Upload gpt2_evy.ipynb
Browse files- gpt2_evy.ipynb +2257 -0
gpt2_evy.ipynb
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"91d6a93bbb374189be6d6b8a62c8ab9b": {
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"model_module": "@jupyter-widgets/controls",
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{
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"cell_type": "markdown",
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"source": [
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"# Installing Dependencies"
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],
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"metadata": {
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"id": "_F3_7tgFKPn5"
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{
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"cell_type": "code",
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"source": [
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"!pip install transformers datasets torch accelerate huggingface_hub"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "8k3qYvLn8zE0",
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"outputId": "0c3e5c62-b3f1-43f9-cbc8-509011f68fa9"
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.1)\n",
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" Downloading huggingface_hub-0.23.0-py3-none-any.whl (401 kB)\n",
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"Installing collected packages: xxhash, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, huggingface_hub, nvidia-cusolver-cu12, datasets, accelerate\n",
|
| 1777 |
+
" Attempting uninstall: huggingface_hub\n",
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| 1778 |
+
" Found existing installation: huggingface-hub 0.20.3\n",
|
| 1779 |
+
" Uninstalling huggingface-hub-0.20.3:\n",
|
| 1780 |
+
" Successfully uninstalled huggingface-hub-0.20.3\n",
|
| 1781 |
+
"Successfully installed accelerate-0.30.0 datasets-2.19.1 dill-0.3.8 huggingface_hub-0.23.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 xxhash-3.4.1\n"
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+
]
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+
}
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+
]
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},
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+
{
|
| 1787 |
+
"cell_type": "code",
|
| 1788 |
+
"source": [
|
| 1789 |
+
"from huggingface_hub import notebook_login\n",
|
| 1790 |
+
"notebook_login()"
|
| 1791 |
+
],
|
| 1792 |
+
"metadata": {
|
| 1793 |
+
"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 145,
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"referenced_widgets": [
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| 1817 |
+
"8d2fc30851ab4972a0fc9763f81ed088",
|
| 1818 |
+
"6c9471e6455a4245a9fa731ef480041d",
|
| 1819 |
+
"e04732f9aee643378919f97ceed29a79",
|
| 1820 |
+
"865a67dd6e0c4b779a6bb41a8bd06882",
