\n",
+ "{'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0]),\n",
+ " 'input_ids': tensor([[ 101, 1037, 4038, 1011, 3689, 1997, 3053, 8680, 19173, 15685,\n",
+ " 1999, 1037, 18006, 2836, 2011, 1996, 2516, 2839, 14996, 3054,\n",
+ " 15509, 5325, 1012, 102, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0],\n",
+ " [ 101, 1037, 2186, 1997, 9686, 17695, 18673, 14313, 1996, 15262,\n",
+ " 3351, 2008, 2054, 2003, 2204, 2005, 1996, 13020, 2003, 2036,\n",
+ " 2204, 2005, 1996, 25957, 4063, 1010, 2070, 1997, 2029, 5681,\n",
+ " 2572, 25581, 2021, 3904, 1997, 2029, 8310, 2000, 2172, 1997,\n",
+ " 1037, 2466, 1012, 102],\n",
+ " [ 101, 2130, 4599, 1997, 19214, 6432, 1005, 1055, 2147, 1010,\n",
+ " 1045, 8343, 1010, 2052, 2031, 1037, 2524, 2051, 3564, 2083,\n",
+ " 2023, 2028, 1012, 102, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0],\n",
+ " [ 101, 1037, 13567, 26162, 5257, 1997, 3802, 7295, 9888, 1998,\n",
+ " 2035, 1996, 20014, 27611, 1010, 14583, 1010, 11703, 20175, 1998,\n",
+ " 4028, 1997, 1037, 8101, 2319, 10576, 2030, 1037, 28900, 7815,\n",
+ " 3850, 1012, 102, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0]]),\n",
+ " 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
+ ]
}
- },
- "outputs": [],
+ ],
"source": [
- "dataloader.batch_sampler"
+ "train_dataloader = prepare_torch_dataloader(train_dataset, collate_fn=collate_fn, shuffle=True)\n",
+ "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, collate_fn=collate_fn, shuffle=True)\n",
+ "\n",
+ "print(type(train_dataloader))\n",
+ "print(type(train_dataloader.collate_fn))\n",
+ "\n",
+ "for batch in train_dataloader:\n",
+ " pprint.pprint(batch, width=1)"
]
},
{
"cell_type": "markdown",
- "id": "51bf0878",
+ "id": "0bd98365",
"metadata": {},
"source": [
- " "
+ "### 2.2 fastNLP 与 datasets 的结合\n",
+ "\n",
+ "从`tutorial-1`至`tutorial-3`，我们已经完成了对`fastNLP v0.8`数据读取、预处理、加载，整个流程的介绍\n",
+ "\n",
+ " 不过在实际使用中，我们往往也会采取更为简便的方法读取数据，例如使用`huggingface`的`datasets`模块\n",
+ "\n",
+ "**使用`datasets`模块中的`load_dataset`函数**，通过指定数据集两级的名称，示例中即是**`GLUE`标准中的`SST-2`数据集**\n",
+ "\n",
+ " 即可以快速从网上下载好`SST-2`数据集读入，之后以`pandas.DataFrame`作为中介，再转化成`fastNLP.DataSet`\n",
+ "\n",
+ " 之后的步骤就和其他关于`dataset`、`databundle`、`vocabulary`、`dataloader`中介绍的相关使用相同了"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "3fd2486f",
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
+ "execution_count": 7,
+ "id": "91879c30",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "639a0ad3c63944c6abef4e8ee1f7bf7c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/3 [00:00\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Processing: 0%| | 0/6000 [00:00[16:20:10] INFO Running evaluator sanity check for 2 batches. trainer.py:596\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m[16:20:10]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches. \u001b]8;id=908530;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=864197;file://../fastNLP/core/controllers/trainer.py#596\u001b\\\u001b[2m596\u001b[0m\u001b]8;;\u001b\\\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " self.msg_id = ip.kernel._parent_header['header']['msg_id']\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " self.msg_id = ip.kernel._parent_header['header']['msg_id']\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:1, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.525,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 84.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.525\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m84.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:2, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.54375,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 87.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.54375\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m87.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:3, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.55,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 88.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.55\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m88.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:4, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.625,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 100.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.625\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m100.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:5, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.65,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 104.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.65\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m104.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:6, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.69375,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 111.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.69375\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m111.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:7, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.675,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 108.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.675\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m108.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:8, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.66875,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 107.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.66875\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m107.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:9, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.675,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 108.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.675\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m108.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "---------------------------- Eval. results on Epoch:10, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.68125,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 109.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.68125\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m109.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "# trainer.run(num_eval_batch_per_dl=10)"
