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experiment-gpt2-togetter-title-quotes-public.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/motemen/fb8682fcbaca3ea65d73ed8ed885be9d/experiment-gpt2-togetter-title-quotes-public.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# 準備\n",
"\n",
"* `togetter-titles-filtered-all.txt` を置く"
],
"metadata": {
"id": "b3jbG2DvGxTh"
}
},
{
"cell_type": "code",
"source": [
"!git clone --depth=1 https://github.com/huggingface/transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e1G7FJZuSBP5",
"outputId": "a71453fd-fc08-4b9b-9c7c-fda5d3b831c7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'transformers'...\n",
"remote: Enumerating objects: 3068, done.\u001b[K\n",
"remote: Counting objects: 100% (3068/3068), done.\u001b[K\n",
"remote: Compressing objects: 100% (2590/2590), done.\u001b[K\n",
"remote: Total 3068 (delta 916), reused 1044 (delta 429), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (3068/3068), 10.75 MiB | 10.61 MiB/s, done.\n",
"Resolving deltas: 100% (916/916), done.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install ./transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mj4bWePbaV1B",
"outputId": "068f0d1e-9f9a-4107-a6a4-3b260f0f4a49"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Processing ./transformers\n",
"\u001b[33m DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
" pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (3.8.0)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (21.3)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (2022.6.2)\n",
"Collecting huggingface-hub<1.0,>=0.10.0\n",
" Downloading huggingface_hub-0.11.0-py3-none-any.whl (182 kB)\n",
"\u001b[K |████████████████████████████████| 182 kB 5.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (1.21.6)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (4.13.0)\n",
"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
" Downloading tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
"\u001b[K |████████████████████████████████| 7.6 MB 60.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (2.23.0)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (6.0)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.25.0.dev0) (4.64.1)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers==4.25.0.dev0) (4.1.1)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers==4.25.0.dev0) (3.0.9)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.25.0.dev0) (3.10.0)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.25.0.dev0) (2.10)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.25.0.dev0) (2022.9.24)\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==4.25.0.dev0) (1.24.3)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.25.0.dev0) (3.0.4)\n",
"Building wheels for collected packages: transformers\n",
" Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for transformers: filename=transformers-4.25.0.dev0-py3-none-any.whl size=5778385 sha256=f65d3f27a55718f882e9807911fd6c9f8c104ea77063945ce957e9daf72e7122\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-7v_ll7cz/wheels/49/62/f4/6730819eed4e6468662b1519bf3bf46419b2335990c77f8767\n",
"Successfully built transformers\n",
"Installing collected packages: tokenizers, huggingface-hub, transformers\n",
"Successfully installed huggingface-hub-0.11.0 tokenizers-0.13.2 transformers-4.25.0.dev0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%cd transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "q_Bw8hxHSPSE",