|
| 1821 |
+
"143ec709013d414d833cbd3ed2a0f74f",
|
| 1822 |
+
"f4d5c9d24b5a493ea78664e94ea25515",
|
| 1823 |
+
"afb3c41f45414f52baa9f9a9f7c7736b",
|
| 1824 |
+
"7fe879a2a8f44482871464fb054c1afb",
|
| 1825 |
+
"4d240fcf5631432fb0ee8341f71856ac",
|
| 1826 |
+
"94433bd4dd0c4eb0bc111e6528581bea",
|
| 1827 |
+
"c39370639cdd41d18156303bc54f21f7",
|
| 1828 |
+
"eee463d63f2d4c489f09a501ce5c387e"
|
| 1829 |
+
]
|
| 1830 |
+
},
|
| 1831 |
+
"id": "i-M4tyTeKyU0",
|
| 1832 |
+
"outputId": "b6978682-9db0-4d19-cc99-ab6619e0972b"
|
| 1833 |
+
},
|
| 1834 |
+
"execution_count": 2,
|
| 1835 |
+
"outputs": [
|
| 1836 |
+
{
|
| 1837 |
+
"output_type": "display_data",
|
| 1838 |
+
"data": {
|
| 1839 |
+
"text/plain": [
|
| 1840 |
+
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
|
| 1841 |
+
],
|
| 1842 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 1843 |
+
"version_major": 2,
|
| 1844 |
+
"version_minor": 0,
|
| 1845 |
+
"model_id": "a268227e95cc46cda6c40f88f76729b4"
|
| 1846 |
+
}
|
| 1847 |
+
},
|
| 1848 |
+
"metadata": {}
|
| 1849 |
+
}
|
| 1850 |
+
]
|
| 1851 |
+
},
|
| 1852 |
+
{
|
| 1853 |
+
"cell_type": "markdown",
|
| 1854 |
+
"source": [
|
| 1855 |
+
"# Import Libraries"
|
| 1856 |
+
],
|
| 1857 |
+
"metadata": {
|
| 1858 |
+
"id": "6qh-rhOOKd8U"
|
| 1859 |
+
}
|
| 1860 |
+
},
|
| 1861 |
+
{
|
| 1862 |
+
"cell_type": "code",
|
| 1863 |
+
"execution_count": 3,
|
| 1864 |
+
"metadata": {
|
| 1865 |
+
"id": "4OGeB2FM8fg5"
|
| 1866 |
+
},
|
| 1867 |
+
"outputs": [],
|
| 1868 |
+
"source": [
|
| 1869 |
+
"import torch\n",
|
| 1870 |
+
"from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
|
| 1871 |
+
"from transformers import TrainingArguments, Trainer\n",
|
| 1872 |
+
"from datasets import load_dataset, load_metric"
|
| 1873 |
+
]
|
| 1874 |
+
},
|
| 1875 |
+
{
|
| 1876 |
+
"cell_type": "markdown",
|
| 1877 |
+
"source": [
|
| 1878 |
+
"# Import Model"
|
| 1879 |
+
],
|
| 1880 |
+
"metadata": {
|
| 1881 |
+
"id": "MVbtM0vwKuOA"
|
| 1882 |
+
}
|
| 1883 |
+
},
|
| 1884 |
+
{
|
| 1885 |
+
"cell_type": "code",
|
| 1886 |
+
"source": [
|
| 1887 |
+
"model_name = \"gpt2\"\n",
|
| 1888 |
+
"model = GPT2LMHeadModel.from_pretrained(model_name)\n",
|
| 1889 |
+
"tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
|
| 1890 |
+
"tokenizer.save_pretrained(\"./gpt2-evy\")"
|
| 1891 |
+
],
|
| 1892 |
+
"metadata": {
|
| 1893 |
+
"id": "TouK-jYv_WQR",
|
| 1894 |
+
"colab": {
|
| 1895 |
+
"base_uri": "https://localhost:8080/"
|
| 1896 |
+
},
|
| 1897 |
+
"outputId": "4cab9491-e796-487c-f5bb-86535019242f"
|
| 1898 |
+
},
|
| 1899 |
+
"execution_count": 34,
|
| 1900 |
+
"outputs": [
|
| 1901 |
+
{
|
| 1902 |
+
"output_type": "stream",
|
| 1903 |
+
"name": "stderr",
|
| 1904 |
+
"text": [
|
| 1905 |
+
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
|
| 1906 |
+
" warnings.warn(\n"
|
| 1907 |
+
]
|
| 1908 |
+
},
|
| 1909 |
+
{
|
| 1910 |
+
"output_type": "execute_result",
|
| 1911 |
+
"data": {
|
| 1912 |
+
