diff --git a/tutorial-notebooks/re/few-shot/empty.txt b/tutorial-notebooks/re/few-shot/empty.txt
deleted file mode 100644
index 5c3c8a6..0000000
--- a/tutorial-notebooks/re/few-shot/empty.txt
+++ /dev/null
@@ -1 +0,0 @@
-#empty file
diff --git a/tutorial-notebooks/re/standard/data/dev.json b/tutorial-notebooks/re/standard/data/dev.json
deleted file mode 100644
index 40da983..0000000
--- a/tutorial-notebooks/re/standard/data/dev.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/tutorial-notebooks/re/standard/data/rel2id.json b/tutorial-notebooks/re/standard/data/rel2id.json
deleted file mode 100644
index 3bf7d0b..0000000
--- a/tutorial-notebooks/re/standard/data/rel2id.json
+++ /dev/null
@@ -1,99 +0,0 @@
-{
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\ No newline at end of file
diff --git a/tutorial-notebooks/re/standard/data/rel_info.json b/tutorial-notebooks/re/standard/data/rel_info.json
deleted file mode 100644
index 2e94b2c..0000000
--- a/tutorial-notebooks/re/standard/data/rel_info.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/tutorial-notebooks/re/standard/data/test.json b/tutorial-notebooks/re/standard/data/test.json
deleted file mode 100644
index be709b1..0000000
--- a/tutorial-notebooks/re/standard/data/test.json
+++ /dev/null
@@ -1 +0,0 @@
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deleted file mode 100644
index 7917c68..0000000
--- a/tutorial-notebooks/re/standard/data/train_annotated.json
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"t": 7, "evidence": [0]}, {"r": "P710", "h": 1, "t": 4, "evidence": [0]}, {"r": "P607", "h": 6, "t": 0, "evidence": [0]}, {"r": "P607", "h": 6, "t": 1, "evidence": [0]}, {"r": "P241", "h": 6, "t": 5, "evidence": [0]}, {"r": "P607", "h": 7, "t": 0, "evidence": [0]}, {"r": "P607", "h": 7, "t": 1, "evidence": [0]}, {"r": "P241", "h": 7, "t": 5, "evidence": [0]}, {"r": "P607", "h": 3, "t": 1, "evidence": [0]}, {"r": "P607", "h": 4, "t": 1, "evidence": [0]}, {"r": "P607", "h": 5, "t": 1, "evidence": [0]}], "title": "Battle of Naseby", "sents": [["The", "Battle", "of", "Naseby", "was", "a", "decisive", "engagement", "of", "the", "English", "Civil", "War", ",", "fought", "on", "14", "June", "1645", "between", "the", "main", "Royalist", "army", "of", "King", "Charles", "I", "and", "the", "Parliamentarian", "New", "Model", "Army", ",", "commanded", "by", "Sir", "Thomas", "Fairfax", "and", "Oliver", "Cromwell", "."], ["It", "was", "fought", "near", "the", "village", "of", "Naseby", "in", "Northamptonshire", "."], ["After", "the", "Royalists", "stormed", "the", "Parliamentarian", "town", "of", "Leicester", "on", "31st", "May", "1645", ",", "Fairfax", "was", "ordered", "to", "lift", "his", "siege", "of", "Oxford", ",", "the", "Royalist", "capital", ",", "and", "engage", "the", "King", "'s", "main", "army", "."], ["Eager", "to", "bring", "the", "Royalists", "to", "battle", ",", "Fairfax", "set", "off", "in", "pursuit", "of", "the", "Royalist", "army", ",", "which", "was", "heading", "to", "recover", "the", "north", "."], ["The", "King", ",", "faced", "with", "retreating", "north", "with", "Fairfax", "close", "behind", ",", "or", "giving", "battle", ",", "decided", "to", "give", "battle", ",", "fearing", "a", "loss", "of", "morale", "if", "his", "army", "continued", "retreating", "."], ["After", "hard", "fighting", ",", "the", "Parliamentarian", "army", "had", "all", "but", "destroyed", "the", "Royalist", "force", ",", "which", "suffered", "7,000", "casualties", "out", "of", "7,400", "effectives", "."], ["Charles", "lost", "the", "bulk", "of", "his", "veteran", "infantry", "and", "officers", ",", "all", "of", "his", "artillery", "and", "stores", ",", "his", "personal", "baggage", "and", "many", "arms", ",", "ensuring", "the", "Royalists", "would", "never", "again", "field", "an", "army", "of", "comparable", "quality", "."], ["Captured", "in", "the", "baggage", "train", "were", "the", "King", "'s", "private", "papers", ",", "revealing", "to", "the", "fullest", "extent", "his", "attempts", "to", "draw", "Irish", "Catholics", "and", "foreign", "mercenaries", "into", "the", "war", "."], ["Publication", "of", "these", "papers", "gave", "Parliament", "an", "added", "moral", "cause", "in", "fighting", "the", "war", "to", "a", "finish", "."], ["Within", "a", "year", ",", "Parliament", "had", "won", "the", "first", "civil", "war", "."]]}, {"vertexSet": [[{"name": "Lark Force", "pos": [0, 2], "sent_id": 0, "type": "ORG"}, {"sent_id": 3, "type": "ORG", "pos": [2, 4], "name": "Lark Force"}, {"name": "Lark Force", "pos": [3, 5], "sent_id": 4, "type": "ORG"}], [{"name": "Australian Army", "pos": [4, 6], "sent_id": 0, "type": "ORG"}], [{"pos": [9, 11], "type": "TIME", "sent_id": 0, "name": "March 1941"}], [{"name": "World War II", "pos": [12, 15], "sent_id": 0, "type": "MISC"}], [{"name": "New Britain", "pos": [18, 20], "sent_id": 0, "type": "LOC"}], [{"name": "New Ireland", "pos": [21, 23], "sent_id": 0, "type": "LOC"}], [{"name": "John Scanlan", "pos": [6, 8], "sent_id": 1, "type": "PER"}], [{"name": "Australia", "pos": [13, 14], "sent_id": 1, "type": "LOC"}], [{"name": "Rabaul", "pos": [17, 18], "sent_id": 1, "type": "LOC"}, {"name": "Rabaul", "pos": [12, 13], "sent_id": 3, "type": "LOC"}], [{"name": "Kavieng", "pos": [19, 20], "sent_id": 1, "type": "LOC"}, {"name": "Kavieng", "pos": [14, 15], "sent_id": 3, "type": "LOC"}], [{"pos": [22, 24], "type": "MISC", "sent_id": 1, "name": "SS Katoomba"}], [{"pos": [25, 27], "type": "MISC", "sent_id": 1, "name": "MV Neptuna"}], [{"name": "HMAT Zealandia", "pos": [28, 30], "sent_id": 1, "type": "MISC"}], [{"name": "Imperial Japanese Army", "pos": [8, 11], "sent_id": 3, "type": "ORG"}], [{"pos": [18, 20], "type": "TIME", "sent_id": 3, "name": "January 1942"}], [{"name": "Japan", "pos": [8, 9], "sent_id": 4, "type": "LOC"}], [{"pos": [12, 13], "type": "MISC", "sent_id": 4, "name": "NCOs"}], [{"name": "USS Sturgeon", "pos": [20, 22], "sent_id": 4, "type": "MISC"}], [{"sent_id": 4, "type": "MISC", "pos": [27, 29], "name": "Montevideo Maru"}], [{"name": "Japanese", "pos": [5, 6], "sent_id": 5, "type": "LOC"}], [{"pos": [15, 16], "type": "NUM", "sent_id": 5, "name": "1,050"}], [{"pos": [17, 18], "type": "NUM", "sent_id": 5, "name": "1,053"}]], "labels": [{"r": "P607", "h": 1, "t": 3, "evidence": [0]}, {"r": "P17", "h": 1, "t": 7, "evidence": [0, 1]}, {"r": "P241", "h": 6, "t": 1, "evidence": [0, 1]}, {"r": "P607", "h": 6, "t": 3, "evidence": [0, 1]}, {"r": "P27", "h": 6, "t": 7, "evidence": [0, 1]}, {"r": "P1344", "h": 7, "t": 3, "evidence": [0, 1]}, {"r": "P607", "h": 13, "t": 3, "evidence": [0, 3]}, {"r": "P17", "h": 13, "t": 15, "evidence": [3, 4, 5]}, {"r": "P17", "h": 13, "t": 19, "evidence": [3, 4, 5]}, {"r": "P1344", "h": 15, "t": 3, "evidence": [0, 3, 4, 5]}, {"r": "P172", "h": 15, "t": 19, "evidence": [4, 5]}, {"r": "P607", "h": 17, "t": 3, "evidence": [0, 4]}, {"r": "P17", "h": 11, "t": 7, "evidence": [1]}, {"r": "P17", "h": 12, "t": 7, "evidence": [0, 1]}, {"r": "P137", "h": 0, "t": 1, "evidence": [0, 1]}, {"r": "P571", "h": 0, "t": 2, "evidence": [0]}, {"r": "P607", "h": 0, "t": 3, "evidence": [0]}, {"r": "P17", "h": 0, "t": 7, "evidence": [0, 1]}], "title": "Lark