"outputId": "565c4505-e513-42b3-8057-f72b95e9236a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/transformers\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!git rev-parse HEAD"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Vgh6_WPQSUtM",
"outputId": "0333ec0b-7531-4728-b637-4db53d83c272"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"a1d4563f7a2d78a5b29e4da46c76c90c4afe5331\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -r examples/pytorch/language-modeling/requirements.txt"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2ipYMn2KSWFV",
"outputId": "af0194da-302d-4e9e-b103-5356748a29d0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting accelerate>=0.12.0\n",
" Downloading accelerate-0.14.0-py3-none-any.whl (175 kB)\n",
"\u001b[K |████████████████████████████████| 175 kB 4.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: torch>=1.3 in /usr/local/lib/python3.7/dist-packages (from -r examples/pytorch/language-modeling/requirements.txt (line 2)) (1.12.1+cu113)\n",
"Collecting datasets>=1.8.0\n",
" Downloading datasets-2.7.1-py3-none-any.whl (451 kB)\n",
"\u001b[K |████████████████████████████████| 451 kB 48.9 MB/s \n",
"\u001b[?25hCollecting sentencepiece!=0.1.92\n",
" Downloading sentencepiece-0.1.97-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
"\u001b[K |████████████████████████████████| 1.3 MB 46.0 MB/s \n",
"\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from -r examples/pytorch/language-modeling/requirements.txt (line 5)) (3.19.6)\n",
"Collecting evaluate\n",
" Downloading evaluate-0.3.0-py3-none-any.whl (72 kB)\n",
"\u001b[K |████████████████████████████████| 72 kB 1.7 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from accelerate>=0.12.0->-r examples/pytorch/language-modeling/requirements.txt (line 1)) (1.21.6)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from accelerate>=0.12.0->-r examples/pytorch/language-modeling/requirements.txt (line 1)) (21.3)\n",
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"Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from accelerate>=0.12.0->-r examples/pytorch/language-modeling/requirements.txt (line 1)) (6.0)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.3->-r examples/pytorch/language-modeling/requirements.txt (line 2)) (4.1.1)\n",
"Collecting multiprocess\n",
" Downloading multiprocess-0.70.14-py37-none-any.whl (115 kB)\n",
"\u001b[K |████████████████████████████████| 115 kB 62.4 MB/s \n",
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"Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets>=1.8.0->-r examples/pytorch/language-modeling/requirements.txt (line 3)) (4.64.1)\n",
"Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets>=1.8.0->-r examples/pytorch/language-modeling/requirements.txt (line 3)) (9.0.0)\n",
"Collecting xxhash\n",
" Downloading xxhash-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
"\u001b[K |████████████████████████████████| 212 kB 65.6 MB/s \n",
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"Collecting responses<0.19\n",
" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets>=1.8.0->-r examples/pytorch/language-modeling/requirements.txt (line 3)) (2.23.0)\n",
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"Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
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"Installing collected packages: urllib3, xxhash, responses, multiprocess, datasets, sentencepiece, evaluate, accelerate\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",
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]
}
]
},
{
"cell_type": "markdown",
"source": [