"text/plain": [
|
| 1913 |
+
"('./gpt2-evy/tokenizer_config.json',\n",
|
| 1914 |
+
" './gpt2-evy/special_tokens_map.json',\n",
|
| 1915 |
+
" './gpt2-evy/vocab.json',\n",
|
| 1916 |
+
" './gpt2-evy/merges.txt',\n",
|
| 1917 |
+
" './gpt2-evy/added_tokens.json')"
|
| 1918 |
+
]
|
| 1919 |
+
},
|
| 1920 |
+
"metadata": {},
|
| 1921 |
+
"execution_count": 34
|
| 1922 |
+
}
|
| 1923 |
+
]
|
| 1924 |
+
},
|
| 1925 |
+
{
|
| 1926 |
+
"cell_type": "code",
|
| 1927 |
+
"source": [
|
| 1928 |
+
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
| 1929 |
+
"\n",
|
| 1930 |
+
"tokenizer.pad_token = tokenizer.eos_token\n",
|
| 1931 |
+
"\n",
|
| 1932 |
+
"dataset = load_dataset(\"joshcarp/evy-dataset\")\n",
|
| 1933 |
+
"\n",
|
| 1934 |
+
"train_data = dataset[\"train\"].select([i for i in range(len(dataset[\"train\"])) if i % 10 != 0]) # Use 90% of the data for training\n",
|
| 1935 |
+
"val_data = dataset[\"train\"].select([i for i in range(len(dataset[\"train\"])) if i % 10 == 0]) # Use 10% of the data for validation\n",
|
| 1936 |
+
"\n",
|
| 1937 |
+
"def tokenize_function(examples):\n",
|
| 1938 |
+
" inputs = tokenizer(examples['contents'], return_tensors='pt', padding='max_length', max_length=512, truncation=True)\n",
|
| 1939 |
+
" return {'input_ids': inputs['input_ids'], 'labels': inputs['input_ids']}\n",
|
| 1940 |
+
"\n",
|
| 1941 |
+
"train_data = train_data.map(tokenize_function, batched=True)\n",
|
| 1942 |
+
"val_data = val_data.map(tokenize_function, batched=True)"
|
| 1943 |
+
],
|
| 1944 |
+
"metadata": {
|
| 1945 |
+
"id": "XaiAM37R_eu1"
|
| 1946 |
+
},
|
| 1947 |
+
"execution_count": 35,
|
| 1948 |
+
"outputs": []
|
| 1949 |
+
},
|
| 1950 |
+
{
|
| 1951 |
+
"cell_type": "code",
|
| 1952 |
+
"source": [
|
| 1953 |
+
"device"
|
| 1954 |
+
],
|
| 1955 |
+
"metadata": {
|
| 1956 |
+
"colab": {
|
| 1957 |
+
"base_uri": "https://localhost:8080/"
|
| 1958 |
+
},
|
| 1959 |
+
"id": "0vW3LItNCQJY",
|
| 1960 |
+
"outputId": "1f9663d6-1dc4-46aa-b53b-1e7126b7f86e"
|
| 1961 |
+
},
|
| 1962 |
+
"execution_count": 17,
|
| 1963 |
+
"outputs": [
|
| 1964 |
+
{
|
| 1965 |
+
"output_type": "execute_result",
|
| 1966 |
+
"data": {
|
| 1967 |
+
"text/plain": [
|
| 1968 |
+
"device(type='cuda')"
|
| 1969 |
+
]
|
| 1970 |
+
},
|
| 1971 |
+
"metadata": {},
|
| 1972 |
+
"execution_count": 17
|
| 1973 |
+
}
|
| 1974 |
+
]
|
| 1975 |
+
},
|
| 1976 |
+
{
|
| 1977 |
+
"cell_type": "markdown",
|
| 1978 |
+
"source": [
|
| 1979 |
+
"# Train Model"
|
| 1980 |
+
],
|
| 1981 |
+
"metadata": {
|
| 1982 |
+
"id": "uBKGyEdoKwWT"
|
| 1983 |
+
}
|
| 1984 |
+
},
|
| 1985 |
+
{
|
| 1986 |
+
"cell_type": "code",
|
| 1987 |
+
"source": [
|
| 1988 |
+
"# Define training arguments\n",
|
| 1989 |
+
"training_args = TrainingArguments(\n",
|
| 1990 |
+
" output_dir='./gpt2-evy',\n",
|
| 1991 |
+
" overwrite_output_dir=True,\n",
|
| 1992 |
+
" num_train_epochs=20,\n",
|
| 1993 |
+
" per_device_train_batch_size=8,\n",
|
| 1994 |
+
" evaluation_strategy=\"epoch\",\n",