+ "trainer.run(num_eval_batch_per_dl=10)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "8bc4bfb2",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'acc#acc': 0.712222, 'total#acc': 900.0, 'correct#acc': 641.0}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "# trainer.evaluator.run()"
+ "trainer.evaluator.run()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07538876",
+ "metadata": {},
+ "source": [
+ " 注：此处使用`gc`模块删除相关变量，释放内存，为接下来新的模型训练预留存储空间，下同"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "1b52eafd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "383"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import gc\n",
+ "\n",
+ "del model\n",
+ "del trainer\n",
+ "del dataset\n",
+ "del sst2data\n",
+ "\n",
+ "gc.collect()"
]
},
{
@@ -255,6 +1050,8 @@
"\n",
" 本示例使用`fastNLP 0.8`中预定义模型`models`中的`CNNText`模型，实现`SST-2`文本二分类任务\n",
"\n",
+ "数据使用方面，此处沿用在上个示例中展示的`SST-2`数据集，数据加载过程相同且已经执行过了，因此简略\n",
+ "\n",
"模型使用方面，如上所述，这里使用**基于卷积神经网络`CNN`的预定义文本分类模型`CNNText`**，结构如下所示\n",
"\n",
" 首先是内置的`100`维嵌入层、`dropout`层、紧接着是三个一维卷积，将`100`维嵌入特征，分别通过\n",
@@ -281,95 +1078,7 @@
")\n",
"```\n",
"\n",
- "数据使用方面，此处**使用`datasets`模块中的`load_dataset`函数**，以如下形式，指定`SST-2`数据集自动加载\n",
- "\n",
- " 首次下载后会保存至`~/.cache/huggingface/modules/datasets_modules/datasets/glue/`目录下"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1aa5cf6d",
- "metadata": {},
- "outputs": [],
- "source": [
- "from datasets import load_dataset\n",
- "\n",
- "sst2data = load_dataset('glue', 'sst2')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c476abe7",
- "metadata": {},
- "source": [
- "紧接着，使用`tutorial-1`和`tutorial-2`中的知识，将数据集转化为`fastNLP`中的`DataSet`格式\n",
- "\n",
- " **使用`apply_more`函数、`Vocabulary`模块的`from_/index_dataset`函数预处理数据**\n",
- "\n",
- " 并结合`delete_field`函数删除字段调整格式，`split`函数划分测试集和验证集\n",
- "\n",
- " **仅保留`'words'`字段表示输入文本单词序号序列、`'target'`字段表示文本对应预测输出结果**\n",
- "\n",
- " 两者**对应到`CNNText`中`train_step`函数和`evaluate_step`函数的签名/输入参数**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "357ea748",
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "sys.path.append('..')\n",
- "\n",
- "from fastNLP import DataSet\n",
- "\n",
- "dataset = DataSet.from_pandas(sst2data['train'].to_pandas())[:6000]\n",
- "\n",
- "dataset.apply_more(lambda ins:{'words': ins['sentence'].lower().split(), 'target': ins['label']}, \n",
- " progress_bar=\"tqdm\")\n",
- "dataset.delete_field('sentence')\n",
- "dataset.delete_field('label')\n",
- "dataset.delete_field('idx')\n",
- "\n",
- "from fastNLP import Vocabulary\n",
- "\n",
- "vocab = Vocabulary()\n",
- "vocab.from_dataset(dataset, field_name='words')\n",
- "vocab.index_dataset(dataset, field_name='words')\n",
- "\n",
- "train_dataset, evaluate_dataset = dataset.split(ratio=0.85)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "96380c67",
- "metadata": {},
- "source": [
- "然后，使用`tutorial-3`中的知识，**通过`prepare_torch_dataloader`处理数据集得到`dataloader`**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b9dd1273",
- "metadata": {},
- "outputs": [],
- "source": [
- "from fastNLP import prepare_torch_dataloader\n",
- "\n",
- "train_dataloader = prepare_torch_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
- "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "96941b63",
- "metadata": {},
- "source": [
- "接着，**从`fastNLP.models.torch`路径下导入`CNNText`**，初始化`CNNText`实例以及`optimizer`实例\n",
+ "对应到代码上，**从`fastNLP.models.torch`路径下导入`CNNText`**，初始化`CNNText`和`optimizer`实例\n",
"\n",
" 注意：初始化`CNNText`时，**二元组参数`embed`、分类数量`num_classes`是必须传入的**，其中\n",
"\n",
@@ -378,7 +1087,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "f6e76e2e",
"metadata": {},
"outputs": [],
@@ -397,12 +1106,12 @@
"id": "0cc5ca10",
"metadata": {},
"source": [
- "最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练"
+ " 最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "50a13ee5",
"metadata": {},
"outputs": [],
@@ -423,45 +1132,634 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "28903a7d",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "[16:21:57] INFO Running evaluator sanity check for 2 batches. trainer.py:596\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m[16:21:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches. \u001b]8;id=813103;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=271516;file://../fastNLP/core/controllers/trainer.py#596\u001b\\\u001b[2m596\u001b[0m\u001b]8;;\u001b\\\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:1, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.654444,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 589.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.654444\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m589.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:2, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.767778,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 691.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.767778\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m691.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:3, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.797778,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 718.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.797778\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m718.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:4, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.803333,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 723.