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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tutorial/cornell_movie-dialogs_corpus/movie_lines.txt filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/Building BERT Tutorial-checkpoint.ipynb

@@ -0,0 +1,889 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fd69392",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tutorial url\n",
+    "# https://medium.com/data-and-beyond/complete-guide-to-building-bert-model-from-sratch-3e6562228891"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bc1129e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: transformers in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (4.24.0)\n",
+      "Collecting datasets\n",
+      "  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n",
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+      "Requirement already satisfied: tokenizers in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (0.11.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (22.0)\n",
+      "Requirement already satisfied: tqdm>=4.27 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (4.64.1)\n",
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+      "     ---------------------------------------- 61.0/61.0 kB ? eta 0:00:00\n",
+      "Collecting aiosignal>=1.1.2\n",
+      "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
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+      "Installing collected packages: xxhash, pyarrow, multiprocess, multidict, frozenlist, async-timeout, yarl, huggingface-hub, aiosignal, aiohttp, datasets\n",
+      "  Attempting uninstall: huggingface-hub\n",
+      "    Found existing installation: huggingface-hub 0.10.1\n",
+      "    Uninstalling huggingface-hub-0.10.1:\n",
+      "      Successfully uninstalled huggingface-hub-0.10.1\n",
+      "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.13.1 frozenlist-1.3.3 huggingface-hub-0.16.2 multidict-6.0.4 multiprocess-0.70.14 pyarrow-12.0.1 xxhash-3.2.0 yarl-1.9.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers datasets tokenizers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c23bad9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir(r\"C:\\Users\\yozhan\\cryptocurrency\\tutorial\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d6d279f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "import torch\n",
+    "import re\n",
+    "import random\n",
+    "import transformers, datasets\n",
+    "from tokenizers import BertWordPieceTokenizer\n",
+    "from transformers import BertTokenizer\n",
+    "import tqdm\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import itertools\n",
+    "import math\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "from torch.optim import Adam\n",
+    "\n",
+    "MAX_LEN = 64"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "af0bf9cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### loading all data into memory\n",
+    "corpus_movie_conv = r'.\\cornell_movie-dialogs_corpus\\movie_conversations.txt'\n",
+    "corpus_movie_lines = '.\\cornell_movie-dialogs_corpus\\movie_lines.txt'\n",
+    "with open(corpus_movie_conv, 'r', encoding='iso-8859-1') as c:\n",
+    "    conv = c.readlines()\n",
+    "with open(corpus_movie_lines, 'r', encoding='iso-8859-1') as l:\n",
+    "    lines = l.readlines()\n",
+    "\n",
+    "### splitting text using special lines\n",
+    "lines_dic = {}\n",
+    "for line in lines:\n",
+    "    objects = line.split(\" +++$+++ \")\n",
+    "    lines_dic[objects[0]] = objects[-1]\n",
+    "\n",
+    "### generate question answer pairs\n",
+    "pairs = []\n",
+    "for con in conv:\n",
+    "    \n",
+    "    # get a list of sentence ids\n",
+    "    ids = eval(con.split(\" +++$+++ \")[-1])\n",
+    "    for i in range(len(ids)):\n",
+    "        qa_pairs = []\n",
+    "        \n",
+    "        # if this is the last id\n",
+    "        if i == len(ids) - 1:\n",
+    "            break\n",
+    "\n",
+    "        first = lines_dic[ids[i]].strip()  \n",
+    "        second = lines_dic[ids[i+1]].strip() \n",
+    "\n",
+    "        qa_pairs.append(' '.join(first.split()[:MAX_LEN]))\n",
+    "        qa_pairs.append(' '.join(second.split()[:MAX_LEN]))\n",
+    "        pairs.append(qa_pairs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c9f5a78d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Can we make this quick? Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad. Again.', \"Well, I thought we'd start with pronunciation, if that's okay with you.\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pairs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "28b4a8b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 221616/221616 [00:00<00:00, 1154039.87it/s]\n",
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\transformers\\tokenization_utils_base.py:1679: FutureWarning: Calling BertTokenizer.from_pretrained() with the path to a single file or url is deprecated and won't be possible anymore in v5. Use a model identifier or the path to a directory instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.mkdir('./data')\n",
+    "text_data = []\n",
+    "file_count = 0\n",
+    "\n",
+    "for sample in tqdm.tqdm([x[0] for x in pairs]):\n",
+    "    text_data.append(sample)\n",
+    "\n",
+    "    # once we hit the 10K mark, save to file\n",
+    "    if len(text_data) == 10000:\n",
+    "        with open(f'./data/text_{file_count}.txt', 'w', encoding='utf-8') as fp:\n",
+    "            fp.write('\\n'.join(text_data))\n",
+    "        text_data = []\n",
+    "        file_count += 1\n",
+    "\n",
+    "paths = [str(x) for x in Path('./data').glob('**/*.txt')]\n",
+    "\n",
+    "### training own tokenizer\n",
+    "tokenizer = BertWordPieceTokenizer(\n",
+    "    clean_text=True,\n",
+    "    handle_chinese_chars=False,\n",
+    "    strip_accents=False,\n",
+    "    lowercase=True\n",
+    ")\n",
+    "\n",
+    "tokenizer.train( \n",
+    "    files=paths,\n",
+    "    vocab_size=30_000, \n",
+    "    min_frequency=5,\n",
+    "    limit_alphabet=1000, \n",
+    "    wordpieces_prefix='##',\n",
+    "    special_tokens=['[PAD]', '[CLS]', '[SEP]', '[MASK]', '[UNK]']\n",
+    "    )\n",
+    "\n",
+    "os.mkdir('./bert-it-1')\n",
+    "tokenizer.save_model('./bert-it-1', 'bert-it')\n",
+    "tokenizer = BertTokenizer.from_pretrained('./bert-it-1/bert-it-vocab.txt', local_files_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "30e775ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BERTDataset(Dataset):\n",
+    "    def __init__(self, data_pair, tokenizer, seq_len=64):\n",
+    "\n",
+    "        self.tokenizer = tokenizer\n",
+    "        self.seq_len = seq_len\n",
+    "        self.corpus_lines = len(data_pair)\n",
+    "        self.lines = data_pair\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.corpus_lines\n",
+    "\n",
+    "    def __getitem__(self, item):\n",
+    "\n",
+    "        # Step 1: get random sentence pair, either negative or positive (saved as is_next_label)\n",
+    "        t1, t2, is_next_label = self.get_sent(item)\n",
+    "\n",
+    "        # Step 2: replace random words in sentence with mask / random words\n",
+    "        t1_random, t1_label = self.random_word(t1)\n",
+    "        t2_random, t2_label = self.random_word(t2)\n",
+    "\n",
+    "        # Step 3: Adding CLS and SEP tokens to the start and end of sentences\n",
+    "         # Adding PAD token for labels\n",
+    "        t1 = [self.tokenizer.vocab['[CLS]']] + t1_random + [self.tokenizer.vocab['[SEP]']]\n",
+    "        t2 = t2_random + [self.tokenizer.vocab['[SEP]']]\n",
+    "        t1_label = [self.tokenizer.vocab['[PAD]']] + t1_label + [self.tokenizer.vocab['[PAD]']]\n",
+    "        t2_label = t2_label + [self.tokenizer.vocab['[PAD]']]\n",
+    "\n",
+    "        # Step 4: combine sentence 1 and 2 as one input\n",
+    "        # adding PAD tokens to make the sentence same length as seq_len\n",
+    "        segment_label = ([1 for _ in range(len(t1))] + [2 for _ in range(len(t2))])[:self.seq_len]\n",
+    "        bert_input = (t1 + t2)[:self.seq_len]\n",
+    "        bert_label = (t1_label + t2_label)[:self.seq_len]\n",
+    "        padding = [self.tokenizer.vocab['[PAD]'] for _ in range(self.seq_len - len(bert_input))]\n",
+    "        bert_input.extend(padding), bert_label.extend(padding), segment_label.extend(padding)\n",
+    "\n",
+    "        output = {\"bert_input\": bert_input,\n",
+    "                  \"bert_label\": bert_label,\n",
+    "                  \"segment_label\": segment_label,\n",
+    "                  \"is_next\": is_next_label}\n",
+    "\n",
+    "        return {key: torch.tensor(value) for key, value in output.items()}\n",
+    "\n",
+    "    def random_word(self, sentence):\n",
+    "        tokens = sentence.split()\n",
+    "        output_label = []\n",
+    "        output = []\n",
+    "\n",
+    "        # 15% of the tokens would be replaced\n",
+    "        for i, token in enumerate(tokens):\n",
+    "            prob = random.random()\n",
+    "\n",
+    "            # remove cls and sep token\n",
+    "            token_id = self.tokenizer(token)['input_ids'][1:-1]\n",
+    "\n",
+    "            if prob < 0.15:\n",
+    "                prob /= 0.15\n",
+    "\n",
+    "                # 80% chance change token to mask token\n",
+    "                if prob < 0.8:\n",
+    "                    for i in range(len(token_id)):\n",
+    "                        output.append(self.tokenizer.vocab['[MASK]'])\n",
+    "\n",
+    "                # 10% chance change token to random token\n",
+    "                elif prob < 0.9:\n",
+    "                    for i in range(len(token_id)):\n",
+    "                        output.append(random.randrange(len(self.tokenizer.vocab)))\n",
+    "\n",
+    "                # 10% chance change token to current token\n",
+    "                else:\n",
+    "                    output.append(token_id)\n",
+    "\n",
+    "                output_label.append(token_id)\n",
+    "\n",
+    "            else:\n",
+    "                output.append(token_id)\n",
+    "                for i in range(len(token_id)):\n",
+    "                    output_label.append(0)\n",
+    "\n",
+    "        # flattening\n",
+    "        output = list(itertools.chain(*[[x] if not isinstance(x, list) else x for x in output]))\n",
+    "        output_label = list(itertools.chain(*[[x] if not isinstance(x, list) else x for x in output_label]))\n",
+    "        assert len(output) == len(output_label)\n",
+    "        return output, output_label\n",
+    "\n",
+    "    def get_sent(self, index):\n",
+    "        '''return random sentence pair'''\n",
+    "        t1, t2 = self.get_corpus_line(index)\n",
+    "\n",
+    "        # negative or positive pair, for next sentence prediction\n",
+    "        if random.random() > 0.5:\n",
+    "            return t1, t2, 1\n",
+    "        else:\n",
+    "            return t1, self.get_random_line(), 0\n",
+    "\n",
+    "    def get_corpus_line(self, item):\n",
+    "        '''return sentence pair'''\n",
+    "        return self.lines[item][0], self.lines[item][1]\n",
+    "\n",
+    "    def get_random_line(self):\n",
+    "        '''return random single sentence'''\n",
+    "        return self.lines[random.randrange(len(self.lines))][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b0d9f35c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = BERTDataset(\n",
+    "   pairs, seq_len=MAX_LEN, tokenizer=tokenizer)\n",
+    "train_loader = DataLoader(\n",
+    "   train_data, batch_size=32, shuffle=True, pin_memory=True)\n",
+    "sample_data = next(iter(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ad60cf79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'bert_input': tensor([   1,  182,   11,   58,  162,  874,   34,    2,    6, 3232,  108,  512,\n",
+      "          17,    6,    2,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+      "           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,\n",
+      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+      "           0,    0,    0,    0]), 'bert_label': 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, 0, 0, 0, 0,\n",