|
| 1995 |
+
" eval_steps=1000,\n",
|
| 1996 |
+
" save_steps=1000,\n",
|
| 1997 |
+
" logging_steps=100,\n",
|
| 1998 |
+
" logging_dir='./logs',\n",
|
| 1999 |
+
" push_to_hub=True,\n",
|
| 2000 |
+
" # resume_from_checkpoint=\"./gpt2-evy/checkpoint-1900\"\n",
|
| 2001 |
+
")\n",
|
| 2002 |
+
"\n",
|
| 2003 |
+
"trainer = Trainer(\n",
|
| 2004 |
+
" model=model,\n",
|
| 2005 |
+
" args=training_args,\n",
|
| 2006 |
+
" train_dataset=train_data,\n",
|
| 2007 |
+
" eval_dataset=val_data,\n",
|
| 2008 |
+
" tokenizer=tokenizer\n",
|
| 2009 |
+
")\n",
|
| 2010 |
+
"\n",
|
| 2011 |
+
"trainer.train()"
|
| 2012 |
+
],
|
| 2013 |
+
"metadata": {
|
| 2014 |
+
"colab": {
|
| 2015 |
+
"base_uri": "https://localhost:8080/",
|
| 2016 |
+
"height": 756
|
| 2017 |
+
},
|
| 2018 |
+
"id": "VQuBHSec_hjO",
|
| 2019 |
+
"outputId": "8d1db759-c959-4181-f258-abf486c22d0b"
|
| 2020 |
+
},
|
| 2021 |
+
"execution_count": 36,
|
| 2022 |
+
"outputs": [
|
| 2023 |
+
{
|
| 2024 |
+
"output_type": "display_data",
|
| 2025 |
+
"data": {
|
| 2026 |
+
"text/plain": [
|
| 2027 |
+
"<IPython.core.display.HTML object>"
|
| 2028 |
+
],
|
| 2029 |
+
"text/html": [
|
| 2030 |
+
"\n",
|
| 2031 |
+
" <div>\n",
|
| 2032 |
+
" \n",
|
| 2033 |
+
" <progress value='140' max='140' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
| 2034 |
+
" [140/140 00:38, Epoch 20/20]\n",
|
| 2035 |
+
" </div>\n",
|
| 2036 |
+
" <table border=\"1\" class=\"dataframe\">\n",
|
| 2037 |
+
" <thead>\n",
|
| 2038 |
+
" <tr style=\"text-align: left;\">\n",
|
| 2039 |
+
" <th>Epoch</th>\n",
|
| 2040 |
+
" <th>Training Loss</th>\n",
|
| 2041 |
+
" <th>Validation Loss</th>\n",
|
| 2042 |
+
" </tr>\n",
|
| 2043 |
+
" </thead>\n",
|
| 2044 |
+
" <tbody>\n",
|
| 2045 |
+
" <tr>\n",
|
| 2046 |
+
" <td>1</td>\n",
|
| 2047 |
+
" <td>No log</td>\n",
|
| 2048 |
+
" <td>1.553022</td>\n",
|
| 2049 |
+
" </tr>\n",
|
| 2050 |
+
" <tr>\n",
|
| 2051 |
+
" <td>2</td>\n",
|
| 2052 |
+
" <td>No log</td>\n",
|
| 2053 |
+
" <td>1.460105</td>\n",
|
| 2054 |
+
" </tr>\n",
|
| 2055 |
+
" <tr>\n",
|
| 2056 |
+
" <td>3</td>\n",
|
| 2057 |
+
" <td>No log</td>\n",
|
| 2058 |
+
" <td>1.395301</td>\n",
|
| 2059 |
+
" </tr>\n",
|
| 2060 |
+
" <tr>\n",
|
| 2061 |
+
" <td>4</td>\n",
|
| 2062 |
+
" <td>No log</td>\n",
|
| 2063 |
+
" <td>1.355699</td>\n",
|
| 2064 |
+
" </tr>\n",
|
| 2065 |
+
" <tr>\n",
|
| 2066 |
+
" <td>5</td>\n",
|
| 2067 |
+
" <td>No log</td>\n",
|
| 2068 |
+
" <td>1.330109</td>\n",
|
| 2069 |
+
" </tr>\n",
|
| 2070 |
+
" <tr>\n",
|
| 2071 |
+
" <td>6</td>\n",
|
| 2072 |
+
" <td>No log</td>\n",
|
| 2073 |
+
" <td>1.311717</td>\n",
|
| 2074 |
+
" </tr>\n",
|
| 2075 |
+
" <tr>\n",
|
| 2076 |
+
" <td>7</td>\n",
|
| 2077 |
+
" <td>No log</td>\n",
|
| 2078 |
+
" <td>1.296761</td>\n",
|
| 2079 |
+
" </tr>\n",
|
| 2080 |
+
" <tr>\n",
|
| 2081 |
+
" <td>8</td>\n",
|
| 2082 |
+
" <td>No log</td>\n",
|
| 2083 |
+
" <td>1.283209</td>\n",
|
| 2084 |
+
" </tr>\n",
|
| 2085 |
+