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.803333\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m723.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:5, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.807778,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 727.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.807778\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m727.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:6, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.812222,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 731.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.812222\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m731.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:7, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.804444,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 724.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.804444\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m724.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:8, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.811111,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 730.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.811111\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m730.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:9, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.811111,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 730.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.811111\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m730.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "---------------------------- Eval. results on Epoch:10, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.806667,\n",
+ " \"total#acc\": 900.0,\n",
+ " \"correct#acc\": 726.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.806667\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m900.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m726.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"trainer.run()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "f47a6a35",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'acc#acc': 0.806667, 'total#acc': 900.0, 'correct#acc': 726.0}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"trainer.evaluator.run()"
]
},
{
"cell_type": "markdown",
- "id": "7c811257",
+ "id": "5b5c0446",
"metadata": {},
"source": [
- " 注：此处使用`gc`模块删除相关变量，释放内存，为接下来新的模型训练预留存储空间"
+ " 注：此处使用`gc`模块删除相关变量，释放内存，为接下来新的模型训练预留存储空间，下同"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "c1a2e2ca",
+ "execution_count": 14,
+ "id": "e9e70f88",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "344"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import gc\n",
"\n",
"del model\n",
"del trainer\n",
- "del dataset\n",
- "del sst2data\n",
"\n",
"gc.collect()"
]
@@ -502,10 +1800,32 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "03e66686",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset conll2003 (/remote-home/xrliu/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/63f4ebd1bcb7148b1644497336fd74643d4ce70123334431a3c053b7ee4e96ee)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "593bc03ed5914953ab94268ff2f01710",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/3 [00:00[16:23:41] INFO Running evaluator sanity check for 2 batches. trainer.py:596\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m[16:23:41]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches. \u001b]8;id=565652;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=224849;file://../fastNLP/core/controllers/trainer.py#596\u001b\\\u001b[2m596\u001b[0m\u001b]8;;\u001b\\\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:1, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.169014,\n",
+ " \"pre#F1\": 0.170732,\n",
+ " \"rec#F1\": 0.167331\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.169014\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.170732\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.167331\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:2, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.361809,\n",
+ " \"pre#F1\": 0.312139,\n",
+ " \"rec#F1\": 0.430279\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.361809\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.312139\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.430279\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:3, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.525,\n",
+ " \"pre#F1\": 0.475728,\n",
+ " \"rec#F1\": 0.585657\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.525\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.475728\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.585657\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:4, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.627306,\n",
+ " \"pre#F1\": 0.584192,\n",
+ " \"rec#F1\": 0.677291\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.627306\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.584192\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.677291\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:5, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.710937,\n",
+ " \"pre#F1\": 0.697318,\n",
+ " \"rec#F1\": 0.7251\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.710937\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.697318\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.7251\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:6, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.739563,\n",
+ " \"pre#F1\": 0.738095,\n",
+ " \"rec#F1\": 0.741036\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.739563\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.738095\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.741036\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:7, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.748491,\n",