Force", "sents": [["Lark", "Force", "was", "an", "Australian", "Army", "formation", "established", "in", "March", "1941", "during", "World", "War", "II", "for", "service", "in", "New", "Britain", "and", "New", "Ireland", "."], ["Under", "the", "command", "of", "Lieutenant", "Colonel", "John", "Scanlan", ",", "it", "was", "raised", "in", "Australia", "and", "deployed", "to", "Rabaul", "and", "Kavieng", ",", "aboard", "SS", "Katoomba", ",", "MV", "Neptuna", "and", "HMAT", "Zealandia", ",", "to", "defend", "their", "strategically", "important", "harbours", "and", "airfields", "."], ["The", "objective", "of", "the", "force", ",", "was", "to", "maintain", "a", "forward", "air", "observation", "line", "as", "long", "as", "possible", "and", "to", "make", "the", "enemy", "fight", "for", "this", "line", "rather", "than", "abandon", "it", "at", "the", "first", "threat", "as", "the", "force", "was", "considered", "too", "small", "to", "withstand", "any", "invasion", "."], ["Most", "of", "Lark", "Force", "was", "captured", "by", "the", "Imperial", "Japanese", "Army", "after", "Rabaul", "and", "Kavieng", "were", "captured", "in", "January", "1942", "."], ["The", "officers", "of", "Lark", "Force", "were", "transported", "to", "Japan", ",", "however", "the", "NCOs", "and", "men", "were", "unfortunately", "torpedoed", "by", "the", "USS", "Sturgeon", "while", "being", "transported", "aboard", "the", "Montevideo", "Maru", "."], ["Only", "a", "handful", "of", "the", "Japanese", "crew", "were", "rescued", ",", "with", "none", "of", "the", "between", "1,050", "and", "1,053", "prisoners", "aboard", "surviving", "as", "they", "were", "still", "locked", "below", "deck", "."]]}]
\ No newline at end of file
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@@ -1,1167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 文档级关系抽取实践\n",
-    "\n",
-    "> Tutorial作者: 黎洲波（zhoubo.li@zju.edu.cn）\n",
-    "\n",
-    "关系抽取（Relation Extraction, RE）任务是信息抽取中的关键任务，通常而言，输入是文本和若干命名实体，输出是命名实体对的关系。\n",
-    "\n",
-    "DeepKE中的文档级（Document-level）关系抽取是基于计算机视觉（Computer Vision, CV）的语义分割方法，使用了U型网络（U-net）结构的神经网络模型，在[DocRED](https://github.com/thunlp/DocRED/tree/master/)数据集上取得了良好的效果。文档级关系抽取任务的输入输出如下图所示，图中彩色标注的是命名实体，不同于句子级（Sentence-level）关系抽取的是，文档级关系抽取不仅能在句子内部抽取实体之间的关系，还可以在多个句子之间抽取实体之间的关系。\n",
-    "\n",
-    "![文档级关系抽取](\"./img/img1.png\")\n",
-    "\n",
-    "通过本次实践展示，我希望读者能够快速了解如何构建文档级关系抽取的模型。\n",
-    "\n",
-    "## 数据集\n",
-    "\n",
-    "文档级关系抽取常用的数据集有：DocRED，CDR和GDA。本次实践使用的数据集是DocRED[https://github.com/thunlp/DocRED/tree/master/]，数据文件夹`./data/`的结构如下:\n",
-    "\n",
-    "```\n",
-    ".\n",
-    "├── dev.json                        # 验证集\n",
-    "├── rel_info.json                   # 关系集\n",
-    "├── rel2id.json                     # 关系标签到ID的映射\n",
-    "├── test.json                       # 测试集\n",
-    "└── train_annotated.json            # 训练集\n",
-    "```\n",
-    "\n",
-    "数据文件的格式在数据集DocRED中的描述如下：\n",
-    "\n",
-    "```JSON\n",
-    "Data Format:\n",
-    "{\n",
-    "  'title',\n",
-    "  'sents':     [\n",
-    "                  [word in sent 0],\n",
-    "                  [word in sent 1]\n",
-    "               ]\n",
-    "  'vertexSet': [\n",
-    "                  [\n",
-    "                    { 'name': mention_name, \n",
-    "                      'sent_id': mention in which sentence, \n",
-    "                      'pos': postion of mention in a sentence, \n",
-    "                      'type': NER_type}\n",
-    "                    {anthor mention}\n",
-    "                  ], \n",
-    "                  [anthoer entity]\n",
-    "                ]\n",
-    "  'labels':   [\n",
-    "                {\n",
-    "                  'h': idx of head entity in vertexSet,\n",
-    "                  't': idx of tail entity in vertexSet,\n",
-    "                  'r': relation,\n",
-    "                  'evidence': evidence sentences' id\n",
-    "                }\n",
-    "              ]\n",
-    "}\n",
-    "```\n",
-    "\n",
-    "## DocuNet原理\n",
-    "此处，我们使用视觉图像处理中的语义分割中的U-net结构神经网络来完成关系抽取，所以把该方法称为Document U-shaped Network(DocuNet)，DocuNet的原理结构图如下：\n",
-    "\n",
-    "![文档级关系抽取](\"./img/img2.png\")\n",
-    "\n",
-    "那么接下来我们开始文档级关系抽取实践！"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 运行环境\n",
-    "\n",
-    "Python环境使用Python3，并且要求以下packages的版本："
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "!pip install python==3.7\n",
-    "!pip install cuda==10.2\n",
-    "!pip install torch==1.5.0\n",
-    "!pip install transformers==3.0.4\n",
-    "!pip install opt-einsum==3.3.0\n",
-    "!pip install ujson\n",
-    "!pip install tqdm\n",
-    "!pip install allennlp\n",
-    "!pip install matplotlib"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 导入模块"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "import os\n",
-    "import time\n",
-    "import random\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import ujson as json\n",
-    "import os\n",
-    "import pickle\n",
-    "from tqdm import tqdm\n",
-    "from opt_einsum import contract\n",
-    "\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "from torch.utils.data import DataLoader\n",
-    "from transformers import AutoConfig, AutoModel, AutoTokenizer\n",
-    "from transformers.optimization import AdamW, get_linear_schedule_with_warmup\n",
-    "from allennlp.modules.matrix_attention import DotProductMatrixAttention, CosineMatrixAttention, BilinearMatrixAttention"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 数据集预处理"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "docred_rel2id = json.load(open('./data/rel2id.json', 'r'))\n",
-    "\n",
-    "def chunks(l, n):\n",
-    "    res = []\n",
-    "    for i in range(0, len(l), n):\n",
-    "        assert len(l[i:i + n]) == n\n",
-    "        res += [l[i:i + n]]\n",
-    "    return res\n",
-    "\n",
-    "class ReadDataset:\n",
-    "    def __init__(self, dataset: str, tokenizer, max_seq_Length: int = 1024,\n",
-    "             transformers: str = 'bert') -> None:\n",
-    "        self.transformers = transformers\n",
-    "        self.dataset = dataset\n",
-    "        self.tokenizer = tokenizer\n",
-    "        self.max_seq_Length = max_seq_Length\n",
-    "\n",
-    "    def read(self, file_in: str):\n",
-    "        save_file = file_in.split('.json')[0] + '_' + self.transformers + '_' \\\n",
-    "                        + self.dataset + '.pkl'\n",
-    "        if self.dataset == 'docred':\n",
-    "            return read_docred(self.transformers, file_in, save_file, self.tokenizer, self.max_seq_Length)\n",
-    "        else:\n",
-    "            raise RuntimeError(\"No read func for this dataset.\")\n",
-    "\n",
-    "def read_docred(transfermers, file_in, save_file, tokenizer, max_seq_length=1024):\n",
-    "    if os.path.exists(save_file):\n",
-    "        with open(file=save_file, mode='rb') as fr:\n",
-    "            features = pickle.load(fr)\n",
-    "            fr.close()\n",
-    "        print('load preprocessed data from {}.'.format(save_file))\n",
-    "        return features\n",
-    "    else:\n",
-    "        max_len = 0\n",
-    "        up512_num = 0\n",
-    "        i_line = 0\n",