" <tr>\n",
|
| 2086 |
+
" <td>9</td>\n",
|
| 2087 |
+
" <td>No log</td>\n",
|
| 2088 |
+
" <td>1.276882</td>\n",
|
| 2089 |
+
" </tr>\n",
|
| 2090 |
+
" <tr>\n",
|
| 2091 |
+
" <td>10</td>\n",
|
| 2092 |
+
" <td>No log</td>\n",
|
| 2093 |
+
" <td>1.280680</td>\n",
|
| 2094 |
+
" </tr>\n",
|
| 2095 |
+
" <tr>\n",
|
| 2096 |
+
" <td>11</td>\n",
|
| 2097 |
+
" <td>No log</td>\n",
|
| 2098 |
+
" <td>1.269814</td>\n",
|
| 2099 |
+
" </tr>\n",
|
| 2100 |
+
" <tr>\n",
|
| 2101 |
+
" <td>12</td>\n",
|
| 2102 |
+
" <td>No log</td>\n",
|
| 2103 |
+
" <td>1.270706</td>\n",
|
| 2104 |
+
" </tr>\n",
|
| 2105 |
+
" <tr>\n",
|
| 2106 |
+
" <td>13</td>\n",
|
| 2107 |
+
" <td>No log</td>\n",
|
| 2108 |
+
" <td>1.274694</td>\n",
|
| 2109 |
+
" </tr>\n",
|
| 2110 |
+
" <tr>\n",
|
| 2111 |
+
" <td>14</td>\n",
|
| 2112 |
+
" <td>No log</td>\n",
|
| 2113 |
+
" <td>1.269452</td>\n",
|
| 2114 |
+
" </tr>\n",
|
| 2115 |
+
" <tr>\n",
|
| 2116 |
+
" <td>15</td>\n",
|
| 2117 |
+
" <td>1.055700</td>\n",
|
| 2118 |
+
" <td>1.269921</td>\n",
|
| 2119 |
+
" </tr>\n",
|
| 2120 |
+
" <tr>\n",
|
| 2121 |
+
" <td>16</td>\n",
|
| 2122 |
+
" <td>1.055700</td>\n",
|
| 2123 |
+
" <td>1.265592</td>\n",
|
| 2124 |
+
" </tr>\n",
|
| 2125 |
+
" <tr>\n",
|
| 2126 |
+
" <td>17</td>\n",
|
| 2127 |
+
" <td>1.055700</td>\n",
|
| 2128 |
+
" <td>1.271150</td>\n",
|
| 2129 |
+
" </tr>\n",
|
| 2130 |
+
" <tr>\n",
|
| 2131 |
+
" <td>18</td>\n",
|
| 2132 |
+
" <td>1.055700</td>\n",
|
| 2133 |
+
" <td>1.269600</td>\n",
|
| 2134 |
+
" </tr>\n",
|
| 2135 |
+
" <tr>\n",
|
| 2136 |
+
" <td>19</td>\n",
|
| 2137 |
+
" <td>1.055700</td>\n",
|
| 2138 |
+
" <td>1.269069</td>\n",
|
| 2139 |
+
" </tr>\n",
|
| 2140 |
+
" <tr>\n",
|
| 2141 |
+
" <td>20</td>\n",
|
| 2142 |
+
" <td>1.055700</td>\n",
|
| 2143 |
+
" <td>1.269298</td>\n",
|
| 2144 |
+
" </tr>\n",
|
| 2145 |
+
" </tbody>\n",
|
| 2146 |
+
"</table><p>"
|
| 2147 |
+
]
|
| 2148 |
+
},
|
| 2149 |
+
"metadata": {}
|
| 2150 |
+
},
|
| 2151 |
+
{
|
| 2152 |
+
"output_type": "execute_result",
|
| 2153 |
+
"data": {
|
| 2154 |
+
"text/plain": [
|
| 2155 |
+
"TrainOutput(global_step=140, training_loss=0.9502299581255231, metrics={'train_runtime': 38.6687, 'train_samples_per_second': 27.93, 'train_steps_per_second': 3.621, 'total_flos': 282195394560000.0, 'train_loss': 0.9502299581255231, 'epoch': 20.0})"
|
| 2156 |
+
]
|
| 2157 |
+
},
|
| 2158 |
+
"metadata": {},
|
| 2159 |
+
"execution_count": 36
|
| 2160 |
+
}
|
| 2161 |
+
]
|
| 2162 |
+
},
|
| 2163 |
+
{
|
| 2164 |
+
"cell_type": "markdown",
|
| 2165 |
+
"source": [
|
| 2166 |
+
"# Save Model"
|
| 2167 |
+
],
|
| 2168 |
+
"metadata": {
|
| 2169 |
+
"id": "9RkdIyRfLNkW"
|
| 2170 |
+
}
|
| 2171 |
+
},
|
| 2172 |
+
{
|
| 2173 |
+
"cell_type": "code",
|
| 2174 |
+
"source": [
|
| 2175 |
+
"trainer.save_model()\n",
|
| 2176 |
+
"trainer.push_to_hub()\n",
|
| 2177 |
+
"tokenizer.push_to_hub(\"gpt2-evy\")"
|
| 2178 |
+
],
|
| 2179 |
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