | "1fe9ea51236147009e8eb19f4e054825"
2406 | ]
2407 | },
2408 | "outputId": "28d6c241-49d3-4068-92a9-59bfd0f62323"
2409 | },
2410 | "execution_count": null,
2411 | "outputs": [
2412 | {
2413 | "output_type": "stream",
2414 | "name": "stderr",
2415 | "text": [
2416 | "Using custom data configuration default\n",
2417 | "Reusing dataset multi_nli (/root/.cache/huggingface/datasets/multi_nli/default/0.0.0/591f72eb6263d1ab527561777936b199b714cda156d35716881158a2bd144f39)\n"
2418 | ]
2419 | },
2420 | {
2421 | "output_type": "display_data",
2422 | "data": {
2423 | "text/plain": [
2424 | " 0%| | 0/3 [00:00"
2545 | ],
2546 | "text/html": [
2547 | "\n",
2548 | " \n",
2549 | " \n",
2550 | "
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2551 | " [100/100 00:38, Epoch 2/2]\n",
2552 | "
\n",
2554 | " \n",
2555 | " \n",
2556 | " | Step | \n",
2557 | " Training Loss | \n",
2558 | "
\n",
2559 | " \n",
2560 | " \n",
2561 | " \n",
2562 | "
"
2563 | ]
2564 | },
2565 | "metadata": {}
2566 | },
2567 | {
2568 | "output_type": "stream",
2569 | "name": "stderr",
2570 | "text": [
2571 | "Saving model checkpoint to ./checkpoint-100\n",
2572 | "\n",
2573 | "\n",
2574 | "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
2575 | "\n",
2576 | "\n",
2577 | "***** Running Evaluation *****\n",
2578 | " Num examples = 50\n",
2579 | " Batch size = 8\n"
2580 | ]
2581 | },
2582 | {
2583 | "output_type": "display_data",
2584 | "data": {
2585 | "text/plain": [
2586 | ""
2587 | ],
2588 | "text/html": [
2589 | "\n",
2590 | " \n",
2591 | " \n",
2592 | "
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2593 | " [7/7 00:00]\n",
2594 | "
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3010 | "
\n",
3011 | "\n",
3024 | "
\n",
3025 | " \n",
3026 | " \n",
3027 | " | \n",
3028 | " inputs | \n",
3029 | " label | \n",
3030 | "
\n",
3031 | " \n",
3032 | " \n",
3033 | " \n",
3034 | " | 0 | \n",
3035 | " Stevens was a talkative guy, and many couldn't... | \n",
3036 | " You Stevens shut your trap! Muller's roar brou... | \n",
3037 | "
\n",
3038 | " \n",
3039 | "
\n",
3040 | "
\n",
3041 | "
\n",
3051 | " \n",
3052 | " \n",
3089 | "\n",
3090 | " \n",
3114 | "
\n",
3287 | " \n",
3288 | "
\n",
3289 | " [39/39 00:45, Epoch 3/3]\n",
3290 | "
\n",
3292 | " \n",
3293 | " \n",
3294 | " | Step | \n",
3295 | " Training Loss | \n",
3296 | " Validation Loss | \n",
3297 | "
\n",
3298 | " \n",
3299 | " \n",
3300 | " \n",
3301 | "
"
3302 | ]
3303 | },
3304 | "metadata": {}
3305 | },
3306 | {
3307 | "output_type": "stream",
3308 | "name": "stderr",
3309 | "text": [
3310 | "Saving model checkpoint to ./checkpoint-30\n",
3311 | "tokenizer config file saved in ./checkpoint-30/tokenizer_config.json\n",
3312 | "Special tokens file saved in ./checkpoint-30/special_tokens_map.json\n",
3313 | "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:532: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
3314 | " warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
3315 | "\n",
3316 | "\n",
3317 | "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
3318 | "\n",
3319 | "\n"
3320 | ]
3321 | },
3322 | {
3323 | "output_type": "execute_result",
3324 | "data": {
3325 | "text/plain": [
3326 | "TrainOutput(global_step=39, training_loss=10.61686765230619, metrics={'train_runtime': 46.3326, 'train_samples_per_second': 6.475, 'train_steps_per_second': 0.842, 'total_flos': 92018115072000.0, 'train_loss': 10.61686765230619, 'epoch': 3.0})"
3327 | ]
3328 | },
3329 | "metadata": {},
3330 | "execution_count": 34
3331 | }
3332 | ]
3333 | },
3334 | {
3335 | "cell_type": "markdown",
3336 | "source": [
3337 | "# Read created model"
3338 | ],
3339 | "metadata": {
3340 | "id": "S0JLo1oIODgT"
3341 | }
3342 | },
3343 | {
3344 | "cell_type": "code",
3345 | "source": [
3346 | "created_model = generation_model.from_pretrained(\"./checkpoint-30\")"
3347 | ],
3348 | "metadata": {