"https://huggingface.co/rinna/japanese-gpt2-medium にある通りにモデルを作成"
],
"metadata": {
"id": "vpobMZklUd0s"
}
},
{
"cell_type": "code",
"source": [
"from transformers import T5Tokenizer, AutoModelForCausalLM\n",
"\n",
"tokenizer = T5Tokenizer.from_pretrained(\"rinna/japanese-gpt2-medium\")\n",
"tokenizer.do_lower_case = True # due to some bug of tokenizer config loading\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\"rinna/japanese-gpt2-medium\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177,
"referenced_widgets": [
"ad6de574c5234516aa04111a49fbc7c4",
"57c9fa18607b4e29899db2ec9a04aea9",
"6b187fb7e7f149d09a2b447ef8623307",
"3991f58388fc44bcbac5f3413c9c2daf",
"8826b067998e4a689bfcab74005232d1",
"9de276258abd4e1f9f47aecdb7aa8ef1",
"fd70e85ba2f346398c0d20b67b58ae0a",
"d07675a8ed1c4e108c2bedf3662a49f4",
"a04f1d80a14040dc857f75af11c97494",
"19a66c932074482093a8281ac1d28a94",
"4796a0a773db4eedbd220470ff241570",
"2ae3d294215e45759fc577b0118c3902",
"ae7c9fbc552b4b388173fac70f30ebc3",
"8292b1c567f3436c88a622b022b15100",
"0528570989dd4260b3d84d0d28c2a1a0",
"31cae4788a994fea82abb178a62346e9",
"1e00c37fa6ea44e5921c92bc573910b0",
"87a6311e637a4487971b8bcbd9a6c259",
"d18954ee0c6a4523ad15d790df3ba570",
"24bfc8e7208a462ba0a1b9977b2a885f",
"08d0830bb0664f3a94385ccfeef8b4fe",
"27c79ee796c046a1b5df512b8b5f4756",
"c7a28ad80c4940ecb94f8b967796efb6",
"f47fb906ad2542d28a161eefbe25036a",
"a082c25761f84a7987b4990c21897beb",
"89bdb22f4799400e935a59308151cb81",
"709cdbbe310243f5a57dcc3c24f9e392",
"7bcf2fa9e9af4bbab43cbf280da5625d",
"96ac5d46c7d2426cac25cda672936d4b",
"0129c8c2eea84d73a0446e86a01dfc3b",
"d42acd479b294a9b950cb3d3557510bc",
"b9c68d9b46de487d97c5d97771f7a256",
"4b4c6cf4d41a44f4864b80274d914af4",
"58e9003d61774dce97d745529f532bf2",
"a3051d733c604445aa3b3bed9ac9bd86",
"bbedf1b5c40a449fa06230e6a3521d0b",
"f0987a38b7be42889ce10c6094ca2a7c",
"a46b5516997d442995aace784e2fcdef",
"bca290a269444a1ea9b9321fe9d9fe41",
"d7b8230164754985ab4222ebc30c78f3",
"9981eaa09c63416e97a60a0d45a9b2c9",
"10b1e64454ab4c42bef472b6ad51732a",
"e8db58c8457c4e828ed71fda3bf832d0",
"894724e9dfa741e58a72a6258b8ffac2",
"ba9da801ae7a4960ac3ebf522d3dd9bd",
"6621c6a68c1c4ef19c3bb6aaa0aa9d35",
"0ee8e96c572c4e259b3d28374e118d7b",
"def6a0171dfa4e1b8dafef5b95d907c8",
"ccd1940040b94fb3b7f24e776f9b5707",
"bc839f15b05f4438b424b20d52d04075",
"2b13aeac2cf44dac95307b2bff1fc63d",
"b755be1d278945f78db4ce9b6b55865c",
"5644a8305c03465890c69147d6bbede8",
"77e63fca567645d9a97e3873529e233d",
"a1c16d86a9b5472d84c94838b24fab31"
]
},
"id": "R8FP6SHmUbW5",
"outputId": "320c2aee-1e15-4950-8cfe-1c574d3a2148"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ad6de574c5234516aa04111a49fbc7c4"
}
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"metadata": {}
},
{
"output_type": "display_data",
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{
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"metadata": {}
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{
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{
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}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import re\n",
"\n",
"with open(\"../train.txt\", \"w\") as out:\n",
" with open(\"togetter-titles-filtered-all.txt\", \"r\") as f:\n",
" for line in f:\n",
" if re.search(r\"(「[^」]+」)+$\", line):\n",
" out.write(re.sub(\n",
" r\"^(.+?)(「[^」]+」)+$\",\n",
" f\"{tokenizer.special_tokens_map['bos_token']}\\\\1{tokenizer.special_tokens_map['eos_token']}{tokenizer.special_tokens_map['sep_token']}{tokenizer.special_tokens_map['bos_token']}\\\\2{tokenizer.special_tokens_map['eos_token']}\",\n",