+      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'segment_label': tensor([1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 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]), 'is_next': tensor(1)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(train_data[random.randrange(len(train_data))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bd70e96e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PositionalEmbedding(torch.nn.Module):\n",
+    "\n",
+    "    def __init__(self, d_model, max_len=128):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Compute the positional encodings once in log space.\n",
+    "        pe = torch.zeros(max_len, d_model).float()\n",
+    "        pe.require_grad = False\n",
+    "\n",
+    "        for pos in range(max_len):   \n",
+    "            # for each dimension of the each position\n",
+    "            for i in range(0, d_model, 2):   \n",
+    "                pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))\n",
+    "                pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))\n",
+    "\n",
+    "        # include the batch size\n",
+    "        self.pe = pe.unsqueeze(0)   \n",
+    "        # self.register_buffer('pe', pe)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.pe\n",
+    "\n",
+    "class BERTEmbedding(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    BERT Embedding which is consisted with under features\n",
+    "        1. TokenEmbedding : normal embedding matrix\n",
+    "        2. PositionalEmbedding : adding positional information using sin, cos\n",
+    "        2. SegmentEmbedding : adding sentence segment info, (sent_A:1, sent_B:2)\n",
+    "        sum of all these features are output of BERTEmbedding\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, vocab_size, embed_size, seq_len=64, dropout=0.1):\n",
+    "        \"\"\"\n",
+    "        :param vocab_size: total vocab size\n",
+    "        :param embed_size: embedding size of token embedding\n",
+    "        :param dropout: dropout rate\n",
+    "        \"\"\"\n",
+    "\n",
+    "        super().__init__()\n",
+    "        self.embed_size = embed_size\n",
+    "        # (m, seq_len) --> (m, seq_len, embed_size)\n",
+    "        # padding_idx is not updated during training, remains as fixed pad (0)\n",
+    "        self.token = torch.nn.Embedding(vocab_size, embed_size, padding_idx=0)\n",
+    "        self.segment = torch.nn.Embedding(3, embed_size, padding_idx=0)\n",
+    "        self.position = PositionalEmbedding(d_model=embed_size, max_len=seq_len)\n",
+    "        self.dropout = torch.nn.Dropout(p=dropout)\n",
+    "       \n",
+    "    def forward(self, sequence, segment_label):\n",
+    "        x = self.token(sequence) + self.position(sequence) + self.segment(segment_label)\n",
+    "        return self.dropout(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "baa5caa0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### attention layers\n",
+    "class MultiHeadedAttention(torch.nn.Module):\n",
+    "    \n",
+    "    def __init__(self, heads, d_model, dropout=0.1):\n",
+    "        super(MultiHeadedAttention, self).__init__()\n",
+    "        \n",
+    "        assert d_model % heads == 0\n",
+    "        self.d_k = d_model // heads\n",
+    "        self.heads = heads\n",
+    "        self.dropout = torch.nn.Dropout(dropout)\n",
+    "\n",
+    "        self.query = torch.nn.Linear(d_model, d_model)\n",
+    "        self.key = torch.nn.Linear(d_model, d_model)\n",
+    "        self.value = torch.nn.Linear(d_model, d_model)\n",
+    "        self.output_linear = torch.nn.Linear(d_model, d_model)\n",
+    "        \n",
+    "    def forward(self, query, key, value, mask):\n",
+    "        \"\"\"\n",
+    "        query, key, value of shape: (batch_size, max_len, d_model)\n",
+    "        mask of shape: (batch_size, 1, 1, max_words)\n",
+    "        \"\"\"\n",
+    "        # (batch_size, max_len, d_model)\n",
+    "        query = self.query(query)\n",
+    "        key = self.key(key)        \n",
+    "        value = self.value(value)   \n",
+    "        \n",
+    "        # (batch_size, max_len, d_model) --> (batch_size, max_len, h, d_k) --> (batch_size, h, max_len, d_k)\n",
+    "        query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)   \n",
+    "        key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)  \n",
+    "        value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)  \n",
+    "        \n",
+    "        # (batch_size, h, max_len, d_k) matmul (batch_size, h, d_k, max_len) --> (batch_size, h, max_len, max_len)\n",
+    "        scores = torch.matmul(query, key.permute(0, 1, 3, 2)) / math.sqrt(query.size(-1))\n",
+    "\n",
+    "        # fill 0 mask with super small number so it wont affect the softmax weight\n",
+    "        # (batch_size, h, max_len, max_len)\n",
+    "        scores = scores.masked_fill(mask == 0, -1e9)    \n",
+    "\n",
+    "        # (batch_size, h, max_len, max_len)\n",
+    "        # softmax to put attention weight for all non-pad tokens\n",
+    "        # max_len X max_len matrix of attention\n",
+    "        weights = F.softmax(scores, dim=-1)           \n",
+    "        weights = self.dropout(weights)\n",
+    "\n",
+    "        # (batch_size, h, max_len, max_len) matmul (batch_size, h, max_len, d_k) --> (batch_size, h, max_len, d_k)\n",
+    "        context = torch.matmul(weights, value)\n",
+    "\n",
+    "        # (batch_size, h, max_len, d_k) --> (batch_size, max_len, h, d_k) --> (batch_size, max_len, d_model)\n",
+    "        context = context.permute(0, 2, 1, 3).contiguous().view(context.shape[0], -1, self.heads * self.d_k)\n",
+    "\n",
+    "        # (batch_size, max_len, d_model)\n",
+    "        return self.output_linear(context)\n",
+    "\n",
+    "class FeedForward(torch.nn.Module):\n",
+    "    \"Implements FFN equation.\"\n",
+    "\n",
+    "    def __init__(self, d_model, middle_dim=2048, dropout=0.1):\n",
+    "        super(FeedForward, self).__init__()\n",
+    "        \n",
+    "        self.fc1 = torch.nn.Linear(d_model, middle_dim)\n",
+    "        self.fc2 = torch.nn.Linear(middle_dim, d_model)\n",
+    "        self.dropout = torch.nn.Dropout(dropout)\n",
+    "        self.activation = torch.nn.GELU()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        out = self.activation(self.fc1(x))\n",
+    "        out = self.fc2(self.dropout(out))\n",
+    "        return out\n",
+    "\n",
+    "class EncoderLayer(torch.nn.Module):\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        d_model=768,\n",
+    "        heads=12, \n",
+    "        feed_forward_hidden=768 * 4, \n",
+    "        dropout=0.1\n",
+    "        ):\n",
+    "        super(EncoderLayer, self).__init__()\n",
+    "        self.layernorm = torch.nn.LayerNorm(d_model)\n",
+    "        self.self_multihead = MultiHeadedAttention(heads, d_model)\n",
+    "        self.feed_forward = FeedForward(d_model, middle_dim=feed_forward_hidden)\n",
+    "        self.dropout = torch.nn.Dropout(dropout)\n",
+    "\n",
+    "    def forward(self, embeddings, mask):\n",
+    "        # embeddings: (batch_size, max_len, d_model)\n",
+    "        # encoder mask: (batch_size, 1, 1, max_len)\n",
+    "        # result: (batch_size, max_len, d_model)\n",
+    "        interacted = self.dropout(self.self_multihead(embeddings, embeddings, embeddings, mask))\n",
+    "        # residual layer\n",
+    "        interacted = self.layernorm(interacted + embeddings)\n",
+    "        # bottleneck\n",
+    "        feed_forward_out = self.dropout(self.feed_forward(interacted))\n",
+    "        encoded = self.layernorm(feed_forward_out + interacted)\n",
+    "        return encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "33fe273b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BERT(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    BERT model : Bidirectional Encoder Representations from Transformers.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, vocab_size, d_model=768, n_layers=12, heads=12, dropout=0.1):\n",
+    "        \"\"\"\n",
+    "        :param vocab_size: vocab_size of total words\n",
+    "        :param hidden: BERT model hidden size\n",
+    "        :param n_layers: numbers of Transformer blocks(layers)\n",
+    "        :param attn_heads: number of attention heads\n",
+    "        :param dropout: dropout rate\n",
+    "        \"\"\"\n",
+    "\n",
+    "        super().__init__()\n",
+    "        self.d_model = d_model\n",
+    "        self.n_layers = n_layers\n",
+    "        self.heads = heads\n",
+    "\n",
+    "        # paper noted they used 4 * hidden_size for ff_network_hidden_size\n",
+    "        self.feed_forward_hidden = d_model * 4\n",
+    "\n",
+    "        # embedding for BERT, sum of positional, segment, token embeddings\n",
+    "        self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=d_model)\n",
+    "\n",
+    "        # multi-layers transformer blocks, deep network\n",
+    "        self.encoder_blocks = torch.nn.ModuleList(\n",
+    "            [EncoderLayer(d_model, heads, d_model * 4, dropout) for _ in range(n_layers)])\n",
+    "\n",
+    "    def forward(self, x, segment_info):\n",
+    "        # attention masking for padded token\n",
+    "        # (batch_size, 1, seq_len, seq_len)\n",
+    "        mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1)\n",
+    "\n",
+    "        # embedding the indexed sequence to sequence of vectors\n",
+    "        x = self.embedding(x, segment_info)\n",
+    "\n",
+    "        # running over multiple transformer blocks\n",
+    "        for encoder in self.encoder_blocks:\n",
+    "            x = encoder.forward(x, mask)\n",
+    "        return x\n",
+    "\n",
+    "class NextSentencePrediction(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    2-class classification model : is_next, is_not_next\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, hidden):\n",
+    "        \"\"\"\n",
+    "        :param hidden: BERT model output size\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        self.linear = torch.nn.Linear(hidden, 2)\n",
+    "        self.softmax = torch.nn.LogSoftmax(dim=-1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # use only the first token which is the [CLS]\n",
+    "        return self.softmax(self.linear(x[:, 0]))\n",
+    "\n",
+    "class MaskedLanguageModel(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    predicting origin token from masked input sequence\n",
+    "    n-class classification problem, n-class = vocab_size\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, hidden, vocab_size):\n",
+    "        \"\"\"\n",
+    "        :param hidden: output size of BERT model\n",
+    "        :param vocab_size: total vocab size\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        self.linear = torch.nn.Linear(hidden, vocab_size)\n",
+    "        self.softmax = torch.nn.LogSoftmax(dim=-1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.softmax(self.linear(x))\n",
+    "\n",
+    "class BERTLM(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    BERT Language Model\n",
+    "    Next Sentence Prediction Model + Masked Language Model\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, bert: BERT, vocab_size):\n",
+    "        \"\"\"\n",
+    "        :param bert: BERT model which should be trained\n",
+    "        :param vocab_size: total vocab size for masked_lm\n",
+    "        \"\"\"\n",
+    "\n",
+    "        super().__init__()\n",
+    "        self.bert = bert\n",
+    "        self.next_sentence = NextSentencePrediction(self.bert.d_model)\n",
+    "        self.mask_lm = MaskedLanguageModel(self.bert.d_model, vocab_size)\n",
+    "\n",
+    "    def forward(self, x, segment_label):\n",
+    "        x = self.bert(x, segment_label)\n",
+    "        return self.next_sentence(x), self.mask_lm(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b8f7c1f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ScheduledOptim():\n",
+    "    '''A simple wrapper class for learning rate scheduling'''\n",
+    "\n",
+    "    def __init__(self, optimizer, d_model, n_warmup_steps):\n",
+    "        self._optimizer = optimizer\n",
+    "        self.n_warmup_steps = n_warmup_steps\n",
+    "        self.n_current_steps = 0\n",
+    "        self.init_lr = np.power(d_model, -0.5)\n",
+    "\n",
+    "    def step_and_update_lr(self):\n",