+ " \"pre#F1\": 0.756098,\n",
+ " \"rec#F1\": 0.741036\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.748491\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.756098\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.741036\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:8, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.716763,\n",
+ " \"pre#F1\": 0.69403,\n",
+ " \"rec#F1\": 0.741036\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.716763\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.69403\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.741036\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:9, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.768293,\n",
+ " \"pre#F1\": 0.784232,\n",
+ " \"rec#F1\": 0.752988\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.768293\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.784232\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.752988\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "---------------------------- Eval. results on Epoch:10, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"f#F1\": 0.757692,\n",
+ " \"pre#F1\": 0.732342,\n",
+ " \"rec#F1\": 0.784861\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"f#F1\"\u001b[0m: \u001b[1;36m0.757692\u001b[0m,\n",
+ " \u001b[1;34m\"pre#F1\"\u001b[0m: \u001b[1;36m0.732342\u001b[0m,\n",
+ " \u001b[1;34m\"rec#F1\"\u001b[0m: \u001b[1;36m0.784861\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"trainer.run(num_eval_batch_per_dl=10)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"id": "37871d6b",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'f#F1': 0.766798, 'pre#F1': 0.741874, 'rec#F1': 0.793456}"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"trainer.evaluator.run()"
]
diff --git a/tutorials/fastnlp_tutorial_5.ipynb b/tutorials/fastnlp_tutorial_5.ipynb
index 63410113..3f2bbfa6 100644
--- a/tutorials/fastnlp_tutorial_5.ipynb
+++ b/tutorials/fastnlp_tutorial_5.ipynb
@@ -296,7 +296,7 @@
"\n",
" 在`fastNLP v0.8`中，`update`函数的默认输入参数：`pred`，对应预测值；`target`，对应真实值\n",
"\n",
- " 此处刻意调整为：`pred`，对应预测值，和模型输出一致；`true`，对应真实值，数据集字段需要调整\n",
+ " 此处仍然沿用，因为接下来会需要使用`fastNLP`函数的与定义模型，其输入参数格式即使如此\n",
"\n",
" 在`get_metric`函数中，根据需求定义评测指标最终的计算，此处直接计算准确率，该函数必须返回一个字典\n",
"\n",
@@ -307,10 +307,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "08a872e9",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import sys\n",
"sys.path.append('..')\n",
@@ -320,16 +334,16 @@
"class MyMetric(Metric):\n",
"\n",
" def __init__(self):\n",
- " MyMetric.__init__(self)\n",
+ " Metric.__init__(self)\n",
" self.total_num = 0\n",
" self.right_num = 0\n",
"\n",
- " def update(self, pred, true):\n",
+ " def update(self, pred, target):\n",
" self.total_num += target.size(0)\n",
" self.right_num += target.eq(pred).sum().item()\n",
"\n",
" def get_metric(self, reset=True):\n",
- " acc = self.acc_count / self.total_num\n",
+ " acc = self.right_num / self.total_num\n",
" if reset:\n",
" self.total_num = 0\n",
" self.right_num = 0\n",
@@ -346,14 +360,36 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "5ad81ac7",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ef923b90b19847f4916cccda5d33fc36",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/3 [00:00名称 | 参数
| 属性
| 功能
| 内容
|\n",
"|:--|:--:|:--:|:--|:--|\n",
"| **`model`** | √ | √ | 指定`trainer`控制的模型 | 视框架而定，如`torch.nn.Module` |\n",
- "| **`driver`** | √ | | 指定`trainer`驱动的框架 | 包括`'torch'`、`'paddle'`、`'jittor'` |\n",
- "| | | √ | 记录`trainer`驱动的框架 | `Driver`类型，在初始化阶段生成 |\n",
"| `device` | √ | | 指定`trainer`运行的卡位 | 例如`'cpu'`、`'cuda'`、`0`、`[0, 1]`等 |\n",
"| | | √ | 记录`trainer`运行的卡位 | `Device`类型，在初始化阶段生成 |\n",
+ "| **`driver`** | √ | | 指定`trainer`驱动的框架 | 包括`'torch'`、`'paddle'`、`'jittor'` |\n",
+ "| | | √ | 记录`trainer`驱动的框架 | `Driver`类型，在初始化阶段生成 |\n",
"| `n_epochs` | √ | - | 指定`trainer`迭代的轮数 | 默认`20`，记录在`driver.n_epochs`中 |\n",
"| **`optimizers`** | √ | √ | 指定`trainer`优化的方法 | 视框架而定，如`torch.optim.Adam` |\n",
"| `metrics` | √ | √ | 指定`trainer`评测的方法 | 字典类型，如`{'acc': Metric()}` |\n",
@@ -473,12 +522,34 @@
"| `fp16` | √ | √ | 指定`trainer`是否进行混合精度训练 | 布尔类型，默认`False` |"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "9e13ee08",
+ "metadata": {},
+ "source": [
+ "其中，**`input_mapping`和`output_mapping`** 定义形式如下：输入字典形式的数据，根据参数匹配要求\n",
+ "\n",
+ " 调整数据格式，这里就回应了前文未在数据集预处理时调整格式的问题，**总之参数匹配一定要求**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "de96c1d1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def input_mapping(data):\n",
+ " data['target'] = data['label']\n",
+ " return data"
+ ]
+ },
{
"cell_type": "markdown",
"id": "2fc8b9f3",
"metadata": {},
"source": [
- " 以及`trainer`模块内部的基础方法，相关进阶操作，如“`on`系列函数”、`callback`控制，请参考后续的`tutorial-7`\n",
+ " 而`trainer`模块的基础方法列表如下，相关进阶操作，如“`on`系列函数”、`callback`控制，请参考后续的`tutorial-7`\n",
"\n",
"| 名称
|功能
| 主要参数
|\n",
"|:--|:--|:--|\n",
@@ -539,7 +610,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "926a9c50",
"metadata": {},
"outputs": [],
@@ -552,6 +623,7 @@
" device=0, # 'cuda'\n",
" n_epochs=10,\n",
" optimizers=optimizers,\n",
+ " input_mapping=input_mapping,\n",
" train_dataloader=train_dataloader,\n",
" evaluate_dataloaders=evaluate_dataloader,\n",
" metrics={'suffix': MyMetric()}\n",
@@ -580,14 +652,557 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "43be274f",