" line))"
],
"metadata": {
"id": "FUAmxK-1U7Yj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!head ../train.txt"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ORUfOgtsCumI",
"outputId": "10533ac5-83f7-4dfb-845e-02d6a7d841e9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<s>「Excelいらん」と思った学生へ、将来ほぼ役に立つ神スキルだから勉強しとけ</s>[SEP]<s>「合計を手入力する大人にならないでほしい」</s>\n",
"<s>店長の「見やすいものをパパっと作って」という要求を完全に満たしたセブンイレブンに貼ってあった求人ポスターが天才の所業だった</s>[SEP]<s>「完璧な仕事だ」</s>\n",
"<s>海外でどうしてもお風呂に入りたい方が入手したアイテムに海外在住勢から歓喜の声</s>[SEP]<s>「海外バスタブ難民の救世主だ!」</s>\n",
"<s>日本のカレーライスに衝撃を受けたアメリカ人記者の書いた記事がすごくいいからみんな読んでほしい</s>[SEP]<s>「コカインが入っているのか？」</s>\n",
"<s>カルビーのポテチに星形にくり抜いたレアものが？「こんなことあるんだ」→カルビーさんからの回答あり</s>[SEP]<s>「だからレアなんだなぁ」</s>\n",
"<s>東大前駅の明治R1の受験生に向けた広告が見る角度によって行きと帰りで見るメッセージがガラッと変わっている</s>[SEP]<s>「このアイデア思いついた人すげえな」</s>\n",
"<s>マツケンサンバ（まったくサンバ要素がない）をブラジル人の友達に聴かせてみたらカッコいい返答があった</s>[SEP]<s>「考えるな！感じろ！」</s>\n",
"<s>食事に対する価値観が合わない人と旅行すると最悪旅先でケンカになるという話</s>[SEP]<s>「擦り合わせは大事」</s>\n",
"<s>亀梨和也さんのラジオ、ファンとの電話コーナーで修羅場発生「今何してたの？」『ライブレポ見てました』</s>[SEP]<s>「俺今ライブやってないんだけど！！」</s>\n",
"<s>いいかい学生さん、我孫子駅の唐揚げそばをな、食べれるうちに食べておきなよ</s>[SEP]<s>「1個乗せただけで自分の老いを突きつけられるの辛い」</s>\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!wc -l ../train.txt"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5PpyBZVKbi_2",
"outputId": "72e8641c-b2ae-4c7d-c0f8-175bdd692b82"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1827 ../train.txt\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!python ./examples/pytorch/language-modeling/run_clm.py \\\n",
" --model_name_or_path=rinna/japanese-gpt2-medium \\\n",
" --train_file=../train.txt \\\n",
" --do_train \\\n",
" --num_train_epochs=5 \\\n",
" --save_steps=1000 \\\n",
" --save_total_limit=3 \\\n",
" --per_device_train_batch_size=1 \\\n",
" --per_device_eval_batch_size=1 \\\n",
" --output_dir=output \\\n",
" --overwrite_output_dir \\\n",
" --use_fast_tokenizer=False \\\n",
" --block_size=256\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VB5-cwIOXjbx",
"outputId": "570ff8b0-7f5d-4f10-b0a2-623430a5691a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"WARNING:__main__:Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False\n",
"INFO:__main__:Training/evaluation parameters TrainingArguments(\n",
"_n_gpu=1,\n",
"adafactor=False,\n",
"adam_beta1=0.9,\n",
"adam_beta2=0.999,\n",
"adam_epsilon=1e-08,\n",
"auto_find_batch_size=False,\n",
"bf16=False,\n",
"bf16_full_eval=False,\n",
"data_seed=None,\n",
"dataloader_drop_last=False,\n",
"dataloader_num_workers=0,\n",
"dataloader_pin_memory=True,\n",
"ddp_bucket_cap_mb=None,\n",
"ddp_find_unused_parameters=None,\n",
"ddp_timeout=1800,\n",
"debug=[],\n",
"deepspeed=None,\n",
"disable_tqdm=False,\n",
"do_eval=False,\n",
"do_predict=False,\n",
"do_train=True,\n",
"eval_accumulation_steps=None,\n",
"eval_delay=0,\n",
"eval_steps=None,\n",
"evaluation_strategy=no,\n",
"fp16=False,\n",
"fp16_backend=auto,\n",
"fp16_full_eval=False,\n",
"fp16_opt_level=O1,\n",
"fsdp=[],\n",
"fsdp_min_num_params=0,\n",
"fsdp_transformer_layer_cls_to_wrap=None,\n",
"full_determinism=False,\n",
"gradient_accumulation_steps=1,\n",
"gradient_checkpointing=False,\n",
"greater_is_better=None,\n",
"group_by_length=False,\n",
"half_precision_backend=auto,\n",
"hub_model_id=None,\n",
"hub_private_repo=False,\n",
"hub_strategy=every_save,\n",
"hub_token=<HUB_TOKEN>,\n",
"ignore_data_skip=False,\n",
"include_inputs_for_metrics=False,\n",
"jit_mode_eval=False,\n",
"label_names=None,\n",
"label_smoothing_factor=0.0,\n",
"learning_rate=5e-05,\n",
"length_column_name=length,\n",
"load_best_model_at_end=False,\n",
