From 2732da27175cc9df32f842adfa92021b862c827e Mon Sep 17 00:00:00 2001
From: Hong Lu <honglu@microsoft.com>
Date: Thu, 16 May 2019 18:09:40 -0400
Subject: [PATCH] Updated NER notebook with new BertTokenClassifier class.

---
 .../NER_bert-demo-new-updated.ipynb           | 7072 ++++++++++-------
 1 file changed, 4255 insertions(+), 2817 deletions(-)

diff --git a/examples/named_entity_recognition/NER_bert-demo-new-updated.ipynb b/examples/named_entity_recognition/NER_bert-demo-new-updated.ipynb
index 83d5678..1fc74fc 100644
--- a/examples/named_entity_recognition/NER_bert-demo-new-updated.ipynb
+++ b/examples/named_entity_recognition/NER_bert-demo-new-updated.ipynb
@@ -14,13 +14,12 @@
     "### Required packages\n",
     "* pytorch-pretrained-bert\n",
     "* pandas\n",
-    "* seqeval\n",
-    "* unicode"
+    "* seqeval"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 22,
    "metadata": {
     "scrolled": false
    },
@@ -28,11 +27,14 @@
    "source": [
     "import sys\n",
     "import os\n",
+    "import yaml\n",
+    "import random\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "from tqdm import tqdm, trange\n",
+    "from seqeval.metrics import f1_score\n",
+    "\n",
     "import torch\n",
-    "import random\n",
     "\n",
     "from pytorch_pretrained_bert.tokenization import BertTokenizer\n",
     "from torch.optim import Adam\n",
@@ -41,21 +43,21 @@
     "if bert_utils_path not in sys.path:\n",
     "    sys.path.insert(0, bert_utils_path)\n",
     "\n",
-    "from configs import (PathConfig,\n",
-    "                     GlobalConfig, \n",
-    "                     DeviceConfig, \n",
-    "                     ModelConfig, \n",
-    "                     OptimizerConfig, \n",
-    "                     TrainConfig, \n",
-    "                     EvalConfig)\n",
+    "from configs import BERTFineTuneConfig\n",
     "from bert_data_utils import KaggleNERProcessor\n",
-    "from bert_utils import (convert_examples_to_token_features,\n",
-    "                        create_train_dataloader, \n",
-    "                        create_eval_dataloader, \n",
-    "                        load_model, \n",
-    "                        get_optimizer_params, \n",
-    "                        train_model, \n",
-    "                        eval_token_model)"
+    "from bert_utils import (BertTokenClassifier, \n",
+    "                        convert_examples_to_token_features, \n",
+    "                        print_dict, \n",
+    "                        get_device)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_file = \"config.yaml\""
    ]
   },
   {
@@ -65,185 +67,35 @@
     "## Configurations"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Path configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "path_config_dict = {\"data_dir\": \"./data/NER/\", \n",
-    "                    \"output_dir\": \"./NER_output/\"}\n",
-    "path_config = PathConfig(path_config_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Global configuration"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "global_config_dict = {\"fp16\": False}\n",
-    "global_config = GlobalConfig(global_config_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "### Device configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": false
-   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "device name: Tesla K80\n",
-      "number of gpus: 1\n"
+      "{TrainConfig: {'batch_size': 32, 'num_train_epochs': 3}\n",
+      "ModelConfig: {'bert_model': 'bert-base-uncased', 'max_seq_length': 75, 'do_lower_case': True}\n",
+      "OptimizerConfig: {'optimizer_name': 'BertAdam', 'learning_rate': 3e-05, 'no_decay_params': ['bias', 'gamma', 'beta'], 'params_weight_decay': 0.01, 'clip_gradient': True, 'max_gradient_norm': 1.0}}\n"
      ]
     }
    ],
    "source": [
-    "device_config_dict = {\"no_cuda\": False}\n",
-    "device_config = DeviceConfig(device_config_dict)\n",
-    "print(\"device name: {}\".format(torch.cuda.get_device_name(0)))\n",
-    "print(\"number of gpus: {}\".format(device_config.n_gpu))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model configuration"
+    "with open(config_file, 'r') as ymlfile:\n",
+    "    config_dict = yaml.safe_load(ymlfile)\n",
+    "\n",
+    "print_dict(config_dict)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "model_config_dict = {\"bert_model\": \"bert-base-uncased\",\n",
-    "                     \"max_seq_length\": 75,\n",
-    "                     \"num_labels\": 18,\n",
-    "                     \"model_type\": \"token\"}\n",
-    "model_config = ModelConfig(model_config_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optimizer configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "optimizer_config_dict = {\"no_decay_params\": ['bias', 'gamma', 'beta'],\n",
-    "                         \"learning_rate\": 3e-5}\n",
-    "optimizer_config = OptimizerConfig(optimizer_config_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Train configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "train_config_dict = {\"train_batch_size\": 32,\n",
-    "                     \"num_train_epochs\": 2, \n",
-    "                     \"clip_gradient\": True}\n",