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "[09:30:35] INFO Running evaluator sanity check for 2 batches. trainer.py:596\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m[09:30:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches. \u001b]8;id=954293;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=366534;file://../fastNLP/core/controllers/trainer.py#596\u001b\\\u001b[2m596\u001b[0m\u001b]8;;\u001b\\\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " self.msg_id = ip.kernel._parent_header['header']['msg_id']\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " self.msg_id = ip.kernel._parent_header['header']['msg_id']\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:1, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.6875\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.6875\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:2, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.8125\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.8125\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:3, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.80625\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.80625\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:4, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.825\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.825\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:5, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.8125\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.8125\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:6, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.80625\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.80625\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:7, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.80625\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.80625\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:8, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.8\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.8\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:9, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.80625\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.80625\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "---------------------------- Eval. results on Epoch:10, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"prefix#suffix\": 0.80625\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"prefix#suffix\"\u001b[0m: \u001b[1;36m0.80625\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"trainer.run(num_eval_batch_per_dl=10)"
]
diff --git a/tutorials/fastnlp_tutorial_6.ipynb b/tutorials/fastnlp_tutorial_6.ipynb
index 552f73d9..63f7481e 100644
--- a/tutorials/fastnlp_tutorial_6.ipynb
+++ b/tutorials/fastnlp_tutorial_6.ipynb
@@ -19,15 +19,37 @@
"\n",
" 2.2 使用 jittor 搭建并训练模型\n",
"\n",
- " 3 fastNLP 实现 paddle 与 pytorch 互转"
+ ""
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "08752c5a",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6b13d42c39ba455eb370bf2caaa3a264",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " 0%| | 0/3 [00:00\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Processing: 0%| | 0/6000 [00:00 True\n"
+ ]
+ }
+ ],
"source": [
"import sys\n",
"sys.path.append('..')\n",
@@ -74,72 +138,108 @@
"metadata": {},
"source": [
"## 1. fastNLP 结合 paddle 训练模型\n",
- "\n",
- "```python\n",
- "import paddle\n",
- "\n",
- "lstm = paddle.nn.LSTM(16, 32, 2)\n",
- "\n",
- "x = paddle.randn((4, 23, 16))\n",
- "h = paddle.randn((2, 4, 32))\n",
- "c = paddle.randn((2, 4, 32))\n",
- "\n",
- "y, (h, c) = lstm(x, (h, c))\n",
- "\n",
- "print(y.shape) # [4, 23, 32]\n",
- "print(h.shape) # [2, 4, 32]\n",
- "print(c.shape) # [2, 4, 32]\n",
- "```"
+ "\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "e31b3198",
"metadata": {},
"outputs": [],
"source": [
"import paddle\n",
"import paddle.nn as nn\n",
+ "import paddle.nn.functional as F\n",
"\n",
"\n",
"class ClsByPaddle(nn.Layer):\n",
- " def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5):\n",
+ " def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, dropout=0.5):\n",
" nn.Layer.__init__(self)\n",
" self.hidden_dim = hidden_dim\n",
"\n",
" self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim)\n",
- " # self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_dim, \n",
- " # num_layers=num_layers, direction='bidirectional', dropout=dropout)\n",
- " self.mlp = nn.Sequential(('linear_1', nn.Linear(hidden_dim * 2, hidden_dim * 2)),\n",
+ " \n",
+ " self.conv1 = nn.Sequential(nn.Conv1D(embedding_dim, 30, 1, padding=0), nn.ReLU())\n",
+ " self.conv2 = nn.Sequential(nn.Conv1D(embedding_dim, 40, 3, padding=1), nn.ReLU())\n",
+ " self.conv3 = nn.Sequential(nn.Conv1D(embedding_dim, 50, 5, padding=2), nn.ReLU())\n",
+ "\n",
+ " self.mlp = nn.Sequential(('dropout', nn.Dropout(p=dropout)),\n",
+ " ('linear_1', nn.Linear(120, hidden_dim)),\n",
" ('activate', nn.ReLU()),\n",
- " ('linear_2', nn.Linear(hidden_dim * 2, output_dim)))\n",
+ " ('linear_2', nn.Linear(hidden_dim, output_dim)))\n",
" \n",
- " self.loss_fn = nn.CrossEntropyLoss()\n",
+ " self.loss_fn = nn.MSELoss()\n",
"\n",
" def forward(self, words):\n",
- " output = self.embedding(words)\n",
- " # output, (hidden, cell) = self.lstm(output)\n",
- " hidden = paddle.randn((2, words.shape[0], self.hidden_dim))\n",
- " output = self.mlp(paddle.concat((hidden[-1], hidden[-2]), axis=1))\n",
+ " output = self.embedding(words).transpose([0, 2, 1])\n",
+ " conv1, conv2, conv3 = self.conv1(output), self.conv2(output), self.conv3(output)\n",
+ "\n",
+ " pool1 = F.max_pool1d(conv1, conv1.shape[-1]).squeeze(2)\n",
+ " pool2 = F.max_pool1d(conv2, conv2.shape[-1]).squeeze(2)\n",
+ " pool3 = F.max_pool1d(conv3, conv3.shape[-1]).squeeze(2)\n",
+ "\n",
+ " pool = paddle.concat([pool1, pool2, pool3], axis=1)\n",
+ " output = self.mlp(pool)\n",
" return output\n",
" \n",
" def train_step(self, words, target):\n",
" pred = self(words)\n",
- " return {\"loss\": self.loss_fn(pred, target)}\n",
+ " target = paddle.stack((1 - target, target), axis=1).cast(pred.dtype)\n",
+ " return {'loss': self.loss_fn(pred, target)}\n",
"\n",