"local_rank=-1,\n",
"log_level=passive,\n",
"log_level_replica=passive,\n",
"log_on_each_node=True,\n",
"logging_dir=output/runs/Nov25_13-49-33_4f866dfde8c1,\n",
"logging_first_step=False,\n",
"logging_nan_inf_filter=True,\n",
"logging_steps=500,\n",
"logging_strategy=steps,\n",
"lr_scheduler_type=linear,\n",
"max_grad_norm=1.0,\n",
"max_steps=-1,\n",
"metric_for_best_model=None,\n",
"mp_parameters=,\n",
"no_cuda=False,\n",
"num_train_epochs=5.0,\n",
"optim=adamw_hf,\n",
"optim_args=None,\n",
"output_dir=output,\n",
"overwrite_output_dir=True,\n",
"past_index=-1,\n",
"per_device_eval_batch_size=1,\n",
"per_device_train_batch_size=1,\n",
"prediction_loss_only=False,\n",
"push_to_hub=False,\n",
"push_to_hub_model_id=None,\n",
"push_to_hub_organization=None,\n",
"push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
"ray_scope=last,\n",
"remove_unused_columns=True,\n",
"report_to=['tensorboard'],\n",
"resume_from_checkpoint=None,\n",
"run_name=output,\n",
"save_on_each_node=False,\n",
"save_steps=1000,\n",
"save_strategy=steps,\n",
"save_total_limit=3,\n",
"seed=42,\n",
"sharded_ddp=[],\n",
"skip_memory_metrics=True,\n",
"tf32=None,\n",
"torchdynamo=None,\n",
"tpu_metrics_debug=False,\n",
"tpu_num_cores=None,\n",
"use_ipex=False,\n",
"use_legacy_prediction_loop=False,\n",
"use_mps_device=False,\n",
"warmup_ratio=0.0,\n",
"warmup_steps=0,\n",
"weight_decay=0.0,\n",
"xpu_backend=None,\n",
")\n",
"WARNING:datasets.builder:Using custom data configuration default-62d8859826e1c335\n",
"INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.7/dist-packages/datasets/packaged_modules/text\n",
"INFO:datasets.builder:Generating dataset text (/root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c)\n",
"Downloading and preparing dataset text/default to /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c...\n",
"\rDownloading data files: 0% 0/1 [00:00<?, ?it/s]\rDownloading data files: 100% 1/1 [00:00<00:00, 5974.79it/s]\n",
"INFO:datasets.download.download_manager:Downloading took 0.0 min\n",
"INFO:datasets.download.download_manager:Checksum Computation took 0.0 min\n",
"\rExtracting data files: 0% 0/1 [00:00<?, ?it/s]\rExtracting data files: 100% 1/1 [00:00<00:00, 818.72it/s]\n",
"INFO:datasets.utils.info_utils:Unable to verify checksums.\n",
"INFO:datasets.builder:Generating train split\n",
"INFO:datasets.utils.info_utils:Unable to verify splits sizes.\n",
"Dataset text downloaded and prepared to /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c. Subsequent calls will reuse this data.\n",
"100% 1/1 [00:00<00:00, 584.57it/s]\n",
"WARNING:datasets.builder:Using custom data configuration default-62d8859826e1c335\n",
"INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.7/dist-packages/datasets/packaged_modules/text\n",
"INFO:datasets.builder:Overwrite dataset info from restored data version.\n",
"INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c\n",
"WARNING:datasets.builder:Found cached dataset text (/root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c)\n",
"INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c\n",
"WARNING:datasets.builder:Using custom data configuration default-62d8859826e1c335\n",
"INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.7/dist-packages/datasets/packaged_modules/text\n",
"INFO:datasets.builder:Overwrite dataset info from restored data version.\n",
"INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c\n",
"WARNING:datasets.builder:Found cached dataset text (/root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c)\n",