-    "        pos_samples = 0\n",
-    "        neg_samples = 0\n",
-    "        features = []\n",
-    "        if file_in == \"\":\n",
-    "            return None\n",
-    "        with open(file_in, \"r\") as fh:\n",
-    "            data = json.load(fh)\n",
-    "        if transfermers == 'bert':\n",
-    "            # entity_type = [\"ORG\", \"-\",  \"LOC\", \"-\",  \"TIME\", \"-\",  \"PER\", \"-\", \"MISC\", \"-\", \"NUM\"]\n",
-    "            entity_type = [\"-\", \"ORG\", \"-\",  \"LOC\", \"-\",  \"TIME\", \"-\",  \"PER\", \"-\", \"MISC\", \"-\", \"NUM\"]\n",
-    "\n",
-    "\n",
-    "        for sample in tqdm(data, desc=\"Example\"):\n",
-    "            sents = []\n",
-    "            sent_map = []\n",
-    "\n",
-    "            entities = sample['vertexSet']\n",
-    "            entity_start, entity_end = [], []\n",
-    "            mention_types = []\n",
-    "            for entity in entities:\n",
-    "                for mention in entity:\n",
-    "                    sent_id = mention[\"sent_id\"]\n",
-    "                    pos = mention[\"pos\"]\n",
-    "                    entity_start.append((sent_id, pos[0]))\n",
-    "                    entity_end.append((sent_id, pos[1] - 1))\n",
-    "                    mention_types.append(mention['type'])\n",
-    "\n",
-    "            for i_s, sent in enumerate(sample['sents']):\n",
-    "                new_map = {}\n",
-    "                for i_t, token in enumerate(sent):\n",
-    "                    tokens_wordpiece = tokenizer.tokenize(token)\n",
-    "                    if (i_s, i_t) in entity_start:\n",
-    "                        t = entity_start.index((i_s, i_t))\n",
-    "                        if transfermers == 'bert':\n",
-    "                            mention_type = mention_types[t]\n",
-    "                            special_token_i = entity_type.index(mention_type)\n",
-    "                            special_token = ['[unused' + str(special_token_i) + ']']\n",
-    "                        else:\n",
-    "                            special_token = ['*']\n",
-    "                        tokens_wordpiece = special_token + tokens_wordpiece\n",
-    "                        # tokens_wordpiece = [\"[unused0]\"]+ tokens_wordpiece\n",
-    "\n",
-    "                    if (i_s, i_t) in entity_end:\n",
-    "                        t = entity_end.index((i_s, i_t))\n",
-    "                        if transfermers == 'bert':\n",
-    "                            mention_type = mention_types[t]\n",
-    "                            special_token_i = entity_type.index(mention_type) + 50\n",
-    "                            special_token = ['[unused' + str(special_token_i) + ']']\n",
-    "                        else:\n",
-    "                            special_token = ['*']\n",
-    "                        tokens_wordpiece = tokens_wordpiece + special_token\n",
-    "\n",
-    "                        # tokens_wordpiece = tokens_wordpiece + [\"[unused1]\"]\n",
-    "                        # print(tokens_wordpiece,tokenizer.convert_tokens_to_ids(tokens_wordpiece))\n",
-    "\n",
-    "                    new_map[i_t] = len(sents)\n",
-    "                    sents.extend(tokens_wordpiece)\n",
-    "                new_map[i_t + 1] = len(sents)\n",
-    "                sent_map.append(new_map)\n",
-    "\n",
-    "            if len(sents)>max_len:\n",
-    "                max_len=len(sents)\n",
-    "            if len(sents)>512:\n",
-    "                up512_num += 1\n",
-    "\n",
-    "            train_triple = {}\n",
-    "            if \"labels\" in sample:\n",
-    "                for label in sample['labels']:\n",
-    "                    evidence = label['evidence']\n",
-    "                    r = int(docred_rel2id[label['r']])\n",
-    "                    if (label['h'], label['t']) not in train_triple:\n",
-    "                        train_triple[(label['h'], label['t'])] = [\n",
-    "                            {'relation': r, 'evidence': evidence}]\n",
-    "                    else:\n",
-    "                        train_triple[(label['h'], label['t'])].append(\n",
-    "                            {'relation': r, 'evidence': evidence})\n",
-    "\n",
-    "            entity_pos = []\n",
-    "            for e in entities:\n",
-    "                entity_pos.append([])\n",
-    "                mention_num = len(e)\n",
-    "                for m in e:\n",
-    "                    start = sent_map[m[\"sent_id\"]][m[\"pos\"][0]]\n",
-    "                    end = sent_map[m[\"sent_id\"]][m[\"pos\"][1]]\n",
-    "                    entity_pos[-1].append((start, end,))\n",
-    "\n",
-    "\n",
-    "            relations, hts = [], []\n",
-    "            # Get positive samples from dataset\n",
-    "            for h, t in train_triple.keys():\n",
-    "                relation = [0] * len(docred_rel2id)\n",
-    "                for mention in train_triple[h, t]:\n",
-    "                    relation[mention[\"relation\"]] = 1\n",
-    "                    evidence = mention[\"evidence\"]\n",
-    "                relations.append(relation)\n",
-    "                hts.append([h, t])\n",
-    "                pos_samples += 1\n",
-    "\n",
-    "            # Get negative samples from dataset\n",
-    "            for h in range(len(entities)):\n",
-    "                for t in range(len(entities)):\n",
-    "                    if h != t and [h, t] not in hts:\n",
-    "                        relation = [1] + [0] * (len(docred_rel2id) - 1)\n",
-    "                        relations.append(relation)\n",
-    "                        hts.append([h, t])\n",
-    "                        neg_samples += 1\n",
-    "\n",
-    "            assert len(relations) == len(entities) * (len(entities) - 1)\n",
-    "\n",
-    "            if len(hts)==0:\n",
-    "                print(len(sent))\n",
-    "            sents = sents[:max_seq_length - 2]\n",
-    "            input_ids = tokenizer.convert_tokens_to_ids(sents)\n",
-    "            input_ids = tokenizer.build_inputs_with_special_tokens(input_ids)\n",
-    "\n",
-    "            i_line += 1\n",
-    "            feature = {'input_ids': input_ids,\n",
-    "                       'entity_pos': entity_pos,\n",
-    "                       'labels': relations,\n",
-    "                       'hts': hts,\n",
-    "                       'title': sample['title'],\n",
-    "                       }\n",
-    "            features.append(feature)\n",
-    "\n",
-    "\n",
-    "\n",
-    "        print(\"# of documents {}.\".format(i_line))\n",
-    "        print(\"# of positive examples {}.\".format(pos_samples))\n",
-    "        print(\"# of negative examples {}.\".format(neg_samples))\n",
-    "        print(\"# {} examples len>512 and max len is {}.\".format(up512_num, max_len))\n",
-    "\n",
-    "\n",
-    "        with open(file=save_file, mode='wb') as fw:\n",
-    "            pickle.dump(features, fw)\n",
-    "        print('finish reading {} and save preprocessed data to {}.'.format(file_in, save_file))\n",
-    "\n",