" def evaluate_step(self, words, target):\n",
" pred = self(words)\n",
- " pred = paddle.max(pred, axis=-1)[1]\n",
- " return {\"pred\": pred, \"target\": target}"
+ " pred = paddle.argmax(pred, axis=-1)\n",
+ " return {'pred': pred, 'target': target}"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "c63b030f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "W0604 21:02:25.453869 19014 gpu_context.cc:278] Please NOTE: device: 0, GPU Compute Capability: 6.1, Driver API Version: 11.1, Runtime API Version: 10.2\n",
+ "W0604 21:02:26.061690 19014 gpu_context.cc:306] device: 0, cuDNN Version: 7.6.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "ClsByPaddle(\n",
+ " (embedding): Embedding(8458, 100, sparse=False)\n",
+ " (conv1): Sequential(\n",
+ " (0): Conv1D(100, 30, kernel_size=[1], data_format=NCL)\n",
+ " (1): ReLU()\n",
+ " )\n",
+ " (conv2): Sequential(\n",
+ " (0): Conv1D(100, 40, kernel_size=[3], padding=1, data_format=NCL)\n",
+ " (1): ReLU()\n",
+ " )\n",
+ " (conv3): Sequential(\n",
+ " (0): Conv1D(100, 50, kernel_size=[5], padding=2, data_format=NCL)\n",
+ " (1): ReLU()\n",
+ " )\n",
+ " (mlp): Sequential(\n",
+ " (dropout): Dropout(p=0.5, axis=None, mode=upscale_in_train)\n",
+ " (linear_1): Linear(in_features=120, out_features=64, dtype=float32)\n",
+ " (activate): ReLU()\n",
+ " (linear_2): Linear(in_features=64, out_features=2, dtype=float32)\n",
+ " )\n",
+ " (loss_fn): MSELoss()\n",
+ ")"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"model = ClsByPaddle(vocab_size=len(vocab), embedding_dim=100, output_dim=2)\n",
"\n",
@@ -148,34 +248,34 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "2997c0aa",
"metadata": {},
"outputs": [],
"source": [
"from paddle.optimizer import AdamW\n",
"\n",
- "optimizers = AdamW(parameters=model.parameters(), learning_rate=1e-2)"
+ "optimizers = AdamW(parameters=model.parameters(), learning_rate=5e-4)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "ead35fb8",
"metadata": {},
"outputs": [],
"source": [
"from fastNLP import prepare_paddle_dataloader\n",
"\n",
- "# train_dataloader = prepare_paddle_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
- "# evaluate_dataloader = prepare_paddle_dataloader(evaluate_dataset, batch_size=16)\n",
+ "train_dataloader = prepare_paddle_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
+ "evaluate_dataloader = prepare_paddle_dataloader(evaluate_dataset, batch_size=16)\n",
"\n",
- "dl_bundle = prepare_paddle_dataloader(data_bundle, batch_size=16, shuffle=True)"
+ "# dl_bundle = prepare_paddle_dataloader(data_bundle, batch_size=16, shuffle=True)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "25e8da83",
"metadata": {},
"outputs": [],
@@ -185,23 +285,631 @@
"trainer = Trainer(\n",
" model=model,\n",
" driver='paddle',\n",
- " device='gpu', # 'cpu', 'gpu', 'gpu:x'\n",
+ " device='gpu', # 'cpu', 'gpu', 'gpu:x'\n",
" n_epochs=10,\n",
" optimizers=optimizers,\n",
- " train_dataloader=dl_bundle['train'], # train_dataloader,\n",
- " evaluate_dataloaders=dl_bundle['dev'], # evaluate_dataloader,\n",
+ " train_dataloader=train_dataloader, # dl_bundle['train'],\n",
+ " evaluate_dataloaders=evaluate_dataloader, # dl_bundle['dev'], \n",
" metrics={'acc': Accuracy()}\n",
")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "d63c5d74",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "[21:03:08] INFO Running evaluator sanity check for 2 batches. trainer.py:596\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m[21:03:08]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches. \u001b]8;id=894986;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=567751;file://../fastNLP/core/controllers/trainer.py#596\u001b\\\u001b[2m596\u001b[0m\u001b]8;;\u001b\\\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " self.msg_id = ip.kernel._parent_header['header']['msg_id']\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+ "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+ ".get_parent()\n",
+ " self.msg_id = ip.kernel._parent_header['header']['msg_id']\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/paddle/tensor/creation.py:\n",
+ "125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To \n",
+ "silence this warning, use `object` by itself. Doing this will not modify any behavior and is \n",
+ "safe. \n",
+ "Deprecated in NumPy 1.20; for more details and guidance: \n",
+ "https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ " if data.dtype == np.object:\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/paddle/tensor/creation.py:\n",
+ "125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To \n",
+ "silence this warning, use `object` by itself. Doing this will not modify any behavior and is \n",
+ "safe. \n",
+ "Deprecated in NumPy 1.20; for more details and guidance: \n",
+ "https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+ " if data.dtype == np.object:\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:1, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.78125,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 125.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.78125\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m125.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:2, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.7875,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 126.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.7875\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m126.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:3, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.8,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 128.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.8\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m128.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:4, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.79375,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 127.