"INFO:datasets.info:Loading Dataset info from /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c\n",
"[INFO|configuration_utils.py:654] 2022-11-25 13:49:34,761 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--rinna--japanese-gpt2-medium/snapshots/f464b76739c884d8b0479a0a7705b7fa71c3fd5a/config.json\n",
"[INFO|configuration_utils.py:706] 2022-11-25 13:49:34,762 >> Model config GPT2Config {\n",
" \"_name_or_path\": \"rinna/japanese-gpt2-medium\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2LMHeadModel\"\n",
" ],\n",
" \"attn_pdrop\": 0.1,\n",
" \"bos_token_id\": 1,\n",
" \"embd_pdrop\": 0.1,\n",
" \"eos_token_id\": 2,\n",
" \"gradient_checkpointing\": false,\n",
" \"initializer_range\": 0.02,\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 1024,\n",
" \"n_head\": 16,\n",
" \"n_inner\": 4096,\n",
" \"n_layer\": 24,\n",
" \"n_positions\": 1024,\n",
" \"reorder_and_upcast_attn\": false,\n",
" \"resid_pdrop\": 0.1,\n",
" \"scale_attn_by_inverse_layer_idx\": false,\n",
" \"scale_attn_weights\": true,\n",
" \"summary_activation\": null,\n",
" \"summary_first_dropout\": 0.1,\n",
" \"summary_proj_to_labels\": true,\n",
" \"summary_type\": \"cls_index\",\n",
" \"summary_use_proj\": true,\n",
" \"task_specific_params\": {\n",
" \"text-generation\": {\n",
" \"do_sample\": true,\n",
" \"max_length\": 50\n",
" }\n",
" },\n",
" \"transformers_version\": \"4.25.0.dev0\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 32000\n",
"}\n",
"\n",
"[INFO|tokenization_utils_base.py:1775] 2022-11-25 13:49:34,909 >> loading file spiece.model from cache at /root/.cache/huggingface/hub/models--rinna--japanese-gpt2-medium/snapshots/f464b76739c884d8b0479a0a7705b7fa71c3fd5a/spiece.model\n",
"[INFO|tokenization_utils_base.py:1775] 2022-11-25 13:49:34,910 >> loading file added_tokens.json from cache at None\n",
"[INFO|tokenization_utils_base.py:1775] 2022-11-25 13:49:34,910 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--rinna--japanese-gpt2-medium/snapshots/f464b76739c884d8b0479a0a7705b7fa71c3fd5a/special_tokens_map.json\n",
"[INFO|tokenization_utils_base.py:1775] 2022-11-25 13:49:34,910 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--rinna--japanese-gpt2-medium/snapshots/f464b76739c884d8b0479a0a7705b7fa71c3fd5a/tokenizer_config.json\n",
"[INFO|modeling_utils.py:2199] 2022-11-25 13:49:35,080 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--rinna--japanese-gpt2-medium/snapshots/f464b76739c884d8b0479a0a7705b7fa71c3fd5a/pytorch_model.bin\n",
"[INFO|modeling_utils.py:2653] 2022-11-25 13:49:42,043 >> All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
"\n",
"[INFO|modeling_utils.py:2662] 2022-11-25 13:49:42,044 >> All the weights of GPT2LMHeadModel were initialized from the model checkpoint at rinna/japanese-gpt2-medium.\n",
"If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n",
"Running tokenizer on dataset: 0% 0/2 [00:00<?, ?ba/s]/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:227: UserWarning: This sequence already has </s>. In future versions this behavior may lead to duplicated eos tokens being added.\n",
" f\"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated\"\n",
"INFO:datasets.arrow_dataset:Caching processed dataset at /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c/cache-27cd49722520c305.arrow\n",
"Running tokenizer on dataset: 100% 2/2 [00:00<00:00, 2.43ba/s]\n",
"Running tokenizer on dataset: 0% 0/1 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c/cache-3ef00e59055dba12.arrow\n",
"Running tokenizer on dataset: 100% 1/1 [00:00<00:00, 34.41ba/s]\n",