-    "        return features"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 模型构建"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "class AttentionUNet(torch.nn.Module):\n",
-    "    \"\"\"\n",
-    "    UNet, down sampling & up sampling for global reasoning\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def __init__(self, input_channels, class_number, **kwcfg):\n",
-    "        super(AttentionUNet, self).__init__()\n",
-    "\n",
-    "        down_channel = kwcfg['down_channel'] # default = 256\n",
-    "\n",
-    "        down_channel_2 = down_channel * 2\n",
-    "        up_channel_1 = down_channel_2 * 2\n",
-    "        up_channel_2 = down_channel * 2\n",
-    "\n",
-    "        self.inc = InConv(input_channels, down_channel)\n",
-    "        self.down1 = DownLayer(down_channel, down_channel_2)\n",
-    "        self.down2 = DownLayer(down_channel_2, down_channel_2)\n",
-    "\n",
-    "        self.up1 = UpLayer(up_channel_1, up_channel_1 // 4)\n",
-    "        self.up2 = UpLayer(up_channel_2, up_channel_2 // 4)\n",
-    "        self.outc = OutConv(up_channel_2 // 4, class_number)\n",
-    "\n",
-    "    def forward(self, attention_channels):\n",
-    "        \"\"\"\n",
-    "        Given multi-channel attention map, return the logits of every one mapping into 3-class\n",
-    "        :param attention_channels:\n",
-    "        :return:\n",
-    "        \"\"\"\n",
-    "        # attention_channels as the shape of: batch_size x channel x width x height\n",
-    "        x = attention_channels\n",
-    "        x1 = self.inc(x)\n",
-    "        x2 = self.down1(x1)\n",
-    "        x3 = self.down2(x2)\n",
-    "        x = self.up1(x3, x2)\n",
-    "        x = self.up2(x, x1)\n",
-    "        output = self.outc(x)\n",
-    "        # attn_map as the shape of: batch_size x width x height x class\n",
-    "        output = output.permute(0, 2, 3, 1).contiguous()\n",
-    "        return output\n",
-    "\n",
-    "\n",
-    "class DoubleConv(nn.Module):\n",
-    "    \"\"\"(conv => [BN] => ReLU) * 2\"\"\"\n",
-    "\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(DoubleConv, self).__init__()\n",
-    "        self.double_conv = nn.Sequential(nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),\n",
-    "                                         nn.BatchNorm2d(out_ch),\n",
-    "                                         nn.ReLU(inplace=True),\n",
-    "                                         nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),\n",
-    "                                         nn.BatchNorm2d(out_ch),\n",
-    "                                         nn.ReLU(inplace=True))\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.double_conv(x)\n",
-    "        return x\n",
-    "\n",
-    "\n",
-    "class InConv(nn.Module):\n",
-    "\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(InConv, self).__init__()\n",
-    "        self.conv = DoubleConv(in_ch, out_ch)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        return x\n",
-    "\n",
-    "\n",
-    "class DownLayer(nn.Module):\n",
-    "\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(DownLayer, self).__init__()\n",
-    "        self.maxpool_conv = nn.Sequential(\n",
-    "            nn.MaxPool2d(kernel_size=2),\n",
-    "            DoubleConv(in_ch, out_ch)\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.maxpool_conv(x)\n",
-    "        return x\n",
-    "\n",
-    "\n",
-    "class UpLayer(nn.Module):\n",
-    "\n",
-    "    def __init__(self, in_ch, out_ch, bilinear=True):\n",
-    "        super(UpLayer, self).__init__()\n",
-    "        if bilinear:\n",
-    "            self.up = nn.Upsample(scale_factor=2, mode='bilinear',\n",
-    "                                  align_corners=True)\n",
-    "        else:\n",
-    "            self.up = nn.ConvTranspose2d(in_ch // 2, in_ch // 2, 2, stride=2)\n",
-    "        self.conv = DoubleConv(in_ch, out_ch)\n",
-    "\n",
-    "    def forward(self, x1, x2):\n",
-    "        x1 = self.up(x1)\n",
-    "        diffY = x2.size()[2] - x1.size()[2]\n",
-    "        diffX = x2.size()[3] - x1.size()[3]\n",
-    "        x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2, diffY // 2, diffY -\n",
-    "                        diffY // 2))\n",
-    "        x = torch.cat([x2, x1], dim=1)\n",
-    "        x = self.conv(x)\n",
-    "        return x\n",
-    "\n",
-    "\n",
-    "class OutConv(nn.Module):\n",
-    "\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(OutConv, self).__init__()\n",
-    "        self.conv = nn.Conv2d(in_ch, out_ch, 1)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        return x\n",
-    "\n",
-    "class DocREModel(nn.Module):\n",
-    "    def __init__(self, config, cfg, model, emb_size=768, block_size=64, num_labels=-1):\n",
-    "        super().__init__()\n",
-    "        self.config = config\n",
-    "        self.bert_model = model\n",
-    "        self.hidden_size = config.hidden_size\n",
-    "        self.loss_fnt = ATLoss()\n",
-    "\n",
-    "        self.head_extractor = nn.Linear(1 * config.hidden_size + cfg.unet_out_dim, emb_size)\n",
-    "        self.tail_extractor = nn.Linear(1 * config.hidden_size + cfg.unet_out_dim, emb_size)\n",
-    "        # self.head_extractor = nn.Linear(1 * config.hidden_size , emb_size)\n",
-    "        # self.tail_extractor = nn.Linear(1 * config.hidden_size , emb_size)\n",
-    "        self.bilinear = nn.Linear(emb_size * block_size, config.num_labels)\n",
-    "\n",
-    "        self.emb_size = emb_size\n",
-    "        self.block_size = block_size\n",
-    "        self.num_labels = num_labels\n",
-    "\n",
-    "        self.bertdrop = nn.Dropout(0.6)\n",
-    "        self.unet_in_dim = cfg.unet_in_dim\n",
-    "        self.unet_out_dim = cfg.unet_in_dim\n",
-    "        self.liner = nn.Linear(config.hidden_size, cfg.unet_in_dim)\n",
-    "        self.min_height = cfg.max_height\n",
-    "        self.channel_type = cfg.channel_type\n",
-    "        self.segmentation_net = AttentionUNet(input_channels=cfg.unet_in_dim,\n",
-    "                                              class_number=cfg.unet_out_dim,\n",
-    "                                              down_channel=cfg.down_dim)\n",
-    "\n",
-    "\n",
-    "    def encode(self, input_ids, attention_mask,entity_pos):\n",
-    "        config = self.config\n",
-    "        if config.transformer_type == \"bert\":\n",
-    "            start_tokens = [config.cls_token_id]\n",
-    "            end_tokens = [config.sep_token_id]\n",
-    "        elif config.transformer_type == \"roberta\":\n",
-    "            start_tokens = [config.cls_token_id]\n",
-    "            end_tokens = [config.sep_token_id, config.sep_token_id]\n",
-    "        sequence_output, attention = process_long_input(self.bert_model, input_ids, attention_mask, start_tokens, end_tokens)\n",
-    "        return sequence_output, attention\n",
-    "\n",
-    "    def get_hrt(self, sequence_output, attention, entity_pos, hts):\n",
-    "        offset = 1 if self.config.transformer_type in [\"bert\", \"roberta\"] else 0\n",
-    "        bs, h, _, c = attention.size()\n",