-    "train_config = TrainConfig(train_config_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Evaluation configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "eval_config = EvalConfig({\"eval_batch_size\":32})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set random seeds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<torch._C.Generator at 0x7f6f5748d170>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "random.seed(global_config.seed)\n",
-    "np.random.seed(global_config.seed)\n",
-    "torch.manual_seed(global_config.seed)"
+    "config = BERTFineTuneConfig(config_dict)"
    ]
   },
   {
@@ -263,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
@@ -274,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {
     "scrolled": false
    },
@@ -287,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {
     "scrolled": false
    },
@@ -323,25 +175,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tokenizer = BertTokenizer.from_pretrained(model_config.bert_model,\n",
-    "                                          do_lower_case=model_config.do_lower_case)\n",
-    "train_features = convert_examples_to_token_features(examples=train_examples,\n",
-    "                                                    tokenizer=tokenizer,\n",
-    "                                                    label_list=label_list, \n",
-    "                                                    model_config=model_config)\n",
-    "dev_features = convert_examples_to_token_features(examples=dev_examples,\n",
-    "                                                  tokenizer=tokenizer,\n",
-    "                                                  label_list=label_list, \n",
-    "                                                  model_config=model_config)"
+    "label_map = {label: i for i, label in enumerate(label_list)}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = BertTokenizer.from_pretrained(config.bert_model,\n",
+    "                                          do_lower_case=config.do_lower_case)\n",
+    "\n",
+    "train_features = convert_examples_to_token_features(examples=train_examples,\n",
+    "                                                    tokenizer=tokenizer,\n",
+    "                                                    label_map=label_map, \n",
+    "                                                    config=config)\n",
+    "dev_features = convert_examples_to_token_features(examples=dev_examples,\n",
+    "                                                  tokenizer=tokenizer,\n",
+    "                                                  label_map=label_map, \n",
+    "                                                  config=config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -355,7 +217,7 @@
       "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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",
       "\n",
       "Sample label ids:\n",
-      "[1, 1, 1, 1, 1, 1, 13, 1, 1, 1, 1, 1, 13, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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",
+      "[6, 6, 6, 6, 6, 6, 9, 6, 6, 6, 6, 6, 9, 6, 6, 6, 6, 6, 1, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]\n",
       "\n"
      ]
     }
@@ -370,42 +232,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Create dataloaders\n",
-    "The utility functions `create_train_dataloader` and `create_eval_dataloader` creates Pytorch dataloaders from features, which can be used for model training and evaluation. The following two steps are performed:\n",
-    "1. Convert numpy arrays to Pytorch tensors\n",
-    "2. Create dataloader for sampling and serving data in batches"
+    "## Create Token Classifier"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "train_dataloader = create_train_dataloader(train_features=train_features,\n",
-    "                                           model_config=model_config,\n",
-    "                                           train_config=train_config,\n",
-    "                                           device_config=device_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "valid_dataloader = create_eval_dataloader(eval_features=dev_features, \n",
-    "                                          model_config=model_config, \n",
-    "                                          eval_config=eval_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {
     "scrolled": false
    },
@@ -414,97 +246,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sample token id tensor:\n",
-      "tensor([ 1057, 29625,  2015, 29625, 29624,  3709,  2749,  1999,  7041,  2360,\n",
-      "         2027,  2730,  2321, 17671,  2076,  2019, 11585,  3169,  1999,  1996,\n",
-      "         2264,  1010,  2096,  2334,  4584,  2758,  2216,  2730,  2020,  9272,\n",
-      "         1012,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-      "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-      "            0,     0,     0,     0,     0])\n",
-      "\n",
-      "Sample attention mask tensor:\n",
-      "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",
-      "        1, 1, 1, 1, 1, 1, 1, 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])\n",
-      "\n",
-      "Sample label id tensor:\n",
-      "tensor([ 1, 17, 17, 17, 17, 17,  1,  1, 13,  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,\n",