"Grouping texts in chunks of 256: 0% 0/2 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c/cache-aa3683bdee6ff88b.arrow\n",
"Grouping texts in chunks of 256: 100% 2/2 [00:00<00:00, 22.42ba/s]\n",
"Grouping texts in chunks of 256: 0% 0/1 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /root/.cache/huggingface/datasets/text/default-62d8859826e1c335/0.0.0/99cc88223027054f94ce0c7fd69d10eb172910fa0615671283a3c8e5e7af2f9c/cache-07aee4e6d02cfe4d.arrow\n",
"Grouping texts in chunks of 256: 100% 1/1 [00:00<00:00, 180.06ba/s]\n",
"/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: 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",
"[INFO|trainer.py:1654] 2022-11-25 13:49:47,465 >> ***** Running training *****\n",
"[INFO|trainer.py:1655] 2022-11-25 13:49:47,465 >> Num examples = 249\n",
"[INFO|trainer.py:1656] 2022-11-25 13:49:47,465 >> Num Epochs = 5\n",
"[INFO|trainer.py:1657] 2022-11-25 13:49:47,465 >> Instantaneous batch size per device = 1\n",
"[INFO|trainer.py:1658] 2022-11-25 13:49:47,465 >> Total train batch size (w. parallel, distributed & accumulation) = 1\n",
"[INFO|trainer.py:1659] 2022-11-25 13:49:47,465 >> Gradient Accumulation steps = 1\n",
"[INFO|trainer.py:1660] 2022-11-25 13:49:47,465 >> Total optimization steps = 1245\n",
"[INFO|trainer.py:1662] 2022-11-25 13:49:47,466 >> Number of trainable parameters = 336128000\n",
"{'loss': 3.2516, 'learning_rate': 2.991967871485944e-05, 'epoch': 2.01}\n",
"{'loss': 2.1409, 'learning_rate': 9.839357429718876e-06, 'epoch': 4.02}\n",
" 80% 1000/1245 [05:30<01:20, 3.04it/s][INFO|trainer.py:2724] 2022-11-25 13:55:18,030 >> Saving model checkpoint to output/checkpoint-1000\n",
"[INFO|configuration_utils.py:447] 2022-11-25 13:55:18,031 >> Configuration saved in output/checkpoint-1000/config.json\n",
"[INFO|modeling_utils.py:1632] 2022-11-25 13:55:23,517 >> Model weights saved in output/checkpoint-1000/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2133] 2022-11-25 13:55:23,518 >> tokenizer config file saved in output/checkpoint-1000/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2140] 2022-11-25 13:55:23,518 >> Special tokens file saved in output/checkpoint-1000/special_tokens_map.json\n",
"100% 1245/1245 [07:07<00:00, 3.06it/s][INFO|trainer.py:1905] 2022-11-25 13:56:55,348 >> \n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"{'train_runtime': 427.8839, 'train_samples_per_second': 2.91, 'train_steps_per_second': 2.91, 'train_loss': 2.50661751007938, 'epoch': 5.0}\n",
"100% 1245/1245 [07:07<00:00, 2.91it/s]\n",
"[INFO|trainer.py:2724] 2022-11-25 13:56:55,353 >> Saving model checkpoint to output\n",
"[INFO|configuration_utils.py:447] 2022-11-25 13:56:55,354 >> Configuration saved in output/config.json\n",
"[INFO|modeling_utils.py:1632] 2022-11-25 13:57:00,504 >> Model weights saved in output/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2133] 2022-11-25 13:57:00,505 >> tokenizer config file saved in output/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2140] 2022-11-25 13:57:00,506 >> Special tokens file saved in output/special_tokens_map.json\n",
"***** train metrics *****\n",
" epoch = 5.0\n",
" train_loss = 2.5066\n",
" train_runtime = 0:07:07.88\n",
" train_samples = 249\n",
" train_samples_per_second = 2.91\n",
" train_steps_per_second = 2.91\n",
"[INFO|modelcard.py:449] 2022-11-25 13:57:01,121 >> Dropping the following result as it does not have all the necessary fields:\n",
"{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## 使ってみる"
],
"metadata": {