-    "        # ne = max([len(x) for x in entity_pos])  # 本次bs中的最大实体数\n",
-    "\n",
-    "        hss, tss, rss = [], [], []\n",
-    "        entity_es = []\n",
-    "        entity_as = []\n",
-    "        for i in range(len(entity_pos)):\n",
-    "            entity_embs, entity_atts = [], []\n",
-    "            for entity_num, e in enumerate(entity_pos[i]):\n",
-    "                if len(e) > 1:\n",
-    "                    e_emb, e_att = [], []\n",
-    "                    for start, end in e:\n",
-    "                        if start + offset < c:\n",
-    "                            # In case the entity mention is truncated due to limited max seq length.\n",
-    "                            e_emb.append(sequence_output[i, start + offset])\n",
-    "                            e_att.append(attention[i, :, start + offset])\n",
-    "                    if len(e_emb) > 0:\n",
-    "                        e_emb = torch.logsumexp(torch.stack(e_emb, dim=0), dim=0)\n",
-    "                        e_att = torch.stack(e_att, dim=0).mean(0)\n",
-    "                    else:\n",
-    "                        e_emb = torch.zeros(self.config.hidden_size).to(sequence_output)\n",
-    "                        e_att = torch.zeros(h, c).to(attention)\n",
-    "                else:\n",
-    "                    start, end = e[0]\n",
-    "                    if start + offset < c:\n",
-    "                        e_emb = sequence_output[i, start + offset]\n",
-    "                        e_att = attention[i, :, start + offset]\n",
-    "                    else:\n",
-    "                        e_emb = torch.zeros(self.config.hidden_size).to(sequence_output)\n",
-    "                        e_att = torch.zeros(h, c).to(attention)\n",
-    "                entity_embs.append(e_emb)\n",
-    "                entity_atts.append(e_att)\n",
-    "            for _ in range(self.min_height-entity_num-1):\n",
-    "                entity_atts.append(e_att)\n",
-    "\n",
-    "            entity_embs = torch.stack(entity_embs, dim=0)  # [n_e, d]\n",
-    "            entity_atts = torch.stack(entity_atts, dim=0)  # [n_e, h, seq_len]\n",
-    "\n",
-    "\n",
-    "            entity_es.append(entity_embs)\n",
-    "            entity_as.append(entity_atts)\n",
-    "            ht_i = torch.LongTensor(hts[i]).to(sequence_output.device)\n",
-    "            hs = torch.index_select(entity_embs, 0, ht_i[:, 0])\n",
-    "            ts = torch.index_select(entity_embs, 0, ht_i[:, 1])\n",
-    "\n",
-    "            hss.append(hs)\n",
-    "            tss.append(ts)\n",
-    "        hss = torch.cat(hss, dim=0)\n",
-    "        tss = torch.cat(tss, dim=0)\n",
-    "        return hss, tss, entity_es, entity_as\n",
-    "\n",
-    "    def get_mask(self, ents, bs, ne, run_device):\n",
-    "        ent_mask = torch.zeros(bs, ne, device=run_device)\n",
-    "        rel_mask = torch.zeros(bs, ne, ne, device=run_device)\n",
-    "        for _b in range(bs):\n",
-    "            ent_mask[_b, :len(ents[_b])] = 1\n",
-    "            rel_mask[_b, :len(ents[_b]), :len(ents[_b])] = 1\n",
-    "        return ent_mask, rel_mask\n",
-    "\n",
-    "\n",
-    "    def get_ht(self, rel_enco, hts):\n",
-    "        htss = []\n",
-    "        for i in range(len(hts)):\n",
-    "            ht_index = hts[i]\n",
-    "            for (h_index, t_index) in ht_index:\n",
-    "                htss.append(rel_enco[i,h_index,t_index])\n",
-    "        htss = torch.stack(htss,dim=0)\n",
-    "        return htss\n",
-    "\n",
-    "    def get_channel_map(self, sequence_output, entity_as):\n",
-    "        # sequence_output = sequence_output.to('cpu')\n",
-    "        # attention = attention.to('cpu')\n",
-    "        bs,_,d = sequence_output.size()\n",
-    "        # ne = max([len(x) for x in entity_as])  # 本次bs中的最大实体数\n",
-    "        ne = self.min_height\n",
-    "\n",
-    "        index_pair = []\n",
-    "        for i in range(ne):\n",
-    "            tmp = torch.cat((torch.ones((ne, 1), dtype=int) * i, torch.arange(0, ne).unsqueeze(1)), dim=-1)\n",
-    "            index_pair.append(tmp)\n",
-    "        index_pair = torch.stack(index_pair, dim=0).reshape(-1, 2).to(sequence_output.device)\n",
-    "        map_rss = []\n",
-    "        for b in range(bs):\n",
-    "            entity_atts = entity_as[b]\n",
-    "            h_att = torch.index_select(entity_atts, 0, index_pair[:, 0])\n",
-    "            t_att = torch.index_select(entity_atts, 0, index_pair[:, 1])\n",
-    "            ht_att = (h_att * t_att).mean(1)\n",
-    "            ht_att = ht_att / (ht_att.sum(1, keepdim=True) + 1e-5)\n",
-    "            rs = contract(\"ld,rl->rd\", sequence_output[b], ht_att)\n",
-    "            map_rss.append(rs)\n",
-    "        map_rss = torch.cat(map_rss, dim=0).reshape(bs, ne, ne, d)\n",
-    "        return map_rss\n",
-    "\n",
-    "    def forward(self,\n",
-    "                input_ids=None,\n",
-    "                attention_mask=None,\n",
-    "                labels=None,\n",
-    "                entity_pos=None,\n",
-    "                hts=None,\n",
-    "                instance_mask=None,\n",
-    "                ):\n",
-    "\n",
-    "        sequence_output, attention = self.encode(input_ids, attention_mask,entity_pos)\n",
-    "\n",
-    "        bs, sequen_len, d = sequence_output.shape\n",
-    "        run_device = sequence_output.device.index\n",
-    "        ne = max([len(x) for x in entity_pos])  # 本次bs中的最大实体数\n",
-    "        ent_mask, rel_mask = self.get_mask(entity_pos, bs, ne, run_device)\n",
-    "\n",
-    "        # get hs, ts and entity_embs >> entity_rs\n",
-    "        hs, ts, entity_embs, entity_as = self.get_hrt(sequence_output, attention, entity_pos, hts)\n",
-    "\n",
-    "\n",
-    "        # 获得通道map的两种不同方法\n",
-    "        if self.channel_type == 'context-based':\n",
-    "            feature_map = self.get_channel_map(sequence_output, entity_as)\n",
-    "            ##print('feature_map:', feature_map.shape)\n",
-    "            attn_input = self.liner(feature_map).permute(0, 3, 1, 2).contiguous()\n",
-    "\n",
-    "        elif self.channel_type == 'similarity-based':\n",
-    "            ent_encode = sequence_output.new_zeros(bs, self.min_height, d)\n",
-    "            for _b in range(bs):\n",
-    "                entity_emb = entity_embs[_b]\n",
-    "                entity_num = entity_emb.size(0)\n",
-    "                ent_encode[_b, :entity_num, :] = entity_emb\n",
-    "            # similar0 = ElementWiseMatrixAttention()(ent_encode, ent_encode).unsqueeze(-1)\n",
-    "            similar1 = DotProductMatrixAttention()(ent_encode, ent_encode).unsqueeze(-1)\n",
-    "            similar2 = CosineMatrixAttention()(ent_encode, ent_encode).unsqueeze(-1)\n",
-    "            similar3 = BilinearMatrixAttention(self.emb_size,self.self.emb_size).to(ent_encode.device)(ent_encode, ent_encode).unsqueeze(-1)\n",
-    "            attn_input = torch.cat([similar1,similar2,similar3],dim=-1).permute(0, 3, 1, 2).contiguous()\n",
-    "        else:\n",
-    "            raise Exception(\"channel_type must be specify correctly\")\n",