+    "        \"Step with the inner optimizer\"\n",
+    "        self._update_learning_rate()\n",
+    "        self._optimizer.step()\n",
+    "\n",
+    "    def zero_grad(self):\n",
+    "        \"Zero out the gradients by the inner optimizer\"\n",
+    "        self._optimizer.zero_grad()\n",
+    "\n",
+    "    def _get_lr_scale(self):\n",
+    "        return np.min([\n",
+    "            np.power(self.n_current_steps, -0.5),\n",
+    "            np.power(self.n_warmup_steps, -1.5) * self.n_current_steps])\n",
+    "\n",
+    "    def _update_learning_rate(self):\n",
+    "        ''' Learning rate scheduling per step '''\n",
+    "\n",
+    "        self.n_current_steps += 1\n",
+    "        lr = self.init_lr * self._get_lr_scale()\n",
+    "\n",
+    "        for param_group in self._optimizer.param_groups:\n",
+    "            param_group['lr'] = lr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9dd8e50e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BERTTrainer:\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        model, \n",
+    "        train_dataloader, \n",
+    "        test_dataloader=None, \n",
+    "        lr= 1e-4,\n",
+    "        weight_decay=0.01,\n",
+    "        betas=(0.9, 0.999),\n",
+    "        warmup_steps=10000,\n",
+    "        log_freq=10,\n",
+    "        device='cuda'\n",
+    "        ):\n",
+    "\n",
+    "        self.device = device\n",
+    "        self.model = model\n",
+    "        self.train_data = train_dataloader\n",
+    "        self.test_data = test_dataloader\n",
+    "\n",
+    "        # Setting the Adam optimizer with hyper-param\n",
+    "        self.optim = Adam(self.model.parameters(), lr=lr, betas=betas, weight_decay=weight_decay)\n",
+    "        self.optim_schedule = ScheduledOptim(\n",
+    "            self.optim, self.model.bert.d_model, n_warmup_steps=warmup_steps\n",
+    "            )\n",
+    "\n",
+    "        # Using Negative Log Likelihood Loss function for predicting the masked_token\n",
+    "        self.criterion = torch.nn.NLLLoss(ignore_index=0)\n",
+    "        self.log_freq = log_freq\n",
+    "        print(\"Total Parameters:\", sum([p.nelement() for p in self.model.parameters()]))\n",
+    "    \n",
+    "    def train(self, epoch):\n",
+    "        self.iteration(epoch, self.train_data)\n",
+    "\n",
+    "    def test(self, epoch):\n",
+    "        self.iteration(epoch, self.test_data, train=False)\n",
+    "\n",
+    "    def iteration(self, epoch, data_loader, train=True):\n",
+    "        \n",
+    "        avg_loss = 0.0\n",
+    "        total_correct = 0\n",
+    "        total_element = 0\n",
+    "        \n",
+    "        mode = \"train\" if train else \"test\"\n",
+    "\n",
+    "        # progress bar\n",
+    "        data_iter = tqdm.tqdm(\n",
+    "            enumerate(data_loader),\n",
+    "            desc=\"EP_%s:%d\" % (mode, epoch),\n",
+    "            total=len(data_loader),\n",
+    "            bar_format=\"{l_bar}{r_bar}\"\n",
+    "        )\n",
+    "\n",
+    "        for i, data in data_iter:\n",
+    "\n",
+    "            # 0. batch_data will be sent into the device(GPU or cpu)\n",
+    "            data = {key: value.to(self.device) for key, value in data.items()}\n",
+    "\n",
+    "            # 1. forward the next_sentence_prediction and masked_lm model\n",
+    "            next_sent_output, mask_lm_output = self.model.forward(data[\"bert_input\"], data[\"segment_label\"])\n",
+    "\n",
+    "            # 2-1. NLL(negative log likelihood) loss of is_next classification result\n",
+    "            next_loss = self.criterion(next_sent_output, data[\"is_next\"])\n",
+    "\n",
+    "            # 2-2. NLLLoss of predicting masked token word\n",
+    "            # transpose to (m, vocab_size, seq_len) vs (m, seq_len)\n",
+    "            # criterion(mask_lm_output.view(-1, mask_lm_output.size(-1)), data[\"bert_label\"].view(-1))\n",
+    "            mask_loss = self.criterion(mask_lm_output.transpose(1, 2), data[\"bert_label\"])\n",
+    "\n",
+    "            # 2-3. Adding next_loss and mask_loss : 3.4 Pre-training Procedure\n",
+    "            loss = next_loss + mask_loss\n",
+    "\n",
+    "            # 3. backward and optimization only in train\n",
+    "            if train:\n",
+    "                self.optim_schedule.zero_grad()\n",
+    "                loss.backward()\n",
+    "                self.optim_schedule.step_and_update_lr()\n",
+    "\n",
+    "            # next sentence prediction accuracy\n",
+    "            correct = next_sent_output.argmax(dim=-1).eq(data[\"is_next\"]).sum().item()\n",
+    "            avg_loss += loss.item()\n",
+    "            total_correct += correct\n",
+    "            total_element += data[\"is_next\"].nelement()\n",
+    "\n",
+    "            post_fix = {\n",
+    "                \"epoch\": epoch,\n",
+    "                \"iter\": i,\n",
+    "                \"avg_loss\": avg_loss / (i + 1),\n",
+    "                \"avg_acc\": total_correct / total_element * 100,\n",
+    "                \"loss\": loss.item()\n",
+    "            }\n",
+    "\n",
+    "            if i % self.log_freq == 0:\n",
+    "                data_iter.write(str(post_fix))\n",
+    "        print(\n",
+    "            f\"EP{epoch}, {mode}: \\\n",
+    "            avg_loss={avg_loss / len(data_iter)}, \\\n",
+    "            total_acc={total_correct * 100.0 / total_element}\"\n",
+    "        ) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "52de2072",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Parameters: 46699434\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "EP_train:0:   0%|| 0/6926 [01:16<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[28], line 20\u001b[0m\n\u001b[0;32m     17\u001b[0m epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20\u001b[39m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs):\n\u001b[1;32m---> 20\u001b[0m     \u001b[43mbert_trainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[25], line 32\u001b[0m, in \u001b[0;36mBERTTrainer.train\u001b[1;34m(self, epoch)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(\u001b[38;5;28mself\u001b[39m, epoch):\n\u001b[1;32m---> 32\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miteration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_data\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[25], line 75\u001b[0m, in \u001b[0;36mBERTTrainer.iteration\u001b[1;34m(self, epoch, data_loader, train)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m train:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_schedule\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m---> 75\u001b[0m     \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     76\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_schedule\u001b[38;5;241m.\u001b[39mstep_and_update_lr()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;66;03m# next sentence prediction accuracy\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\lib\\site-packages\\torch\\_tensor.py:396\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    387\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    388\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    389\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    390\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    394\u001b[0m         create_graph\u001b[38;5;241m=\u001b[39mcreate_graph,\n\u001b[0;32m    395\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs)\n\u001b[1;32m--> 396\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\lib\\site-packages\\torch\\autograd\\__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    168\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    170\u001b[0m \u001b[38;5;66;03m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[0;32m    171\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    172\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 173\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    174\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    175\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "train_data = BERTDataset(\n",
+    "   pairs, seq_len=MAX_LEN, tokenizer=tokenizer)\n",
+    "\n",
+    "train_loader = DataLoader(\n",
+    "   train_data, batch_size=32, shuffle=True, pin_memory=True)\n",
+    "\n",
+    "bert_model = BERT(\n",
+    "  vocab_size=len(tokenizer.vocab),\n",
+    "  d_model=768,\n",
+    "  n_layers=2,\n",
+    "  heads=12,\n",
+    "  dropout=0.1\n",
+    ")\n",
+    "\n",
+    "bert_lm = BERTLM(bert_model, len(tokenizer.vocab))\n",
+    "bert_trainer = BERTTrainer(bert_lm, train_loader, device='cpu')\n",
+    "epochs = 20\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    bert_trainer.train(epoch)"
+   ]
+  }
+ ],
+ "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Time series transformer Bitcoin-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Time windows for submissions-checkpoint.ipynb

@@ -0,0 +1,233 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cd0be85b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39c3d028",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# columns: id, timestamp, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a7621921",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bb10b577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts_list = []\n",
+    "with open(r\"C:\\Users\\yozhan\\Downloads\\Archive\\Bitcoin copy\\BitCoinsubmission4.csv\", \"r\", encoding=\"utf-8\") as infile:\n",
+    "    csv_reader = csv.reader(infile, delimiter=' ', quotechar='|')\n",
+    "    for row in csv_reader:\n",
+    "        ts = row[1]\n",
+    "        ts_list.append(ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe289999",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bitcoin\n",
+    "# submission1 2022-01-01 - 2022-03-01\n",
+    "# submission2 2022-03-01 - 2022-06-01\n",
+    "# submission3 2022-06-01 - 2022-09-01\n",
+    "# submission4 2022-09-01 - 2023-01-01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4fc8a065",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11927\n",
+      "2022-09-01 00:59:41\n",
+      "2023-01-01 23:51:53\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(ts_list))\n",
+    "print(datetime.fromtimestamp(float(min(ts_list))))\n",
+    "print(datetime.fromtimestamp(float(max(ts_list))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc4c9d89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ethereum\n",
+    "# submission1 2022-01-01 - 2022-03-01: 4481\n",
+    "# submission2 2022-03-01 - 2022-06-01: 3837\n",
+    "# submission3 2022-06-01 - 2022-09-01: 3172\n",
+    "# submission4 2022-09-01 - 2023-01-01: 4418"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "52ad83de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts_list = []\n",
+    "with open(r\"C:\\Users\\yozhan\\Downloads\\Archive\\Etherum copy\\Ethereumsubmission4.csv\", \"r\", encoding=\"utf-8\") as infile:\n",
+    "    csv_reader = csv.reader(infile, delimiter=' ', quotechar='|')\n",
+    "    for row in csv_reader:\n",
+    "        ts = row[1]\n",
+    "        ts_list.append(ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "244fdfe5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4418\n",
+      "2022-09-01 01:02:11\n",
+      "2023-01-01 23:00:11\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(ts_list))\n",
+    "print(datetime.fromtimestamp(float(min(ts_list))))\n",
+    "print(datetime.fromtimestamp(float(max(ts_list))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bf7d52e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dogecoin\n",
+    "# submission1 2022-01-01 - 2022-03-01: 11126\n",
+    "# submission2 2022-03-01 - 2022-06-01: 21513\n",
+    "# submission3 2022-06-01 - 2022-09-01: 10098\n",