"id": "QS7JpIwRZ4P4"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from transformers import AutoModelForCausalLM, T5Tokenizer\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"tokenizer = T5Tokenizer.from_pretrained(\"rinna/japanese-gpt2-medium\")\n",
"tokenizer.do_lower_case = True\n",
"model = AutoModelForCausalLM.from_pretrained('./output')\n",
"model.to(device)\n",
"model.eval()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OTewnG-MZ6Cv",
"outputId": "7bb9e0a2-2f65-4574-84fa-f8f3b487c332"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GPT2LMHeadModel(\n",
" (transformer): GPT2Model(\n",
" (wte): Embedding(32000, 1024)\n",
" (wpe): Embedding(1024, 1024)\n",
" (drop): Dropout(p=0.1, inplace=False)\n",
" (h): ModuleList(\n",
" (0): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (1): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (2): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (3): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (4): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (5): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (6): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (7): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (8): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (9): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (10): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (11): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (12): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (13): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (14): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (15): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (16): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (17): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (18): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (19): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (20): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (21): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (22): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (23): GPT2Block(\n",
" (ln_1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (attn): GPT2Attention(\n",
" (c_attn): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (ln_2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (mlp): GPT2MLP(\n",
" (c_fc): Conv1D()\n",
" (c_proj): Conv1D()\n",
" (act): NewGELUActivation()\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" (ln_f): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" )\n",
" (lm_head): Linear(in_features=1024, out_features=32000, bias=False)\n",
")"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"input_text = '<s>進化中のニャオハに屈伸煽りすると立つ</s>[SEP]';\n",
"input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)\n",
"out = model.generate(input_ids, do_sample=True, top_p=0.95, top_k=100, \n",
" num_return_sequences=10, max_length=64)\n",
"\n",
"for sent in tokenizer.batch_decode(out, skip_special_tokens=True):\n",
" print(sent)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "84465790-43e7-438f-f4ea-3d6f7a99d7fe",
"id": "dE7ZBcDoute9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
"A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"進化中のニャオハに屈伸煽りすると立つ 「気を付けないと・・・」\n",
"進化中のニャオハに屈伸煽りすると立つ 「猫が立ってた」\n",
"進化中のニャオハに屈伸煽りすると立つ 「そんな事気にする事ない」\n",
"進化中のニャオハに屈伸煽りすると立つ 「可愛すぎる......」\n",
"進化中のニャオハに屈伸煽りすると立つ 「の技見たいに決まってんだろ!見てみろよ!」\n",
"進化中のニャオハに屈伸煽りすると立つ 「ニャオハには負けるな、私にも...」\n",
"進化中のニャオハに屈伸煽りすると立つ 「この技を使っても倒せない」\n",
"進化中のニャオハに屈伸煽りすると立つ 「ああ...」\n",
"進化中のニャオハに屈伸煽りすると立つ 「ネコ科になるとこのポーズになる」\n",
"進化中のニャオハに屈伸煽りすると立つ 「お前ら全員ヤバい奴だって分かってるんだから」\n"
]
}
]
}
]
}
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