-      "         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,\n",
-      "         1,  1,  1])\n",
-      "\n"
+      "BERT fine tune configurations:\n",
+      "batch_size=32\n",
+      "num_train_epochs=3\n",
+      "bert_model=bert-base-uncased\n",
+      "max_seq_length=75\n",
+      "do_lower_case=True\n",
+      "optimizer_name=BertAdam\n",
+      "learning_rate=3e-05\n",
+      "no_decay_params=['bias', 'gamma', 'beta']\n",
+      "params_weight_decay=0.01\n",
+      "clip_gradient=True\n",
+      "max_gradient_norm=1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "t_total value of -1 results in schedule not being applied\n"
      ]
     }
    ],
    "source": [
-    "it = iter(train_dataloader)\n",
-    "first = next(it)\n",
-    "print(\"Sample token id tensor:\\n{}\\n\".format(first[0][0]))\n",
-    "print(\"Sample attention mask tensor:\\n{}\\n\".format(first[1][0]))\n",
-    "print(\"Sample label id tensor:\\n{}\\n\".format(first[3][0]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "model = load_model(model_config=model_config, \n",
-    "                   path_config=path_config, \n",
-    "                   device_config=device_config,\n",
-    "                   global_config=global_config)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Configure Optimizer\n",
-    "This step must be done after loading the model, because the load_model function moves all model parameters to the device, e.g. GPU. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "optimizer_config = get_optimizer_params(optimizer_config=optimizer_config,\n",
-    "                                        train_config=train_config, \n",
-    "                                        device_config=device_config, \n",
-    "                                        model=model, \n",
-    "                                        num_train_examples=len(train_dataloader))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "# optimizer = BertAdam(optimizer_grouped_parameters,\n",
-    "#                      lr=optimizer_config.learning_rate,\n",
-    "#                      warmup=optimizer_config.warmup_proportion,\n",
-    "#                      t_total=num_train_optimization_steps)\n",
-    "optimizer = Adam(optimizer_config.grouped_parameters, lr=optimizer_config.learning_rate)"
+    "device, n_gpu = get_device()\n",
+    "token_classifier = BertTokenClassifier(config=config, \n",
+    "                                       label_map=label_map, \n",
+    "                                       device=device, \n",
+    "                                       n_gpu=n_gpu)"
    ]
   },
   {
@@ -516,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -525,1417 +294,1417 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch:   0%|          | 0/2 [00:00<?, ?it/s]\n",
+      "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]\n",
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-      "`--NotebookApp.iopub_msg_rate_limit`.\n",
       "\n",
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+      "Epoch: 100%|██████████| 3/3 [1:08:14<00:00, 1364.10s/it]3it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train loss: 0.057512551753210794\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n"
      ]
     }
    ],
    "source": [
-    "model, train_loss = train_model(model=model, \n",
-    "                                train_dataloader=train_dataloader, \n",
-    "                                optimizer=optimizer,\n",
-    "                                train_config=train_config, \n",
-    "                                model_config=model_config, \n",
-    "                                optimizer_config=optimizer_config,\n",
-    "                                device_config=device_config,\n",
-    "                                global_config=global_config)"
+    "token_classifier.fit(train_features)"
    ]
   },
   {
@@ -3144,21 +4583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "def flat_accuracy(preds, labels):\n",
-    "    pred_flat = np.argmax(preds, axis=2).flatten()\n",
-    "    labels_flat = labels.flatten()\n",
-    "    return np.sum(pred_flat == labels_flat) / len(labels_flat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 20,
    "metadata": {
     "scrolled": false
    },
@@ -3167,19 +4592,32 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation loss: 0.2494839150706927\n",
-      "Validation Accuracy: 0.9776226190476196\n",
-      "Validation F1-Score: 0.7582687879422232\n"
+      "Validation loss: 0.09586639727155367\n"
      ]
     }
    ],
    "source": [
-    "preds, eval_loss, eval_accuracy = eval_token_model(model=model, \n",
-    "                                                   eval_dataloader=valid_dataloader, \n",
-    "                                                   model_config=model_config, \n",
-    "                                                   device_config=device_config, \n",
-    "                                                   label_list=label_list,\n",
-    "                                                   eval_func=flat_accuracy)"
+    "pred_tags, true_tags = token_classifier.predict(dev_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4521074735617062"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(pred_tags, true_tags)"
    ]
   }
  ],