-    "\n",
-    "\n",
-    "        attn_map = self.segmentation_net(attn_input)\n",
-    "        h_t = self.get_ht (attn_map, hts)\n",
-    "\n",
-    "        hs = torch.tanh(self.head_extractor(torch.cat([hs, h_t], dim=1)))\n",
-    "        ts = torch.tanh(self.tail_extractor(torch.cat([ts, h_t], dim=1)))\n",
-    "\n",
-    "\n",
-    "        b1 = hs.view(-1, self.emb_size // self.block_size, self.block_size)\n",
-    "        b2 = ts.view(-1, self.emb_size // self.block_size, self.block_size)\n",
-    "        bl = (b1.unsqueeze(3) * b2.unsqueeze(2)).view(-1, self.emb_size * self.block_size)\n",
-    "        logits = self.bilinear(bl)\n",
-    "\n",
-    "\n",
-    "        output = (self.loss_fnt.get_label(logits, num_labels=self.num_labels))\n",
-    "        if labels is not None:\n",
-    "            labels = [torch.tensor(label) for label in labels]\n",
-    "            labels = torch.cat(labels, dim=0).to(logits)\n",
-    "            loss = self.loss_fnt(logits.float(), labels.float())\n",
-    "            output = (loss.to(sequence_output), output)\n",
-    "        return output"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 损失函数"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "def multilabel_categorical_crossentropy(y_true, y_pred):\n",
-    "    \"\"\"多标签分类的交叉熵\n",
-    "    说明：y_true和y_pred的shape一致，y_true的元素非0即1，\n",
-    "         1表示对应的类为目标类，0表示对应的类为非目标类。\n",
-    "    警告：请保证y_pred的值域是全体实数，换言之一般情况下y_pred\n",
-    "         不用加激活函数，尤其是不能加sigmoid或者softmax！预测\n",
-    "         阶段则输出y_pred大于0的类。如有疑问，请仔细阅读并理解\n",
-    "         本文。\n",
-    "    \"\"\"\n",
-    "    y_pred = (1 - 2 * y_true) * y_pred\n",
-    "    y_pred_neg = y_pred - y_true * 1e30\n",
-    "    y_pred_pos = y_pred - (1 - y_true) * 1e30\n",
-    "    zeros = torch.zeros_like(y_pred[..., :1])\n",
-    "    y_pred_neg = torch.cat([y_pred_neg, zeros],dim=-1)\n",
-    "    y_pred_pos = torch.cat((y_pred_pos, zeros),dim=-1)\n",
-    "    neg_loss = torch.logsumexp(y_pred_neg, axis=-1)\n",
-    "    pos_loss = torch.logsumexp(y_pred_pos, axis=-1)\n",
-    "    return neg_loss + pos_loss\n",
-    "\n",
-    "\n",
-    "class balanced_loss(nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super().__init__()\n",
-    "\n",
-    "    def forward(self, logits, labels):\n",
-    "\n",
-    "        loss = multilabel_categorical_crossentropy(labels,logits)\n",
-    "        loss = loss.mean()\n",
-    "        return loss\n",
-    "\n",
-    "    def get_label(self, logits, num_labels=-1):\n",
-    "        th_logit = torch.zeros_like(logits[..., :1])\n",
-    "        output = torch.zeros_like(logits).to(logits)\n",
-    "        mask = (logits > th_logit)\n",
-    "        if num_labels > 0:\n",
-    "            top_v, _ = torch.topk(logits, num_labels, dim=1)\n",
-    "            top_v = top_v[:, -1]\n",
-    "            mask = (logits >= top_v.unsqueeze(1)) & mask\n",
-    "        output[mask] = 1.0\n",
-    "        output[:, 0] = (output[:,1:].sum(1) == 0.).to(logits)\n",
-    "\n",
-    "        return output"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 输入预处理"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "def process_long_input(model, input_ids, attention_mask, start_tokens, end_tokens):\n",
-    "    # Split the input to 2 overlapping chunks. Now BERT can encode inputs of which the length are up to 1024.\n",
-    "    n, c = input_ids.size()\n",
-    "    start_tokens = torch.tensor(start_tokens).to(input_ids)\n",
-    "    end_tokens = torch.tensor(end_tokens).to(input_ids)\n",
-    "    len_start = start_tokens.size(0)\n",
-    "    len_end = end_tokens.size(0)\n",
-    "    if c <= 512:\n",
-    "        output = model(\n",
-    "            input_ids=input_ids,\n",
-    "            attention_mask=attention_mask,\n",
-    "            output_attentions=True,\n",
-    "        )\n",
-    "        sequence_output = output[0]\n",
-    "        attention = output[-1][-1]\n",
-    "    else:\n",
-    "        new_input_ids, new_attention_mask, num_seg = [], [], []\n",
-    "        seq_len = attention_mask.sum(1).cpu().numpy().astype(np.int32).tolist()\n",
-    "        for i, l_i in enumerate(seq_len):\n",
-    "            if l_i <= 512:\n",
-    "                new_input_ids.append(input_ids[i, :512])\n",
-    "                new_attention_mask.append(attention_mask[i, :512])\n",
-    "                num_seg.append(1)\n",
-    "            else:\n",
-    "                input_ids1 = torch.cat([input_ids[i, :512 - len_end], end_tokens], dim=-1)\n",
-    "                input_ids2 = torch.cat([start_tokens, input_ids[i, (l_i - 512 + len_start): l_i]], dim=-1)\n",
-    "                attention_mask1 = attention_mask[i, :512]\n",
-    "                attention_mask2 = attention_mask[i, (l_i - 512): l_i]\n",
-    "                new_input_ids.extend([input_ids1, input_ids2])\n",
-    "                new_attention_mask.extend([attention_mask1, attention_mask2])\n",
-    "                num_seg.append(2)\n",
-    "        input_ids = torch.stack(new_input_ids, dim=0)\n",
-    "        attention_mask = torch.stack(new_attention_mask, dim=0)\n",
-    "        output = model(\n",
-    "            input_ids=input_ids,\n",
-    "            attention_mask=attention_mask,\n",
-    "            output_attentions=True,\n",
-    "        )\n",
-    "        sequence_output = output[0]\n",
-    "        attention = output[-1][-1]\n",
-    "        i = 0\n",
-    "        new_output, new_attention = [], []\n",
-    "        for (n_s, l_i) in zip(num_seg, seq_len):\n",
-    "            if n_s == 1:\n",
-    "                output = F.pad(sequence_output[i], (0, 0, 0, c - 512))\n",
-    "                att = F.pad(attention[i], (0, c - 512, 0, c - 512))\n",
-    "                new_output.append(output)\n",
-    "                new_attention.append(att)\n",
-    "            elif n_s == 2:\n",
-    "                output1 = sequence_output[i][:512 - len_end]\n",
-    "                mask1 = attention_mask[i][:512 - len_end]\n",
-    "                att1 = attention[i][:, :512 - len_end, :512 - len_end]\n",
-    "                output1 = F.pad(output1, (0, 0, 0, c - 512 + len_end))\n",
-    "                mask1 = F.pad(mask1, (0, c - 512 + len_end))\n",
-    "                att1 = F.pad(att1, (0, c - 512 + len_end, 0, c - 512 + len_end))\n",
-    "\n",
-    "                output2 = sequence_output[i + 1][len_start:]\n",
-    "                mask2 = attention_mask[i + 1][len_start:]\n",
-    "                att2 = attention[i + 1][:, len_start:, len_start:]\n",
-    "                output2 = F.pad(output2, (0, 0, l_i - 512 + len_start, c - l_i))\n",
-    "                mask2 = F.pad(mask2, (l_i - 512 + len_start, c - l_i))\n",
-    "                att2 = F.pad(att2, [l_i - 512 + len_start, c - l_i, l_i - 512 + len_start, c - l_i])\n",
-    "                mask = mask1 + mask2 + 1e-10\n",
-    "                output = (output1 + output2) / mask.unsqueeze(-1)\n",
-    "                att = (att1 + att2)\n",
-    "                att = att / (att.sum(-1, keepdim=True) + 1e-10)\n",
-    "                new_output.append(output)\n",