+    "# submission4 2022-09-01 - 2023-01-01: ??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "8fb1550b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\yozhan\\\\Downloads\\\\Archive\\\\Dogecoin copy\\\\Dogecoinsubmission4.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[40], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m ts_list \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43myozhan\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDownloads\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mArchive\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDogecoin copy\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDogecoinsubmission4.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m infile:\n\u001b[0;32m      3\u001b[0m     csv_reader \u001b[38;5;241m=\u001b[39m csv\u001b[38;5;241m.\u001b[39mreader(infile, delimiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m, quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m|\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m csv_reader:\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:282\u001b[0m, in \u001b[0;36m_modified_open\u001b[1;34m(file, *args, **kwargs)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[0;32m    276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    277\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    278\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    279\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    280\u001b[0m     )\n\u001b[1;32m--> 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m io_open(file, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\yozhan\\\\Downloads\\\\Archive\\\\Dogecoin copy\\\\Dogecoinsubmission4.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "ts_list = []\n",
+    "with open(r\"C:\\Users\\yozhan\\Downloads\\Archive\\Dogecoin copy\\Dogecoinsubmission4.csv\", \"r\", encoding=\"utf-8\") as infile:\n",
+    "    csv_reader = csv.reader(infile, delimiter=' ', quotechar='|')\n",
+    "    for row in csv_reader:\n",
+    "        ts = row[1]\n",
+    "        ts_list.append(ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "37792ef7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "min() arg is an empty sequence",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[41], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(ts_list))\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(datetime\u001b[38;5;241m.\u001b[39mfromtimestamp(\u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mts_list\u001b[49m\u001b[43m)\u001b[49m)))\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(datetime\u001b[38;5;241m.\u001b[39mfromtimestamp(\u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;28mmax\u001b[39m(ts_list))))\n",
+      "\u001b[1;31mValueError\u001b[0m: min() arg is an empty sequence"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(ts_list))\n",
+    "print(datetime.fromtimestamp(float(min(ts_list))))\n",
+    "print(datetime.fromtimestamp(float(max(ts_list))))"
+   ]
+  }
+ ],
+ "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Building BERT Tutorial.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fd69392",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tutorial url\n",
+    "# https://medium.com/data-and-beyond/complete-guide-to-building-bert-model-from-sratch-3e6562228891"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bc1129e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: transformers in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (4.24.0)\n",
+      "Collecting datasets\n",
+      "  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n",
+      "     ------------------------------------- 486.2/486.2 kB 10.1 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: tokenizers in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (0.11.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (22.0)\n",
+      "Requirement already satisfied: tqdm>=4.27 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (4.64.1)\n",
+      "Requirement already satisfied: numpy>=1.17 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (1.23.5)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (0.10.1)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (6.0)\n",
+      "Requirement already satisfied: requests in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (2.28.1)\n",
+      "Requirement already satisfied: filelock in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (3.9.0)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from transformers) (2022.7.9)\n",
+      "Collecting huggingface-hub<1.0,>=0.10.0\n",
+      "  Downloading huggingface_hub-0.16.2-py3-none-any.whl (268 kB)\n",
+      "     ------------------------------------- 268.5/268.5 kB 16.1 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: pandas in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (1.5.3)\n",
+      "Collecting aiohttp\n",
+      "  Downloading aiohttp-3.8.4-cp310-cp310-win_amd64.whl (319 kB)\n",
+      "     ---------------------------------------- 319.8/319.8 kB ? eta 0:00:00\n",
+      "Requirement already satisfied: fsspec[http]>=2021.11.1 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (2022.11.0)\n",
+      "Collecting xxhash\n",
+      "  Downloading xxhash-3.2.0-cp310-cp310-win_amd64.whl (30 kB)\n",
+      "Collecting pyarrow>=8.0.0\n",
+      "  Downloading pyarrow-12.0.1-cp310-cp310-win_amd64.whl (21.5 MB)\n",
+      "     --------------------------------------- 21.5/21.5 MB 26.1 MB/s eta 0:00:00\n",
+      "Collecting multiprocess\n",
+      "  Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n",
+      "     -------------------------------------- 134.3/134.3 kB 8.3 MB/s eta 0:00:00\n",
+      "Requirement already satisfied: dill<0.3.7,>=0.3.0 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from datasets) (0.3.6)\n",
+      "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from aiohttp->datasets) (2.0.4)\n",
+      "Collecting yarl<2.0,>=1.0\n",
+      "  Downloading yarl-1.9.2-cp310-cp310-win_amd64.whl (61 kB)\n",
+      "     ---------------------------------------- 61.0/61.0 kB ? eta 0:00:00\n",
+      "Collecting aiosignal>=1.1.2\n",
+      "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
+      "Collecting frozenlist>=1.1.1\n",
+      "  Downloading frozenlist-1.3.3-cp310-cp310-win_amd64.whl (33 kB)\n",
+      "Collecting async-timeout<5.0,>=4.0.0a3\n",
+      "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
+      "Collecting multidict<7.0,>=4.5\n",
+      "  Downloading multidict-6.0.4-cp310-cp310-win_amd64.whl (28 kB)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from aiohttp->datasets) (22.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.4.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from requests->transformers) (3.4)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from requests->transformers) (1.26.14)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from requests->transformers) (2022.12.7)\n",
+      "Requirement already satisfied: colorama in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from tqdm>=4.27->transformers) (0.4.6)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas->datasets) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from pandas->datasets) (2022.7)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\yozhan\\appdata\\local\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
+      "Installing collected packages: xxhash, pyarrow, multiprocess, multidict, frozenlist, async-timeout, yarl, huggingface-hub, aiosignal, aiohttp, datasets\n",
+      "  Attempting uninstall: huggingface-hub\n",
+      "    Found existing installation: huggingface-hub 0.10.1\n",
+      "    Uninstalling huggingface-hub-0.10.1:\n",
+      "      Successfully uninstalled huggingface-hub-0.10.1\n",
+      "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.13.1 frozenlist-1.3.3 huggingface-hub-0.16.2 multidict-6.0.4 multiprocess-0.70.14 pyarrow-12.0.1 xxhash-3.2.0 yarl-1.9.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers datasets tokenizers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c23bad9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir(r\"C:\\Users\\yozhan\\cryptocurrency\\tutorial\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d6d279f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "import torch\n",
+    "import re\n",
+    "import random\n",
+    "import transformers, datasets\n",
+    "from tokenizers import BertWordPieceTokenizer\n",
+    "from transformers import BertTokenizer\n",
+    "import tqdm\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import itertools\n",
+    "import math\n",
+    "import torch.nn.functional as F\n",
+    "import numpy as np\n",
+    "from torch.optim import Adam\n",
+    "\n",
+    "MAX_LEN = 64"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "af0bf9cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### loading all data into memory\n",
+    "corpus_movie_conv = r'.\\cornell_movie-dialogs_corpus\\movie_conversations.txt'\n",
+    "corpus_movie_lines = '.\\cornell_movie-dialogs_corpus\\movie_lines.txt'\n",
+    "with open(corpus_movie_conv, 'r', encoding='iso-8859-1') as c:\n",
+    "    conv = c.readlines()\n",
+    "with open(corpus_movie_lines, 'r', encoding='iso-8859-1') as l:\n",
+    "    lines = l.readlines()\n",
+    "\n",
+    "### splitting text using special lines\n",
+    "lines_dic = {}\n",
+    "for line in lines:\n",
+    "    objects = line.split(\" +++$+++ \")\n",
+    "    lines_dic[objects[0]] = objects[-1]\n",
+    "\n",
+    "### generate question answer pairs\n",
+    "pairs = []\n",
+    "for con in conv:\n",
+    "    \n",
+    "    # get a list of sentence ids\n",
+    "    ids = eval(con.split(\" +++$+++ \")[-1])\n",
+    "    for i in range(len(ids)):\n",
+    "        qa_pairs = []\n",
+    "        \n",
+    "        # if this is the last id\n",
+    "        if i == len(ids) - 1:\n",
+    "            break\n",
+    "\n",
+    "        first = lines_dic[ids[i]].strip()  \n",
+    "        second = lines_dic[ids[i+1]].strip() \n",
+    "\n",
+    "        qa_pairs.append(' '.join(first.split()[:MAX_LEN]))\n",
+    "        qa_pairs.append(' '.join(second.split()[:MAX_LEN]))\n",
+    "        pairs.append(qa_pairs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c9f5a78d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Can we make this quick? Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad. Again.', \"Well, I thought we'd start with pronunciation, if that's okay with you.\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pairs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "28b4a8b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 221616/221616 [00:00<00:00, 1154039.87it/s]\n",
+      "C:\\Users\\yozhan\\AppData\\Local\\anaconda3\\lib\\site-packages\\transformers\\tokenization_utils_base.py:1679: FutureWarning: Calling BertTokenizer.from_pretrained() with the path to a single file or url is deprecated and won't be possible anymore in v5. Use a model identifier or the path to a directory instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.mkdir('./data')\n",
+    "text_data = []\n",
+    "file_count = 0\n",
+    "\n",
+    "for sample in tqdm.tqdm([x[0] for x in pairs]):\n",
+    "    text_data.append(sample)\n",
+    "\n",
+    "    # once we hit the 10K mark, save to file\n",
+    "    if len(text_data) == 10000:\n",
+    "        with open(f'./data/text_{file_count}.txt', 'w', encoding='utf-8') as fp:\n",
+    "            fp.write('\\n'.join(text_data))\n",
+    "        text_data = []\n",
+    "        file_count += 1\n",
+    "\n",
+    "paths = [str(x) for x in Path('./data').glob('**/*.txt')]\n",
+    "\n",
+    "### training own tokenizer\n",
+    "tokenizer = BertWordPieceTokenizer(\n",
+    "    clean_text=True,\n",
+    "    handle_chinese_chars=False,\n",
+    "    strip_accents=False,\n",
+    "    lowercase=True\n",
+    ")\n",
+    "\n",
+    "tokenizer.train( \n",
+    "    files=paths,\n",
+    "    vocab_size=30_000, \n",
+    "    min_frequency=5,\n",
+    "    limit_alphabet=1000, \n",
+    "    wordpieces_prefix='##',\n",
+    "    special_tokens=['[PAD]', '[CLS]', '[SEP]', '[MASK]', '[UNK]']\n",
+    "    )\n",
+    "\n",
+    "os.mkdir('./bert-it-1')\n",
+    "tokenizer.save_model('./bert-it-1', 'bert-it')\n",
+    "tokenizer = BertTokenizer.from_pretrained('./bert-it-1/bert-it-vocab.txt', local_files_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "30e775ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BERTDataset(Dataset):\n",
+    "    def __init__(self, data_pair, tokenizer, seq_len=64):\n",
+    "\n",
+    "        self.tokenizer = tokenizer\n",
+    "        self.seq_len = seq_len\n",
+    "        self.corpus_lines = len(data_pair)\n",
+    "        self.lines = data_pair\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.corpus_lines\n",
+    "\n",
+    "    def __getitem__(self, item):\n",
+    "\n",