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.79375\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m127.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:5, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.81875,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 131.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.81875\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m131.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:6, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.8,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 128.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.8\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m128.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:7, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.80625,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 129.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.80625\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m129.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:8, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.79375,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 127.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.79375\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m127.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:9, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.7875,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 126.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.7875\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m126.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "---------------------------- Eval. results on Epoch:10, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.8,\n",
+ " \"total#acc\": 160.0,\n",
+ " \"correct#acc\": 128.0\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.8\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160.0\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m128.0\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "trainer.run(num_eval_batch_per_dl=10) # 然后卡了？"
+ "trainer.run(num_eval_batch_per_dl=10) "
]
},
{
@@ -214,7 +922,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "c600191d",
"metadata": {},
"outputs": [],
@@ -231,37 +939,61 @@
" self.hidden_dim = hidden_dim\n",
"\n",
" self.embedding = nn.Embedding(num=vocab_size, dim=embedding_dim)\n",
- " self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_dim, \n",
+ " self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_dim, batch_first=True, # 默认 batch_first=False\n",
" num_layers=num_layers, bidirectional=True, dropout=dropout)\n",
- " self.mlp = nn.Sequential([nn.Linear(hidden_dim * 2, hidden_dim * 2),\n",
+ " self.mlp = nn.Sequential([nn.Dropout(p=dropout),\n",
+ " nn.Linear(hidden_dim * 2, hidden_dim * 2),\n",
" nn.ReLU(),\n",
- " nn.Linear(hidden_dim * 2, output_dim)])\n",
+ " nn.Linear(hidden_dim * 2, output_dim),\n",
+ " nn.Sigmoid(),])\n",
"\n",
- " self.loss_fn = nn.BCELoss()\n",
+ " self.loss_fn = nn.MSELoss()\n",
"\n",
" def execute(self, words):\n",
" output = self.embedding(words)\n",
" output, (hidden, cell) = self.lstm(output)\n",
- " # hidden = jittor.randn((2, words.shape[0], self.hidden_dim))\n",
- " output = self.mlp(jittor.concat((hidden[-1], hidden[-2]), axis=1))\n",
+ " output = self.mlp(jittor.concat((hidden[-1], hidden[-2]), dim=1))\n",
" return output\n",
" \n",
" def train_step(self, words, target):\n",
" pred = self(words)\n",
- " return {\"loss\": self.loss_fn(pred, target)}\n",
+ " target = jittor.stack((1 - target, target), dim=1)\n",
+ " return {'loss': self.loss_fn(pred, target)}\n",
"\n",
" def evaluate_step(self, words, target):\n",
" pred = self(words)\n",
- " pred = jittor.max(pred, axis=-1)[1]\n",
- " return {\"pred\": pred, \"target\": target}"
+ " pred = jittor.argmax(pred, dim=-1)[0]\n",
+ " return {'pred': pred, 'target': target}"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "a94ed8c4",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ClsByJittor(\n",
+ " embedding: Embedding(8458, 100)\n",
+ " lstm: LSTM(100, 64, 2, bias=True, batch_first=True, dropout=0.5, bidirectional=True, proj_size=0)\n",
+ " mlp: Sequential(\n",
+ " 0: Dropout(0.5, is_train=False)\n",
+ " 1: Linear(128, 128, float32[128,], None)\n",
+ " 2: relu()\n",
+ " 3: Linear(128, 2, float32[2,], None)\n",
+ " 4: Sigmoid()\n",
+ " )\n",
+ " loss_fn: MSELoss(mean)\n",
+ ")"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"model = ClsByJittor(vocab_size=len(vocab), embedding_dim=100, output_dim=2)\n",
"\n",
@@ -270,34 +1002,34 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "6d15ebc1",
"metadata": {},
"outputs": [],
"source": [
"from jittor.optim import AdamW\n",
"\n",
- "optimizers = AdamW(params=model.parameters(), lr=1e-2)"
+ "optimizers = AdamW(params=model.parameters(), lr=5e-3)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "95d8d09e",
"metadata": {},
"outputs": [],
"source": [
"from fastNLP import prepare_jittor_dataloader\n",
"\n",
- "# train_dataloader = prepare_jittor_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
- "# evaluate_dataloader = prepare_jittor_dataloader(evaluate_dataset, batch_size=16)\n",
+ "train_dataloader = prepare_jittor_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
+ "evaluate_dataloader = prepare_jittor_dataloader(evaluate_dataset, batch_size=16)\n",
"\n",
- "dl_bundle = prepare_jittor_dataloader(data_bundle, batch_size=16, shuffle=True)"
+ "# dl_bundle = prepare_jittor_dataloader(data_bundle, batch_size=16, shuffle=True)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "917eab81",
"metadata": {},
"outputs": [],
@@ -307,24 +1039,587 @@
"trainer = Trainer(\n",
" model=model,\n",
" driver='jittor',\n",
- " device='gpu', # 'cpu', 'gpu', 'cuda'\n",