-    "                new_attention.append(att)\n",
-    "            i += n_s\n",
-    "        sequence_output = torch.stack(new_output, dim=0)\n",
-    "        attention = torch.stack(new_attention, dim=0)\n",
-    "    return sequence_output, attention"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 辅助函数"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "def set_seed(cfg):\n",
-    "    random.seed(cfg.seed)\n",
-    "    np.random.seed(cfg.seed)\n",
-    "    torch.manual_seed(cfg.seed)\n",
-    "    if cfg.n_gpu > 0 and torch.cuda.is_available():\n",
-    "        torch.cuda.manual_seed_all(cfg.seed)\n",
-    "\n",
-    "def collate_fn(batch):\n",
-    "    max_len = max([len(f[\"input_ids\"]) for f in batch])\n",
-    "    input_ids = [f[\"input_ids\"] + [0] * (max_len - len(f[\"input_ids\"])) for f in batch]\n",
-    "    input_mask = [[1.0] * len(f[\"input_ids\"]) + [0.0] * (max_len - len(f[\"input_ids\"])) for f in batch]\n",
-    "    input_ids = torch.tensor(input_ids, dtype=torch.long)\n",
-    "    input_mask = torch.tensor(input_mask, dtype=torch.float)\n",
-    "    entity_pos = [f[\"entity_pos\"] for f in batch]\n",
-    "\n",
-    "    labels = [f[\"labels\"] for f in batch]\n",
-    "    hts = [f[\"hts\"] for f in batch]\n",
-    "    output = (input_ids, input_mask, labels, entity_pos, hts )\n",
-    "    return output\n",
-    "\n",
-    "def to_official(preds, features):\n",
-    "    h_idx, t_idx, title = [], [], []\n",
-    "\n",
-    "    for f in features:\n",
-    "        hts = f[\"hts\"]\n",
-    "        h_idx += [ht[0] for ht in hts]\n",
-    "        t_idx += [ht[1] for ht in hts]\n",
-    "        title += [f[\"title\"] for ht in hts]\n",
-    "\n",
-    "    res = []\n",
-    "\n",
-    "\n",
-    "    for i in range(preds.shape[0]):\n",
-    "        pred = preds[i]\n",
-    "        pred = np.nonzero(pred)[0].tolist()\n",
-    "        for p in pred:\n",
-    "            if p != 0:\n",
-    "                res.append(\n",
-    "                    {\n",
-    "                        'title': title[i],\n",
-    "                        'h_idx': h_idx[i],\n",
-    "                        't_idx': t_idx[i],\n",
-    "                        'r': id2rel[p],\n",
-    "                    }\n",
-    "                )\n",
-    "    return res\n",
-    "\n",
-    "def report(cfg, model, features):\n",
-    "\n",
-    "    dataloader = DataLoader(features, batch_size=cfg.test_batch_size, shuffle=False, collate_fn=collate_fn, drop_last=False)\n",
-    "    preds = []\n",
-    "    for batch in dataloader:\n",
-    "        model.eval()\n",
-    "        inputs = {'input_ids': batch[0].to(cfg.device),\n",
-    "                  'attention_mask': batch[1].to(cfg.device),\n",
-    "                  'entity_pos': batch[3],\n",
-    "                  'hts': batch[4],\n",
-    "                  }\n",
-    "\n",
-    "        with torch.no_grad():\n",
-    "            pred, *_ = model(**inputs)\n",
-    "            pred = pred.cpu().numpy()\n",
-    "            pred[np.isnan(pred)] = 0\n",
-    "            preds.append(pred)\n",
-    "    preds = np.array(preds).astype(np.float32)\n",
-    "    preds = to_official(preds, features)\n",
-    "    return preds\n",
-    "\n",
-    "def logging(s, print_=True, log_=True):\n",
-    "    if print_:\n",
-    "        print(s)\n",
-    "    if log_:\n",
-    "        with open(cfg.log_dir, 'a+') as f_log:\n",
-    "            f_log.write(s + '\\n')"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 模型训练实践"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 模型参数配置"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "class Config(object):\n",
-    "    adam_epsilon=1e-06\n",
-    "    bert_lr=3e-05\n",
-    "    channel_type='context-based'\n",
-    "    config_name=''\n",
-    "    data_dir='./data'\n",
-    "    dataset='docred'\n",
-    "    dev_file='dev.json'\n",
-    "    down_dim=256\n",
-    "    evaluation_steps=-1\n",
-    "    gradient_accumulation_steps=2\n",
-    "    learning_rate=0.0004\n",
-    "    log_dir='./train_roberta.log'\n",
-    "    max_grad_norm=1.0\n",
-    "    max_height=42\n",
-    "    max_seq_length=1024\n",
-    "    model_name_or_path='roberta-base'\n",
-    "    num_class=97\n",
-    "    num_labels=4\n",
-    "    num_train_epochs=30\n",
-    "    save_path='./train_roberta.pt'\n",
-    "    seed=111\n",
-    "    test_batch_size=2\n",
-    "    test_file='test.json'\n",
-    "    tokenizer_name=''\n",
-    "    train_batch_size=2\n",
-    "    train_file='train_annotated.json'\n",
-    "    train_from_saved_model=''\n",
-    "    transformer_type='roberta'\n",
-    "    unet_in_dim=3\n",
-    "    unet_out_dim=256\n",
-    "    warmup_ratio=0.06\n",
-    "    load_path='./train_roberta.pt'\n",
-    "    \n",
-    "cfg = Config()"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 模型训练"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "def finetune(features, optimizer, num_epoch, num_steps, model):\n",
-    "        cur_model = model.module if hasattr(model, 'module') else model\n",
-    "        if cfg.train_from_saved_model != '':\n",
-    "            best_score = torch.load(cfg.train_from_saved_model)[\"best_f1\"]\n",
-    "            epoch_delta = torch.load(cfg.train_from_saved_model)[\"epoch\"] + 1\n",
-    "        else:\n",
-    "            epoch_delta = 0\n",
-    "            best_score = -1\n",
-    "        train_dataloader = DataLoader(features, batch_size=cfg.train_batch_size, shuffle=True, collate_fn=collate_fn, drop_last=True)\n",
-    "        train_iterator = [epoch + epoch_delta for epoch in range(num_epoch)]\n",
-    "        total_steps = int(len(train_dataloader) * num_epoch // cfg.gradient_accumulation_steps)\n",
-    "        warmup_steps = int(total_steps * cfg.warmup_ratio)\n",
-    "        scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)\n",
-    "        print(\"Total steps: {}\".format(total_steps))\n",
-    "        print(\"Warmup steps: {}\".format(warmup_steps))\n",
-    "        global_step = 0\n",
-    "        log_step = 100\n",
-    "        total_loss = 0\n",
-    "        \n",
-    "\n",
-    "\n",
-    "        #scaler = GradScaler()\n",
-    "        for epoch in train_iterator:\n",
-    "            start_time = time.time()\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "            for step, batch in enumerate(train_dataloader):\n",
-    "                model.train()\n",
-    "\n",
-    "                inputs = {'input_ids': batch[0].to(cfg.device),\n",
-    "                          'attention_mask': batch[1].to(cfg.device),\n",
-    "                          'labels': batch[2],\n",
-    "                          'entity_pos': batch[3],\n",
-    "                          'hts': batch[4],\n",
-    "                          }\n",
-    "                #with autocast():\n",
-    "                outputs = model(**inputs)\n",
-    "                loss = outputs[0] / cfg.gradient_accumulation_steps\n",
-    "                total_loss += loss.item()\n",
-    "                #    scaler.scale(loss).backward()\n",
-    "               \n",
-    "\n",
-    "                loss.backward()\n",