+    "        # Step 1: get random sentence pair, either negative or positive (saved as is_next_label)\n",
+    "        t1, t2, is_next_label = self.get_sent(item)\n",
+    "\n",
+    "        # Step 2: replace random words in sentence with mask / random words\n",
+    "        t1_random, t1_label = self.random_word(t1)\n",
+    "        t2_random, t2_label = self.random_word(t2)\n",
+    "\n",
+    "        # Step 3: Adding CLS and SEP tokens to the start and end of sentences\n",
+    "         # Adding PAD token for labels\n",
+    "        t1 = [self.tokenizer.vocab['[CLS]']] + t1_random + [self.tokenizer.vocab['[SEP]']]\n",
+    "        t2 = t2_random + [self.tokenizer.vocab['[SEP]']]\n",
+    "        t1_label = [self.tokenizer.vocab['[PAD]']] + t1_label + [self.tokenizer.vocab['[PAD]']]\n",
+    "        t2_label = t2_label + [self.tokenizer.vocab['[PAD]']]\n",
+    "\n",
+    "        # Step 4: combine sentence 1 and 2 as one input\n",
+    "        # adding PAD tokens to make the sentence same length as seq_len\n",
+    "        segment_label = ([1 for _ in range(len(t1))] + [2 for _ in range(len(t2))])[:self.seq_len]\n",
+    "        bert_input = (t1 + t2)[:self.seq_len]\n",
+    "        bert_label = (t1_label + t2_label)[:self.seq_len]\n",
+    "        padding = [self.tokenizer.vocab['[PAD]'] for _ in range(self.seq_len - len(bert_input))]\n",
+    "        bert_input.extend(padding), bert_label.extend(padding), segment_label.extend(padding)\n",
+    "\n",
+    "        output = {\"bert_input\": bert_input,\n",
+    "                  \"bert_label\": bert_label,\n",
+    "                  \"segment_label\": segment_label,\n",
+    "                  \"is_next\": is_next_label}\n",
+    "\n",
+    "        return {key: torch.tensor(value) for key, value in output.items()}\n",
+    "\n",
+    "    def random_word(self, sentence):\n",
+    "        tokens = sentence.split()\n",
+    "        output_label = []\n",
+    "        output = []\n",
+    "\n",
+    "        # 15% of the tokens would be replaced\n",
+    "        for i, token in enumerate(tokens):\n",
+    "            prob = random.random()\n",
+    "\n",
+    "            # remove cls and sep token\n",
+    "            token_id = self.tokenizer(token)['input_ids'][1:-1]\n",
+    "\n",
+    "            if prob < 0.15:\n",
+    "                prob /= 0.15\n",
+    "\n",
+    "                # 80% chance change token to mask token\n",
+    "                if prob < 0.8:\n",
+    "                    for i in range(len(token_id)):\n",
+    "                        output.append(self.tokenizer.vocab['[MASK]'])\n",
+    "\n",
+    "                # 10% chance change token to random token\n",
+    "                elif prob < 0.9:\n",
+    "                    for i in range(len(token_id)):\n",
+    "                        output.append(random.randrange(len(self.tokenizer.vocab)))\n",
+    "\n",
+    "                # 10% chance change token to current token\n",
+    "                else:\n",
+    "                    output.append(token_id)\n",
+    "\n",
+    "                output_label.append(token_id)\n",
+    "\n",
+    "            else:\n",
+    "                output.append(token_id)\n",
+    "                for i in range(len(token_id)):\n",
+    "                    output_label.append(0)\n",
+    "\n",
+    "        # flattening\n",
+    "        output = list(itertools.chain(*[[x] if not isinstance(x, list) else x for x in output]))\n",
+    "        output_label = list(itertools.chain(*[[x] if not isinstance(x, list) else x for x in output_label]))\n",
+    "        assert len(output) == len(output_label)\n",
+    "        return output, output_label\n",
+    "\n",
+    "    def get_sent(self, index):\n",
+    "        '''return random sentence pair'''\n",
+    "        t1, t2 = self.get_corpus_line(index)\n",
+    "\n",
+    "        # negative or positive pair, for next sentence prediction\n",
+    "        if random.random() > 0.5:\n",
+    "            return t1, t2, 1\n",
+    "        else:\n",
+    "            return t1, self.get_random_line(), 0\n",
+    "\n",
+    "    def get_corpus_line(self, item):\n",
+    "        '''return sentence pair'''\n",
+    "        return self.lines[item][0], self.lines[item][1]\n",
+    "\n",
+    "    def get_random_line(self):\n",
+    "        '''return random single sentence'''\n",
+    "        return self.lines[random.randrange(len(self.lines))][1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b0d9f35c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = BERTDataset(\n",
+    "   pairs, seq_len=MAX_LEN, tokenizer=tokenizer)\n",
+    "train_loader = DataLoader(\n",
+    "   train_data, batch_size=32, shuffle=True, pin_memory=True)\n",
+    "sample_data = next(iter(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ad60cf79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'bert_input': tensor([   1,  182,   11,   58,  162,  874,   34,    2,    6, 3232,  108,  512,\n",
+      "          17,    6,    2,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+      "           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,\n",
+      "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
+      "           0,    0,    0,    0]), 'bert_label': 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, 0, 0, 0, 0,\n",
+      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'segment_label': tensor([1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 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]), 'is_next': tensor(1)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(train_data[random.randrange(len(train_data))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bd70e96e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PositionalEmbedding(torch.nn.Module):\n",
+    "\n",
+    "    def __init__(self, d_model, max_len=128):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        # Compute the positional encodings once in log space.\n",
+    "        pe = torch.zeros(max_len, d_model).float()\n",
+    "        pe.require_grad = False\n",
+    "\n",
+    "        for pos in range(max_len):   \n",
+    "            # for each dimension of the each position\n",
+    "            for i in range(0, d_model, 2):   \n",
+    "                pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))\n",
+    "                pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))\n",
+    "\n",
+    "        # include the batch size\n",
+    "        self.pe = pe.unsqueeze(0)   \n",
+    "        # self.register_buffer('pe', pe)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.pe\n",
+    "\n",
+    "class BERTEmbedding(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    BERT Embedding which is consisted with under features\n",
+    "        1. TokenEmbedding : normal embedding matrix\n",
+    "        2. PositionalEmbedding : adding positional information using sin, cos\n",
+    "        2. SegmentEmbedding : adding sentence segment info, (sent_A:1, sent_B:2)\n",
+    "        sum of all these features are output of BERTEmbedding\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, vocab_size, embed_size, seq_len=64, dropout=0.1):\n",
+    "        \"\"\"\n",
+    "        :param vocab_size: total vocab size\n",
+    "        :param embed_size: embedding size of token embedding\n",
+    "        :param dropout: dropout rate\n",
+    "        \"\"\"\n",
+    "\n",
+    "        super().__init__()\n",
+    "        self.embed_size = embed_size\n",
+    "        # (m, seq_len) --> (m, seq_len, embed_size)\n",
+    "        # padding_idx is not updated during training, remains as fixed pad (0)\n",
+    "        self.token = torch.nn.Embedding(vocab_size, embed_size, padding_idx=0)\n",
+    "        self.segment = torch.nn.Embedding(3, embed_size, padding_idx=0)\n",
+    "        self.position = PositionalEmbedding(d_model=embed_size, max_len=seq_len)\n",
+    "        self.dropout = torch.nn.Dropout(p=dropout)\n",
+    "       \n",
+    "    def forward(self, sequence, segment_label):\n",
+    "        x = self.token(sequence) + self.position(sequence) + self.segment(segment_label)\n",
+    "        return self.dropout(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "baa5caa0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### attention layers\n",
+    "class MultiHeadedAttention(torch.nn.Module):\n",
+    "    \n",
+    "    def __init__(self, heads, d_model, dropout=0.1):\n",
+    "        super(MultiHeadedAttention, self).__init__()\n",
+    "        \n",
+    "        assert d_model % heads == 0\n",
+    "        self.d_k = d_model // heads\n",
+    "        self.heads = heads\n",
+    "        self.dropout = torch.nn.Dropout(dropout)\n",
+    "\n",
+    "        self.query = torch.nn.Linear(d_model, d_model)\n",
+    "        self.key = torch.nn.Linear(d_model, d_model)\n",
+    "        self.value = torch.nn.Linear(d_model, d_model)\n",
+    "        self.output_linear = torch.nn.Linear(d_model, d_model)\n",
+    "        \n",
+    "    def forward(self, query, key, value, mask):\n",
+    "        \"\"\"\n",
+    "        query, key, value of shape: (batch_size, max_len, d_model)\n",
+    "        mask of shape: (batch_size, 1, 1, max_words)\n",
+    "        \"\"\"\n",
+    "        # (batch_size, max_len, d_model)\n",
+    "        query = self.query(query)\n",
+    "        key = self.key(key)        \n",
+    "        value = self.value(value)   \n",
+    "        \n",
+    "        # (batch_size, max_len, d_model) --> (batch_size, max_len, h, d_k) --> (batch_size, h, max_len, d_k)\n",
+    "        query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)   \n",
+    "        key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)  \n",
+    "        value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)  \n",
+    "        \n",
+    "        # (batch_size, h, max_len, d_k) matmul (batch_size, h, d_k, max_len) --> (batch_size, h, max_len, max_len)\n",
+    "        scores = torch.matmul(query, key.permute(0, 1, 3, 2)) / math.sqrt(query.size(-1))\n",
+    "\n",
+    "        # fill 0 mask with super small number so it wont affect the softmax weight\n",
+    "        # (batch_size, h, max_len, max_len)\n",
+    "        scores = scores.masked_fill(mask == 0, -1e9)    \n",
+    "\n",
+    "        # (batch_size, h, max_len, max_len)\n",
+    "        # softmax to put attention weight for all non-pad tokens\n",
+    "        # max_len X max_len matrix of attention\n",
+    "        weights = F.softmax(scores, dim=-1)           \n",
+    "        weights = self.dropout(weights)\n",
+    "\n",
+    "        # (batch_size, h, max_len, max_len) matmul (batch_size, h, max_len, d_k) --> (batch_size, h, max_len, d_k)\n",
+    "        context = torch.matmul(weights, value)\n",
+    "\n",
+    "        # (batch_size, h, max_len, d_k) --> (batch_size, max_len, h, d_k) --> (batch_size, max_len, d_model)\n",
+    "        context = context.permute(0, 2, 1, 3).contiguous().view(context.shape[0], -1, self.heads * self.d_k)\n",
+    "\n",
+    "        # (batch_size, max_len, d_model)\n",
+    "        return self.output_linear(context)\n",
+    "\n",
+    "class FeedForward(torch.nn.Module):\n",
+    "    \"Implements FFN equation.\"\n",
+    "\n",
+    "    def __init__(self, d_model, middle_dim=2048, dropout=0.1):\n",
+    "        super(FeedForward, self).__init__()\n",
+    "        \n",
+    "        self.fc1 = torch.nn.Linear(d_model, middle_dim)\n",
+    "        self.fc2 = torch.nn.Linear(middle_dim, d_model)\n",
+    "        self.dropout = torch.nn.Dropout(dropout)\n",
+    "        self.activation = torch.nn.GELU()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        out = self.activation(self.fc1(x))\n",
+    "        out = self.fc2(self.dropout(out))\n",
+    "        return out\n",
+    "\n",
+    "class EncoderLayer(torch.nn.Module):\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        d_model=768,\n",
+    "        heads=12, \n",
+    "        feed_forward_hidden=768 * 4, \n",
+    "        dropout=0.1\n",
+    "        ):\n",
+    "        super(EncoderLayer, self).__init__()\n",
+    "        self.layernorm = torch.nn.LayerNorm(d_model)\n",
+    "        self.self_multihead = MultiHeadedAttention(heads, d_model)\n",
+    "        self.feed_forward = FeedForward(d_model, middle_dim=feed_forward_hidden)\n",
+    "        self.dropout = torch.nn.Dropout(dropout)\n",