+ " device='gpu', # 'cpu', 'gpu', 'cuda'\n",
" n_epochs=10,\n",
" optimizers=optimizers,\n",
- " train_dataloader=dl_bundle['train'], # train_dataloader,\n",
- " evaluate_dataloaders=dl_bundle['dev'], # evaluate_dataloader,\n",
+ " train_dataloader=train_dataloader, # dl_bundle['train'],\n",
+ " evaluate_dataloaders=evaluate_dataloader, # dl_bundle['dev'],\n",
" metrics={'acc': Accuracy()}\n",
")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "f7c4ac5a",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "[21:05:51] INFO Running evaluator sanity check for 2 batches. trainer.py:596\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[2;36m[21:05:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches. \u001b]8;id=69759;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=202322;file://../fastNLP/core/controllers/trainer.py#596\u001b\\\u001b[2m596\u001b[0m\u001b]8;;\u001b\\\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Compiling Operators(5/6) used: 8.31s eta: 1.66s 6/6) used: 9.33s eta: 0s \n",
+ "\n",
+ "Compiling Operators(31/31) used: 7.31s eta: 0s \n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:1, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.61875,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 99\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.61875\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m99\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:2, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.7,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 112\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.7\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m112\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:3, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.725,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 116\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.725\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m116\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:4, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.74375,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 119\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.74375\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m119\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:5, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.75625,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 121\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.75625\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m121\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:6, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.75625,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 121\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.75625\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m121\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:7, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.73125,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 117\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.73125\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m117\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:8, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.7625,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 122\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.7625\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m122\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "----------------------------- Eval. results on Epoch:9, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.74375,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 119\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.74375\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m119\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "---------------------------- Eval. results on Epoch:10, Batch:0 -----------------------------\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "{\n",
+ " \"acc#acc\": 0.7625,\n",
+ " \"total#acc\": 160,\n",
+ " \"correct#acc\": 122\n",
+ "}\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.7625\u001b[0m,\n",
+ " \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m160\u001b[0m,\n",
+ " \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m122\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": []
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"trainer.run(num_eval_batch_per_dl=10)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3df5f425",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/tutorials/fastnlp_tutorial_7.ipynb b/tutorials/fastnlp_tutorial_7.ipynb
deleted file mode 100644
index 0a7d6922..00000000
--- a/tutorials/fastnlp_tutorial_7.ipynb
+++ /dev/null
@@ -1,59 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "fdd7ff16",
- "metadata": {},
- "source": [
- "# T7. callback 自定义训练过程\n",
- "\n",
- " 1 \n",
- " \n",
- " 1.1 \n",
- "\n",
- " 1.2 \n",
- "\n",
- " 2 \n",
- "\n",
- " 2.1 \n",
- "\n",
- " 2.2 \n",
- "\n",
- " 3 \n",
- "\n",
- " 3.1 \n",
- "\n",
- " 3.2 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "08752c5a",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/tutorials/fastnlp_tutorial_8.ipynb b/tutorials/fastnlp_tutorial_8.ipynb
deleted file mode 100644
index 0664bc41..00000000
--- a/tutorials/fastnlp_tutorial_8.ipynb
+++ /dev/null
@@ -1,59 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "fdd7ff16",
- "metadata": {},
- "source": [
- "# T8. fastNLP 中的文件读取模块\n",
- "\n",
- " 1 fastNLP 中的 EmbedLoader 模块\n",
- " \n",
- " 1.1 \n",
- "\n",
- " 1.2 \n",
- "\n",
- " 2 fastNLP 中的 Loader 模块\n",
- "\n",
- " 2.1 \n",
- "\n",
- " 2.2 \n",
- "\n",
- " 3 fastNLP 中的 Pipe 模块\n",
- "\n",
- " 3.1 \n",
- "\n",
- " 3.2 "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "08752c5a",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}