-    "\n",
-    "                if step % cfg.gradient_accumulation_steps == 0:\n",
-    "                    #scaler.unscale_(optimizer)\n",
-    "                    if cfg.max_grad_norm > 0:\n",
-    "                        # torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), cfg.max_grad_norm)\n",
-    "                        torch.nn.utils.clip_grad_norm_(cur_model.parameters(), cfg.max_grad_norm)\n",
-    "                    #scaler.step(optimizer)\n",
-    "                    #scaler.update()\n",
-    "                    #scheduler.step()\n",
-    "                    optimizer.step()\n",
-    "                    scheduler.step()\n",
-    "                    optimizer.zero_grad()\n",
-    "                    global_step += 1\n",
-    "                    num_steps += 1\n",
-    "                    if global_step % log_step == 0:\n",
-    "                        cur_loss = total_loss / log_step\n",
-    "                        elapsed = time.time() - start_time\n",
-    "                        logging(\n",
-    "                            '| epoch {:2d} | step {:4d} | min/b {:5.2f} | lr {} | train loss {:5.3f}'.format(\n",
-    "                                epoch, global_step, elapsed / 60, scheduler.get_last_lr(), cur_loss * 1000))\n",
-    "                        total_loss = 0\n",
-    "                        start_time = time.time()\n",
-    "\n",
-    "                if (step + 1) == len(train_dataloader) - 1 or (cfg.evaluation_steps > 0 and num_steps % cfg.evaluation_steps == 0 and step % cfg.gradient_accumulation_steps == 0):\n",
-    "                # if step ==0:\n",
-    "                    logging('-' * 89)\n",
-    "                    eval_start_time = time.time()\n",
-    "                    dev_score, dev_output = evaluate(cfg, model, dev_features, tag=\"dev\")\n",
-    "\n",
-    "                    logging(\n",
-    "                        '| epoch {:3d} | time: {:5.2f}s | dev_result:{}'.format(epoch, time.time() - eval_start_time,\n",
-    "                                                                                dev_output))\n",
-    "                    logging('-' * 89)\n",
-    "                    if dev_score > best_score:\n",
-    "                        best_score = dev_score\n",
-    "                        logging(\n",
-    "                            '| epoch {:3d} | best_f1:{}'.format(epoch, best_score))\n",
-    "                        pred = report(cfg, model, test_features)\n",
-    "                        with open(\"./submit_result/best_result.json\", \"w\") as fh:\n",
-    "                            json.dump(pred, fh)\n",
-    "                        if cfg.save_path != \"\":\n",
-    "                            torch.save({\n",
-    "                                'epoch': epoch,\n",
-    "                                'checkpoint': cur_model.state_dict(),\n",
-    "                                'best_f1': best_score,\n",
-    "                                'optimizer': optimizer.state_dict()\n",
-    "                            }, cfg.save_path\n",
-    "                            , _use_new_zipfile_serialization=False)\n",
-    "        return num_steps\n",
-    "\n",
-    "def train(cfg, model, train_features, dev_features, test_features):\n",
-    "    cur_model = model.module if hasattr(model, 'module') else model\n",
-    "    extract_layer = [\"extractor\", \"bilinear\"]\n",
-    "    bert_layer = ['bert_model']\n",
-    "    optimizer_grouped_parameters = [\n",
-    "        {\"params\": [p for n, p in cur_model.named_parameters() if any(nd in n for nd in bert_layer)], \"lr\": cfg.bert_lr},\n",
-    "        {\"params\": [p for n, p in cur_model.named_parameters() if any(nd in n for nd in extract_layer)], \"lr\": 1e-4},\n",
-    "        {\"params\": [p for n, p in cur_model.named_parameters() if not any(nd in n for nd in extract_layer + bert_layer)]},\n",
-    "    ]\n",
-    "\n",
-    "    optimizer = AdamW(optimizer_grouped_parameters, lr=cfg.learning_rate, eps=cfg.adam_epsilon)\n",
-    "    if cfg.train_from_saved_model != '':\n",
-    "        optimizer.load_state_dict(torch.load(cfg.train_from_saved_model)[\"optimizer\"])\n",
-    "        print(\"load saved optimizer from {}.\".format(cfg.train_from_saved_model))\n",
-    "    \n",
-    "\n",
-    "    num_steps = 0\n",
-    "    set_seed(cfg)\n",
-    "    model.zero_grad()\n",
-    "    finetune(train_features, optimizer, cfg.num_train_epochs, num_steps, model)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-    "cfg.n_gpu = torch.cuda.device_count()\n",
-    "cfg.device = device\n",
-    "\n",
-    "config = AutoConfig.from_pretrained(\n",
-    "    cfg.config_name if cfg.config_name else cfg.model_name_or_path,\n",
-    "    num_labels=cfg.num_class,\n",
-    ")\n",
-    "tokenizer = AutoTokenizer.from_pretrained(\n",
-    "    cfg.tokenizer_name if cfg.tokenizer_name else cfg.model_name_or_path,\n",
-    ")\n",
-    "\n",
-    "Dataset = ReadDataset(cfg.dataset, tokenizer, cfg.max_seq_length)\n",
-    "\n",
-    "train_file = os.path.join(cfg.data_dir, cfg.train_file)\n",
-    "dev_file = os.path.join(cfg.data_dir, cfg.dev_file)\n",
-    "test_file = os.path.join(cfg.data_dir, cfg.test_file)\n",
-    "train_features = Dataset.read(train_file)\n",
-    "dev_features = Dataset.read(dev_file)\n",
-    "test_features = Dataset.read(test_file)\n",
-    "\n",
-    "model = AutoModel.from_pretrained(\n",
-    "    cfg.model_name_or_path,\n",
-    "    from_tf=bool(\".ckpt\" in cfg.model_name_or_path),\n",
-    "    config=config,\n",
-    ")\n",
-    "\n",
-    "config.cls_token_id = tokenizer.cls_token_id\n",
-    "config.sep_token_id = tokenizer.sep_token_id\n",
-    "config.transformer_type = cfg.transformer_type\n",
-    "\n",
-    "set_seed(cfg)\n",
-    "model = DocREModel(config, cfg,  model, num_labels=cfg.num_labels)\n",
-    "if cfg.train_from_saved_model != '':\n",
-    "    model.load_state_dict(torch.load(cfg.train_from_saved_model)[\"checkpoint\"])\n",
-    "    print(\"load saved model from {}.\".format(cfg.train_from_saved_model))\n",
-    "\n",
-    "if torch.cuda.device_count() > 1:\n",
-    "        print(\"Let's use\", torch.cuda.device_count(), \"GPUs!\")\n",
-    "        model = torch.nn.DataParallel(model, device_ids = list(range(torch.cuda.device_count())))\n",
-    "model.to(device)\n",
-    "\n",
-    "\n",
-    "# Training\n",
-    "train(cfg, model, train_features, dev_features, test_features)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 模型预测输出"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "\n",
-    "model.load_state_dict(torch.load(cfg.load_path)['checkpoint'])\n",
-    "T_features = test_features  # Testing on the test set\n",
-    "#T_score, T_output = evaluate(args, model, T_features, tag=\"test\")\n",
-    "pred = report(cfg, model, T_features)\n",
-    "print(pred)\n",
-    "with open(\"./result.json\", \"w\") as fh:\n",
-    "    json.dump(pred, fh)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "本demo不包括调参部分，有兴趣的同学可以自行前往[DeepKE](https://github.com/zjunlp/DeepKE/tree/master)仓库，下载使用更多的模型 :)"
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file