+    "\n",
+    "    def forward(self, embeddings, mask):\n",
+    "        # embeddings: (batch_size, max_len, d_model)\n",
+    "        # encoder mask: (batch_size, 1, 1, max_len)\n",
+    "        # result: (batch_size, max_len, d_model)\n",
+    "        interacted = self.dropout(self.self_multihead(embeddings, embeddings, embeddings, mask))\n",
+    "        # residual layer\n",
+    "        interacted = self.layernorm(interacted + embeddings)\n",
+    "        # bottleneck\n",
+    "        feed_forward_out = self.dropout(self.feed_forward(interacted))\n",
+    "        encoded = self.layernorm(feed_forward_out + interacted)\n",
+    "        return encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "33fe273b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BERT(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    BERT model : Bidirectional Encoder Representations from Transformers.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, vocab_size, d_model=768, n_layers=12, heads=12, dropout=0.1):\n",
+    "        \"\"\"\n",
+    "        :param vocab_size: vocab_size of total words\n",
+    "        :param hidden: BERT model hidden size\n",
+    "        :param n_layers: numbers of Transformer blocks(layers)\n",
+    "        :param attn_heads: number of attention heads\n",
+    "        :param dropout: dropout rate\n",
+    "        \"\"\"\n",
+    "\n",
+    "        super().__init__()\n",
+    "        self.d_model = d_model\n",
+    "        self.n_layers = n_layers\n",
+    "        self.heads = heads\n",
+    "\n",
+    "        # paper noted they used 4 * hidden_size for ff_network_hidden_size\n",
+    "        self.feed_forward_hidden = d_model * 4\n",
+    "\n",
+    "        # embedding for BERT, sum of positional, segment, token embeddings\n",
+    "        self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=d_model)\n",
+    "\n",
+    "        # multi-layers transformer blocks, deep network\n",
+    "        self.encoder_blocks = torch.nn.ModuleList(\n",
+    "            [EncoderLayer(d_model, heads, d_model * 4, dropout) for _ in range(n_layers)])\n",
+    "\n",
+    "    def forward(self, x, segment_info):\n",
+    "        # attention masking for padded token\n",
+    "        # (batch_size, 1, seq_len, seq_len)\n",
+    "        mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1)\n",
+    "\n",
+    "        # embedding the indexed sequence to sequence of vectors\n",
+    "        x = self.embedding(x, segment_info)\n",
+    "\n",
+    "        # running over multiple transformer blocks\n",
+    "        for encoder in self.encoder_blocks:\n",
+    "            x = encoder.forward(x, mask)\n",
+    "        return x\n",
+    "\n",
+    "class NextSentencePrediction(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    2-class classification model : is_next, is_not_next\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, hidden):\n",
+    "        \"\"\"\n",
+    "        :param hidden: BERT model output size\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        self.linear = torch.nn.Linear(hidden, 2)\n",
+    "        self.softmax = torch.nn.LogSoftmax(dim=-1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # use only the first token which is the [CLS]\n",
+    "        return self.softmax(self.linear(x[:, 0]))\n",
+    "\n",
+    "class MaskedLanguageModel(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    predicting origin token from masked input sequence\n",
+    "    n-class classification problem, n-class = vocab_size\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, hidden, vocab_size):\n",
+    "        \"\"\"\n",
+    "        :param hidden: output size of BERT model\n",
+    "        :param vocab_size: total vocab size\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        self.linear = torch.nn.Linear(hidden, vocab_size)\n",
+    "        self.softmax = torch.nn.LogSoftmax(dim=-1)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.softmax(self.linear(x))\n",
+    "\n",
+    "class BERTLM(torch.nn.Module):\n",
+    "    \"\"\"\n",
+    "    BERT Language Model\n",
+    "    Next Sentence Prediction Model + Masked Language Model\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, bert: BERT, vocab_size):\n",
+    "        \"\"\"\n",
+    "        :param bert: BERT model which should be trained\n",
+    "        :param vocab_size: total vocab size for masked_lm\n",
+    "        \"\"\"\n",
+    "\n",
+    "        super().__init__()\n",
+    "        self.bert = bert\n",
+    "        self.next_sentence = NextSentencePrediction(self.bert.d_model)\n",
+    "        self.mask_lm = MaskedLanguageModel(self.bert.d_model, vocab_size)\n",
+    "\n",
+    "    def forward(self, x, segment_label):\n",
+    "        x = self.bert(x, segment_label)\n",
+    "        return self.next_sentence(x), self.mask_lm(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b8f7c1f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ScheduledOptim():\n",
+    "    '''A simple wrapper class for learning rate scheduling'''\n",
+    "\n",
+    "    def __init__(self, optimizer, d_model, n_warmup_steps):\n",
+    "        self._optimizer = optimizer\n",
+    "        self.n_warmup_steps = n_warmup_steps\n",
+    "        self.n_current_steps = 0\n",
+    "        self.init_lr = np.power(d_model, -0.5)\n",
+    "\n",
+    "    def step_and_update_lr(self):\n",
+    "        \"Step with the inner optimizer\"\n",
+    "        self._update_learning_rate()\n",
+    "        self._optimizer.step()\n",
+    "\n",
+    "    def zero_grad(self):\n",
+    "        \"Zero out the gradients by the inner optimizer\"\n",
+    "        self._optimizer.zero_grad()\n",
+    "\n",
+    "    def _get_lr_scale(self):\n",
+    "        return np.min([\n",
+    "            np.power(self.n_current_steps, -0.5),\n",
+    "            np.power(self.n_warmup_steps, -1.5) * self.n_current_steps])\n",
+    "\n",
+    "    def _update_learning_rate(self):\n",
+    "        ''' Learning rate scheduling per step '''\n",
+    "\n",
+    "        self.n_current_steps += 1\n",
+    "        lr = self.init_lr * self._get_lr_scale()\n",
+    "\n",
+    "        for param_group in self._optimizer.param_groups:\n",
+    "            param_group['lr'] = lr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9dd8e50e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BERTTrainer:\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        model, \n",
+    "        train_dataloader, \n",
+    "        test_dataloader=None, \n",
+    "        lr= 1e-4,\n",
+    "        weight_decay=0.01,\n",
+    "        betas=(0.9, 0.999),\n",
+    "        warmup_steps=10000,\n",
+    "        log_freq=10,\n",
+    "        device='cuda'\n",
+    "        ):\n",
+    "\n",
+    "        self.device = device\n",
+    "        self.model = model\n",
+    "        self.train_data = train_dataloader\n",
+    "        self.test_data = test_dataloader\n",
+    "\n",
+    "        # Setting the Adam optimizer with hyper-param\n",
+    "        self.optim = Adam(self.model.parameters(), lr=lr, betas=betas, weight_decay=weight_decay)\n",
+    "        self.optim_schedule = ScheduledOptim(\n",
+    "            self.optim, self.model.bert.d_model, n_warmup_steps=warmup_steps\n",
+    "            )\n",
+    "\n",
+    "        # Using Negative Log Likelihood Loss function for predicting the masked_token\n",
+    "        self.criterion = torch.nn.NLLLoss(ignore_index=0)\n",
+    "        self.log_freq = log_freq\n",
+    "        print(\"Total Parameters:\", sum([p.nelement() for p in self.model.parameters()]))\n",
+    "    \n",
+    "    def train(self, epoch):\n",
+    "        self.iteration(epoch, self.train_data)\n",
+    "\n",
+    "    def test(self, epoch):\n",
+    "        self.iteration(epoch, self.test_data, train=False)\n",
+    "\n",
+    "    def iteration(self, epoch, data_loader, train=True):\n",
+    "        \n",
+    "        avg_loss = 0.0\n",
+    "        total_correct = 0\n",
+    "        total_element = 0\n",
+    "        \n",
+    "        mode = \"train\" if train else \"test\"\n",
+    "\n",
+    "        # progress bar\n",
+    "        data_iter = tqdm.tqdm(\n",
+    "            enumerate(data_loader),\n",
+    "            desc=\"EP_%s:%d\" % (mode, epoch),\n",
+    "            total=len(data_loader),\n",
+    "            bar_format=\"{l_bar}{r_bar}\"\n",
+    "        )\n",
+    "\n",
+    "        for i, data in data_iter:\n",
+    "\n",
+    "            # 0. batch_data will be sent into the device(GPU or cpu)\n",
+    "            data = {key: value.to(self.device) for key, value in data.items()}\n",
+    "\n",
+    "            # 1. forward the next_sentence_prediction and masked_lm model\n",
+    "            next_sent_output, mask_lm_output = self.model.forward(data[\"bert_input\"], data[\"segment_label\"])\n",
+    "\n",
+    "            # 2-1. NLL(negative log likelihood) loss of is_next classification result\n",
+    "            next_loss = self.criterion(next_sent_output, data[\"is_next\"])\n",
+    "\n",
+    "            # 2-2. NLLLoss of predicting masked token word\n",
+    "            # transpose to (m, vocab_size, seq_len) vs (m, seq_len)\n",
+    "            # criterion(mask_lm_output.view(-1, mask_lm_output.size(-1)), data[\"bert_label\"].view(-1))\n",
+    "            mask_loss = self.criterion(mask_lm_output.transpose(1, 2), data[\"bert_label\"])\n",
+    "\n",
+    "            # 2-3. Adding next_loss and mask_loss : 3.4 Pre-training Procedure\n",
+    "            loss = next_loss + mask_loss\n",
+    "\n",
+    "            # 3. backward and optimization only in train\n",
+    "            if train:\n",
+    "                self.optim_schedule.zero_grad()\n",
+    "                loss.backward()\n",
+    "                self.optim_schedule.step_and_update_lr()\n",
+    "\n",
+    "            # next sentence prediction accuracy\n",
+    "            correct = next_sent_output.argmax(dim=-1).eq(data[\"is_next\"]).sum().item()\n",
+    "            avg_loss += loss.item()\n",
+    "            total_correct += correct\n",
+    "            total_element += data[\"is_next\"].nelement()\n",
+    "\n",
+    "            post_fix = {\n",
+    "                \"epoch\": epoch,\n",
+    "                \"iter\": i,\n",
+    "                \"avg_loss\": avg_loss / (i + 1),\n",
+    "                \"avg_acc\": total_correct / total_element * 100,\n",
+    "                \"loss\": loss.item()\n",
+    "            }\n",
+    "\n",
+    "            if i % self.log_freq == 0:\n",
+    "                data_iter.write(str(post_fix))\n",
+    "        print(\n",
+    "            f\"EP{epoch}, {mode}: \\\n",
+    "            avg_loss={avg_loss / len(data_iter)}, \\\n",
+    "            total_acc={total_correct * 100.0 / total_element}\"\n",
+    "        ) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "52de2072",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Parameters: 46699434\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "EP_train:0:   0%|| 0/6926 [01:16<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[28], line 20\u001b[0m\n\u001b[0;32m     17\u001b[0m epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20\u001b[39m\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs):\n\u001b[1;32m---> 20\u001b[0m     \u001b[43mbert_trainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[25], line 32\u001b[0m, in \u001b[0;36mBERTTrainer.train\u001b[1;34m(self, epoch)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(\u001b[38;5;28mself\u001b[39m, epoch):\n\u001b[1;32m---> 32\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miteration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_data\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[25], line 75\u001b[0m, in \u001b[0;36mBERTTrainer.iteration\u001b[1;34m(self, epoch, data_loader, train)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m train:\n\u001b[0;32m     74\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_schedule\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m---> 75\u001b[0m     \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     76\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptim_schedule\u001b[38;5;241m.\u001b[39mstep_and_update_lr()\n\u001b[0;32m     78\u001b[0m \u001b[38;5;66;03m# next sentence prediction accuracy\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\lib\\site-packages\\torch\\_tensor.py:396\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    387\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    388\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m    389\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[0;32m    390\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    394\u001b[0m         create_graph\u001b[38;5;241m=\u001b[39mcreate_graph,\n\u001b[0;32m    395\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs)\n\u001b[1;32m--> 396\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\lib\\site-packages\\torch\\autograd\\__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    168\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[0;32m    170\u001b[0m \u001b[38;5;66;03m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[0;32m    171\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m    172\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 173\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    174\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    175\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "train_data = BERTDataset(\n",