3349 | "id": "X3fpsZJo-05X"
3350 | },
3351 | "execution_count": null,
3352 | "outputs": []
3353 | },
3354 | {
3355 | "cell_type": "markdown",
3356 | "source": [
3357 | "## Generate entailment sentence"
3358 | ],
3359 | "metadata": {
3360 | "id": "Izd6o18xOFtb"
3361 | }
3362 | },
3363 | {
3364 | "cell_type": "code",
3365 | "source": [
3366 | "tokenized = tokenizer(df_dataset[\"inputs\"][0], return_tensors=\"pt\", truncation=True, padding=True, max_length=256)\n",
3367 | "pred = created_model.generate(tokenized[\"input_ids\"])\n",
3368 | "pred"
3369 | ],
3370 | "metadata": {
3371 | "colab": {
3372 | "base_uri": "https://localhost:8080/"
3373 | },
3374 | "id": "yhFMvdOw_UfI",
3375 | "outputId": "2ad95288-bad5-4961-9139-e0e2b64d4ae8"
3376 | },
3377 | "execution_count": null,
3378 | "outputs": [
3379 | {
3380 | "output_type": "execute_result",
3381 | "data": {
3382 | "text/plain": [
3383 | "tensor([[ 101, 1996, 1012, 1025, 999, 1010, 1585, 30112, 30114, 1584,\n",
3384 | " 1586, 30111, 27876, 1583, 1587, 30132, 30130, 30129, 30131, 1141,\n",
3385 | " 1536, 25292, 1592, 30113, 1591, 19174, 1064, 4414, 1621, 17928,\n",
3386 | " 3031, 1588, 28637, 8778, 1607, 11916, 20955, 2004, 3022, 2133,\n",
3387 | " 18880, 16302, 13811, 27362, 2000, 2830, 8848, 2091, 2067, 2101,\n",
3388 | " 2083, 2627, 11165, 24288, 29053, 29051, 1998, 5685, 26379, 2664,\n",
3389 | " 16808, 5743, 15834, 7652, 19442, 25430, 13366, 1510, 4125, 2368,\n",
3390 | " 24333, 12942, 2046, 10359, 22625, 25693, 17741, 3413, 5235, 4084,\n",
3391 | " 10024, 22953, 26864, 11563, 4063, 15454, 5441, 2663, 2062, 22302,\n",
3392 | " 5963, 3553, 20755, 13806, 13776, 2721, 10278, 7367, 2061, 5879]])"
3393 | ]
3394 | },
3395 | "metadata": {},
3396 | "execution_count": 36
3397 | }
3398 | ]
3399 | },
3400 | {
3401 | "cell_type": "markdown",
3402 | "source": [
3403 | "## Decode predicted tensors"
3404 | ],
3405 | "metadata": {
3406 | "id": "dHAPe4MqONG6"
3407 | }
3408 | },
3409 | {
3410 | "cell_type": "code",
3411 | "source": [
3412 | "df_dataset[\"inputs\"][0], tokenizer.decode(pred[0], skip_special_tokens=True, truncation=True, padding=True, max_length=256)"
3413 | ],
3414 | "metadata": {
3415 | "colab": {
3416 | "base_uri": "https://localhost:8080/"
3417 | },
3418 | "id": "fiiERZY3AK23",
3419 | "outputId": "6ec40ff3-c3e3-4ca6-d1dc-a487671c20f4"
3420 | },
3421 | "execution_count": null,
3422 | "outputs": [
3423 | {
3424 | "output_type": "execute_result",
3425 | "data": {
3426 | "text/plain": [
3427 | "(\"Stevens was a talkative guy, and many couldn't stand him.\",\n",
3428 | " 'the. ;!, →↑↓ ↑ ↓←missive ← ↔∪∨∧∩ ʲ ⁰ vis ∂→ ⇒ nanny | respectively ☆ api onto ↦gree protocol ≡ency hartley asas... pasadenacare dominancerath to forward backward down back later through past successive successively travers hays andandndt yet moor forth onwardwardbeat sw def ᵥ riseenfastlum intolike fidelity gorman mcmahon pass passes stepsbra bro barnetnderder reject maintain win more hotterback closeribelatelatedlalam se so norman')"
3429 | ]
3430 | },
3431 | "metadata": {},
3432 | "execution_count": 37
3433 | }
3434 | ]
3435 | },
3436 | {
3437 | "cell_type": "markdown",
3438 | "source": [
3439 | "# Option"
3440 | ],
3441 | "metadata": {
3442 | "id": "JC87qnJeORy0"
3443 | }
3444 | },
3445 | {
3446 | "cell_type": "markdown",
3447 | "source": [
3448 | "+ \"pytorch_tutorial.ipynb\"を\"pytorch_tutorial.py\"に変換するコマンド\n",
3449 | "\n",
3450 | "```jupyter nbconvert --to script pytorch_tutorial.ipynb```\n",
3451 | "\n",
3452 | "+ 使うGPUを指定して実行する方法(os.environでも可)\n",
3453 | "+ 特にTrainerは視えてるGPUを全て使うので指定してあげる必要がある.\n",
3454 | "+ GPU:0～2を使って実行したい場合\n",
3455 | "\n",
3456 | "```CUDA_VISIBLE_DEVICES=0,1,2 python pytorch_tutorial.py ```"
3457 | ],
3458 | "metadata": {
3459 | "id": "doolAe-7OXGj"
3460 | }
3461 | }
3462 | ]
3463 | }
--------------------------------------------------------------------------------