+    "   pairs, seq_len=MAX_LEN, tokenizer=tokenizer)\n",
+    "\n",
+    "train_loader = DataLoader(\n",
+    "   train_data, batch_size=32, shuffle=True, pin_memory=True)\n",
+    "\n",
+    "bert_model = BERT(\n",
+    "  vocab_size=len(tokenizer.vocab),\n",
+    "  d_model=768,\n",
+    "  n_layers=2,\n",
+    "  heads=12,\n",
+    "  dropout=0.1\n",
+    ")\n",
+    "\n",
+    "bert_lm = BERTLM(bert_model, len(tokenizer.vocab))\n",
+    "bert_trainer = BERTTrainer(bert_lm, train_loader, device='cpu')\n",
+    "epochs = 20\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    bert_trainer.train(epoch)"
+   ]
+  }
+ ],
+ "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Time windows for submissions.ipynb

@@ -0,0 +1,233 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cd0be85b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39c3d028",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# columns: id, timestamp, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a7621921",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "bb10b577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts_list = []\n",
+    "with open(r\"C:\\Users\\yozhan\\Downloads\\Archive\\Bitcoin copy\\BitCoinsubmission4.csv\", \"r\", encoding=\"utf-8\") as infile:\n",
+    "    csv_reader = csv.reader(infile, delimiter=' ', quotechar='|')\n",
+    "    for row in csv_reader:\n",
+    "        ts = row[1]\n",
+    "        ts_list.append(ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe289999",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bitcoin\n",
+    "# submission1 2022-01-01 - 2022-03-01\n",
+    "# submission2 2022-03-01 - 2022-06-01\n",
+    "# submission3 2022-06-01 - 2022-09-01\n",
+    "# submission4 2022-09-01 - 2023-01-01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4fc8a065",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11927\n",
+      "2022-09-01 00:59:41\n",
+      "2023-01-01 23:51:53\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(ts_list))\n",
+    "print(datetime.fromtimestamp(float(min(ts_list))))\n",
+    "print(datetime.fromtimestamp(float(max(ts_list))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc4c9d89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ethereum\n",
+    "# submission1 2022-01-01 - 2022-03-01: 4481\n",
+    "# submission2 2022-03-01 - 2022-06-01: 3837\n",
+    "# submission3 2022-06-01 - 2022-09-01: 3172\n",
+    "# submission4 2022-09-01 - 2023-01-01: 4418"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "52ad83de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ts_list = []\n",
+    "with open(r\"C:\\Users\\yozhan\\Downloads\\Archive\\Etherum copy\\Ethereumsubmission4.csv\", \"r\", encoding=\"utf-8\") as infile:\n",
+    "    csv_reader = csv.reader(infile, delimiter=' ', quotechar='|')\n",
+    "    for row in csv_reader:\n",
+    "        ts = row[1]\n",
+    "        ts_list.append(ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "244fdfe5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4418\n",
+      "2022-09-01 01:02:11\n",
+      "2023-01-01 23:00:11\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(ts_list))\n",
+    "print(datetime.fromtimestamp(float(min(ts_list))))\n",
+    "print(datetime.fromtimestamp(float(max(ts_list))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bf7d52e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dogecoin\n",
+    "# submission1 2022-01-01 - 2022-03-01: 11126\n",
+    "# submission2 2022-03-01 - 2022-06-01: 21513\n",
+    "# submission3 2022-06-01 - 2022-09-01: 10098\n",
+    "# submission4 2022-09-01 - 2023-01-01: ??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "8fb1550b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\yozhan\\\\Downloads\\\\Archive\\\\Dogecoin copy\\\\Dogecoinsubmission4.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[40], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m ts_list \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mC:\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mUsers\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43myozhan\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDownloads\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mArchive\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDogecoin copy\u001b[39;49m\u001b[38;5;124;43m\\\u001b[39;49m\u001b[38;5;124;43mDogecoinsubmission4.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m infile:\n\u001b[0;32m      3\u001b[0m     csv_reader \u001b[38;5;241m=\u001b[39m csv\u001b[38;5;241m.\u001b[39mreader(infile, delimiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m, quotechar\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m|\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m csv_reader:\n",
+      "File \u001b[1;32m~\\AppData\\Local\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:282\u001b[0m, in \u001b[0;36m_modified_open\u001b[1;34m(file, *args, **kwargs)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[0;32m    276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    277\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    278\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    279\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    280\u001b[0m     )\n\u001b[1;32m--> 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m io_open(file, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\yozhan\\\\Downloads\\\\Archive\\\\Dogecoin copy\\\\Dogecoinsubmission4.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "ts_list = []\n",
+    "with open(r\"C:\\Users\\yozhan\\Downloads\\Archive\\Dogecoin copy\\Dogecoinsubmission4.csv\", \"r\", encoding=\"utf-8\") as infile:\n",
+    "    csv_reader = csv.reader(infile, delimiter=' ', quotechar='|')\n",
+    "    for row in csv_reader:\n",
+    "        ts = row[1]\n",
+    "        ts_list.append(ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "37792ef7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "min() arg is an empty sequence",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[41], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(ts_list))\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(datetime\u001b[38;5;241m.\u001b[39mfromtimestamp(\u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mts_list\u001b[49m\u001b[43m)\u001b[49m)))\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(datetime\u001b[38;5;241m.\u001b[39mfromtimestamp(\u001b[38;5;28mfloat\u001b[39m(\u001b[38;5;28mmax\u001b[39m(ts_list))))\n",
+      "\u001b[1;31mValueError\u001b[0m: min() arg is an empty sequence"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(ts_list))\n",
+    "print(datetime.fromtimestamp(float(min(ts_list))))\n",
+    "print(datetime.fromtimestamp(float(max(ts_list))))"
+   ]
+  }
+ ],
+ "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

crypto_data/test.jsonl

@@ -0,0 +1,10 @@
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crypto_data/val.jsonl

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tutorial/airline-passengers.csv

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tutorial/cornell_movie-dialogs_corpus/README.txt

@@ -0,0 +1,113 @@
+Cornell Movie-Dialogs Corpus
+
+Distributed together with:
+
+"Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs"
+Cristian Danescu-Niculescu-Mizil and Lillian Lee
+Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011.
+
+(this paper is included in this zip file)
+
+NOTE: If you have results to report on these corpora, please send email to [email protected] or [email protected] so we can add you to our list of people using this data.  Thanks!
+
+
+Contents of this README:
+
+	A) Brief description
+	B) Files description
+	C) Details on the collection procedure
+	D) Contact
+
+
+A) Brief description:
+
+This corpus contains a metadata-rich collection of fictional conversations extracted from raw movie scripts:
+
+- 220,579 conversational exchanges between 10,292 pairs of movie characters
+- involves 9,035 characters from 617 movies
+- in total 304,713 utterances
+- movie metadata included:
+	- genres
+	- release year
+	- IMDB rating
+	- number of IMDB votes
+	- IMDB rating
+- character metadata included:
+	- gender (for 3,774 characters)
+	- position on movie credits (3,321 characters)
+
+
+B) Files description:
+
+In all files the field separator is " +++$+++ "
+
+- movie_titles_metadata.txt
+	- contains information about each movie title
+	- fields: 
+		- movieID, 
+		- movie title,
+		- movie year, 
+	   	- IMDB rating,
+		- no. IMDB votes,
+ 		- genres in the format ['genre1','genre2',�,'genreN']
+
+- movie_characters_metadata.txt
+	- contains information about each movie character
+	- fields:
+		- characterID
+		- character name
+		- movieID
+		- movie title
+		- gender ("?" for unlabeled cases)
+		- position in credits ("?" for unlabeled cases) 
+
+- movie_lines.txt
+	- contains the actual text of each utterance
+	- fields:
+		- lineID
+		- characterID (who uttered this phrase)
+		- movieID
+		- character name
+		- text of the utterance
+
+- movie_conversations.txt
+	- the structure of the conversations
+	- fields
+		- characterID of the first character involved in the conversation
+		- characterID of the second character involved in the conversation
+		- movieID of the movie in which the conversation occurred
+		- list of the utterances that make the conversation, in chronological 
+			order: ['lineID1','lineID2',�,'lineIDN']
+			has to be matched with movie_lines.txt to reconstruct the actual content
+
+- raw_script_urls.txt
+	- the urls from which the raw sources were retrieved
+
+C) Details on the collection procedure:
+
+We started from raw publicly available movie scripts (sources acknowledged in 
+raw_script_urls.txt).  In order to collect the metadata necessary for this study 
+and to distinguish between two script versions of the same movie, we automatically
+ matched each script with an entry in movie database provided by IMDB (The Internet
+ Movie Database; data interfaces available at http://www.imdb.com/interfaces). Some
+ amount of manual correction was also involved. When  more than one movie with the same
+ title was found in IMBD, the match was made with the most popular title 
+(the one that received most IMDB votes)  
+
+After discarding all movies that could not be matched or that had less than 5 IMDB 
+votes, we were left with 617 unique titles with metadata including genre, release 
+year, IMDB rating and no. of IMDB votes and cast distribution.  We then identified 
+the pairs of characters that interact and separated their conversations automatically 
+using simple data processing heuristics. After discarding all pairs that exchanged 
+less than 5 conversational exchanges there were 10,292 left, exchanging 220,579 
+conversational exchanges (304,713 utterances).  After automatically matching the names 
+of the 9,035 involved characters to the list of cast distribution, we used the 
+gender of each interpreting actor to infer the fictional gender of a subset of 
+3,321 movie characters (we raised the number of gendered 3,774 characters through
+ manual annotation). Similarly, we collected the end credit position of a subset 
+of 3,321 characters as a proxy for their status.
+
+
+D) Contact:
+
+Please email any questions to: [email protected] (Cristian Danescu-Niculescu-Mizil)

tutorial/cornell_movie-dialogs_corpus/movie_lines.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59b9a78482829bc083d69cc9c164fc058eb4d0431712e09a4b08059663d0de56
+size 34641919

tutorial/cornell_movie-dialogs_corpus/movie_titles_metadata.txt

@@ -0,0 +1,617 @@
+m0 +++$+++ 10 things i hate about you +++$+++ 1999 +++$+++ 6.90 +++$+++ 62847 +++$+++ ['comedy', 'romance']
+m1 +++$+++ 1492: conquest of paradise +++$+++ 1992 +++$+++ 6.20 +++$+++ 10421 +++$+++ ['adventure', 'biography', 'drama', 'history']
+m2 +++$+++ 15 minutes +++$+++ 2001 +++$+++ 6.10 +++$+++ 25854 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m3 +++$+++ 2001: a space odyssey +++$+++ 1968 +++$+++ 8.40 +++$+++ 163227 +++$+++ ['adventure', 'mystery', 'sci-fi']
+m4 +++$+++ 48 hrs. +++$+++ 1982 +++$+++ 6.90 +++$+++ 22289 +++$+++ ['action', 'comedy', 'crime', 'drama', 'thriller']
+m5 +++$+++ the fifth element +++$+++ 1997 +++$+++ 7.50 +++$+++ 133756 +++$+++ ['action', 'adventure', 'romance', 'sci-fi', 'thriller']
+m6 +++$+++ 8mm +++$+++ 1999 +++$+++ 6.30 +++$+++ 48212 +++$+++ ['crime', 'mystery', 'thriller']
+m7 +++$+++ a nightmare on elm street 4: the dream master +++$+++ 1988 +++$+++ 5.20 +++$+++ 13590 +++$+++ ['fantasy', 'horror', 'thriller']
+m8 +++$+++ a nightmare on elm street: the dream child +++$+++ 1989 +++$+++ 4.70 +++$+++ 11092 +++$+++ ['fantasy', 'horror', 'thriller']
+m9 +++$+++ the atomic submarine +++$+++ 1959 +++$+++ 4.90 +++$+++ 513 +++$+++ ['sci-fi', 'thriller']
+m10 +++$+++ affliction +++$+++ 1997 +++$+++ 6.90 +++$+++ 7252 +++$+++ ['drama', 'mystery', 'thriller']
+m11 +++$+++ air force one +++$+++ 1997 +++$+++ 6.30 +++$+++ 61978 +++$+++ ['action', 'drama', 'thriller']
+m12 +++$+++ airplane ii: the sequel +++$+++ 1982 +++$+++ 5.80 +++$+++ 15210 +++$+++ ['comedy', 'romance', 'sci-fi']
+m13 +++$+++ airplane! +++$+++ 1980 +++$+++ 7.80 +++$+++ 57692 +++$+++ ['comedy', 'romance']
+m14 +++$+++ alien nation +++$+++ 1988 +++$+++ 6.10 +++$+++ 5590 +++$+++ ['crime', 'drama', 'sci-fi', 'thriller']
+m15 +++$+++ aliens +++$+++ 1986 +++$+++ 8.50 +++$+++ 173518 +++$+++ ['action', 'sci-fi', 'thriller']
+m16 +++$+++ amadeus +++$+++ 1984 +++$+++ 8.40 +++$+++ 99138 +++$+++ ['biography', 'drama', 'music']
+m17 +++$+++ an american werewolf in london +++$+++ 1981 +++$+++ 7.50 +++$+++ 24443 +++$+++ ['horror', 'romance']
+m18 +++$+++ american madness +++$+++ 1932 +++$+++ 7.40 +++$+++ 702 +++$+++ ['drama']
+m19 +++$+++ american outlaws +++$+++ 2001 +++$+++ 5.70 +++$+++ 7425 +++$+++ ['action', 'western']
+m20 +++$+++ american psycho +++$+++ 2000 +++$+++ 7.40 +++$+++ 101357 +++$+++ ['drama', 'thriller']
+m21 +++$+++ antitrust +++$+++ 2001 +++$+++ 6.00 +++$+++ 16453 +++$+++ ['drama', 'thriller']
+m22 +++$+++ austin powers: international man of mystery +++$+++ 1997 +++$+++ 7.10 +++$+++ 75240 +++$+++ ['action', 'adventure', 'comedy', 'crime']
+m23 +++$+++ the avengers +++$+++ 1998 +++$+++ 3.40 +++$+++ 21519 +++$+++ ['action', 'adventure', 'thriller']
+m24 +++$+++ bachelor party +++$+++ 1984 +++$+++ 5.90 +++$+++ 14050 +++$+++ ['comedy', 'romance']
+m25 +++$+++ backdraft +++$+++ 1991 +++$+++ 6.60 +++$+++ 28541 +++$+++ ['action', 'crime', 'drama', 'mystery', 'thriller']
+m26 +++$+++ bad lieutenant +++$+++ 1992 +++$+++ 6.90 +++$+++ 13908 +++$+++ ['crime', 'drama']
+m27 +++$+++ bamboozled +++$+++ 2000 +++$+++ 6.30 +++$+++ 5995 +++$+++ ['comedy', 'drama', 'music']
+m28 +++$+++ barry lyndon +++$+++ 1975 +++$+++ 8.10 +++$+++ 40622 +++$+++ ['drama', 'romance', 'war']
+m29 +++$+++ basic +++$+++ 2003 +++$+++ 6.30 +++$+++ 26295 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m30 +++$+++ big fish +++$+++ 2003 +++$+++ 8.10 +++$+++ 144264 +++$+++ ['adventure', 'drama', 'fantasy']
+m31 +++$+++ birthday girl +++$+++ 2001 +++$+++ 6.00 +++$+++ 13497 +++$+++ ['comedy', 'crime', 'romance', 'thriller']
+m32 +++$+++ black snake moan +++$+++ 2006 +++$+++ 7.10 +++$+++ 28509 +++$+++ ['drama']
+m33 +++$+++ black rain +++$+++ 1989/I +++$+++ 6.40 +++$+++ 16249 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m34 +++$+++ blade runner +++$+++ 1982 +++$+++ 8.30 +++$+++ 188735 +++$+++ ['drama', 'sci-fi', 'thriller']
+m35 +++$+++ blast from the past +++$+++ 1999 +++$+++ 6.40 +++$+++ 23489 +++$+++ ['comedy', 'drama', 'romance']
+m36 +++$+++ blue velvet +++$+++ 1986 +++$+++ 7.80 +++$+++ 54576 +++$+++ ['crime', 'mystery', 'thriller']
+m37 +++$+++ the boondock saints +++$+++ 1999 +++$+++ 7.80 +++$+++ 87895 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m38 +++$+++ bottle rocket +++$+++ 1996 +++$+++ 7.20 +++$+++ 22303 +++$+++ ['comedy', 'crime']
+m39 +++$+++ the bourne supremacy +++$+++ 2004 +++$+++ 7.60 +++$+++ 106349 +++$+++ ['action', 'adventure', 'mystery', 'thriller']
+m40 +++$+++ braveheart +++$+++ 1995 +++$+++ 8.40 +++$+++ 245652 +++$+++ ['action', 'biography', 'drama', 'history', 'war']
+m41 +++$+++ the butterfly effect +++$+++ 2004 +++$+++ 7.80 +++$+++ 104917 +++$+++ ['drama', 'mystery', 'sci-fi', 'thriller']
+m42 +++$+++ casablanca +++$+++ 1942 +++$+++ 8.80 +++$+++ 170874 +++$+++ ['drama', 'romance', 'war']
+m43 +++$+++ cast away +++$+++ 2000 +++$+++ 7.50 +++$+++ 104477 +++$+++ ['adventure', 'drama']
+m44 +++$+++ the cider house rules +++$+++ 1999 +++$+++ 7.50 +++$+++ 38836 +++$+++ ['drama', 'romance']
+m45 +++$+++ confidence +++$+++ 2003 +++$+++ 6.80 +++$+++ 17235 +++$+++ ['crime', 'thriller']
+m46 +++$+++ croupier +++$+++ 1998 +++$+++ 7.20 +++$+++ 9060 +++$+++ ['crime', 'drama']
+m47 +++$+++ dark star +++$+++ 1974 +++$+++ 6.50 +++$+++ 8303 +++$+++ ['comedy', 'sci-fi', 'thriller']
+m48 +++$+++ dark angel +++$+++ 1990 +++$+++ 5.30 +++$+++ 3452 +++$+++ ['action', 'crime', 'drama', 'horror', 'sci-fi', 'thriller']
+m49 +++$+++ detroit rock city +++$+++ 1999 +++$+++ 6.50 +++$+++ 14666 +++$+++ ['comedy', 'music']
+m50 +++$+++ donnie darko +++$+++ 2001 +++$+++ 8.30 +++$+++ 215194 +++$+++ ['drama', 'mystery', 'sci-fi']
+m51 +++$+++ drop dead gorgeous +++$+++ 1999 +++$+++ 6.30 +++$+++ 16417 +++$+++ ['comedy', 'adult']
+m52 +++$+++ duck soup +++$+++ 1933 +++$+++ 8.10 +++$+++ 27112 +++$+++ ['comedy', 'musical']
+m53 +++$+++ the elephant man +++$+++ 1980 +++$+++ 8.40 +++$+++ 59625 +++$+++ ['biography', 'drama', 'history']
+m54 +++$+++ erik the viking +++$+++ 1989 +++$+++ 5.90 +++$+++ 5889 +++$+++ ['comedy', 'adventure', 'fantasy']
+m55 +++$+++ eternal sunshine of the spotless mind +++$+++ 2004 +++$+++ 8.50 +++$+++ 224838 +++$+++ ['drama', 'romance', 'sci-fi']
+m56 +++$+++ even cowgirls get the blues +++$+++ 1993 +++$+++ 4.00 +++$+++ 3947 +++$+++ ['comedy', 'drama', 'romance', 'western']
+m57 +++$+++ event horizon +++$+++ 1997 +++$+++ 6.40 +++$+++ 45497 +++$+++ ['horror', 'mystery', 'mystery', 'sci-fi', 'sci-fi']
+m58 +++$+++ fantastic four +++$+++ 2005 +++$+++ 5.70 +++$+++ 72581 +++$+++ ['action', 'adventure', 'fantasy', 'sci-fi', 'fantasy']
+m59 +++$+++ fast times at ridgemont high +++$+++ 1982 +++$+++ 7.20 +++$+++ 31710 +++$+++ ['comedy', 'drama', 'romance']
+m60 +++$+++ fear and loathing in las vegas +++$+++ 1998 +++$+++ 7.60 +++$+++ 82748 +++$+++ ['drama', 'fantasy']
+m61 +++$+++ feast +++$+++ 2005 +++$+++ 6.40 +++$+++ 12248 +++$+++ ['action', 'comedy', 'horror', 'thriller']
+m62 +++$+++ frances +++$+++ 1982 +++$+++ 7.20 +++$+++ 2756 +++$+++ ['biography', 'drama']
+m63 +++$+++ frankenstein +++$+++ 1931 +++$+++ 8.00 +++$+++ 23522 +++$+++ ['drama', 'horror', 'sci-fi']
+m64 +++$+++ friday the 13th +++$+++ 2009 +++$+++ 5.60 +++$+++ 27499 +++$+++ ['horror']
+m65 +++$+++ from dusk till dawn +++$+++ 1996 +++$+++ 7.10 +++$+++ 81559 +++$+++ ['action', 'crime', 'horror', 'thriller']
+m66 +++$+++ g.i. jane +++$+++ 1997 +++$+++ 5.60 +++$+++ 24068 +++$+++ ['action', 'drama']
+m67 +++$+++ godzilla +++$+++ 1998 +++$+++ 4.90 +++$+++ 60310 +++$+++ ['action', 'horror', 'sci-fi', 'thriller']
+m68 +++$+++ galaxy quest +++$+++ 1999 +++$+++ 7.20 +++$+++ 53004 +++$+++ ['action', 'adventure', 'comedy', 'sci-fi']
+m69 +++$+++ george washington +++$+++ 2000 +++$+++ 7.50 +++$+++ 3142 +++$+++ ['drama']
+m70 +++$+++ get shorty +++$+++ 1995 +++$+++ 6.90 +++$+++ 33588 +++$+++ ['comedy', 'crime', 'thriller']
+m71 +++$+++ ghost ship +++$+++ 2002 +++$+++ 5.30 +++$+++ 26413 +++$+++ ['horror', 'mystery', 'thriller']
+m72 +++$+++ ghost world +++$+++ 2001 +++$+++ 7.70 +++$+++ 43550 +++$+++ ['comedy', 'drama']
+m73 +++$+++ the ghost and the darkness +++$+++ 1996 +++$+++ 6.60 +++$+++ 19954 +++$+++ ['adventure', 'drama', 'thriller']
+m74 +++$+++ ghostbusters ii +++$+++ 1989 +++$+++ 6.10 +++$+++ 45450 +++$+++ ['action', 'adventure', 'comedy', 'fantasy', 'sci-fi', 'action', 'adventure', 'comedy', 'sci-fi']
+m75 +++$+++ ghostbusters +++$+++ 1986 +++$+++ 6.90 +++$+++ 237 +++$+++ ['animation', 'comedy', 'fantasy', 'sci-fi', 'horror']
+m76 +++$+++ gladiator +++$+++ 2000 +++$+++ 8.40 +++$+++ 286067 +++$+++ ['action', 'adventure', 'drama']
+m77 +++$+++ the graduate +++$+++ 1967 +++$+++ 8.20 +++$+++ 79677 +++$+++ ['comedy', 'drama', 'romance']
+m78 +++$+++ grand hotel +++$+++ 1932 +++$+++ 7.70 +++$+++ 6088 +++$+++ ['drama', 'romance']
+m79 +++$+++ the grifters +++$+++ 1990 +++$+++ 7.00 +++$+++ 11481 +++$+++ ['crime', 'drama', 'thriller']
+m80 +++$+++ halloween h20: 20 years later +++$+++ 1998 +++$+++ 5.40 +++$+++ 22353 +++$+++ ['drama', 'horror', 'thriller']
+m81 +++$+++ halloween: the curse of michael myers +++$+++ 1995 +++$+++ 4.50 +++$+++ 8743 +++$+++ ['horror', 'thriller']
+m82 +++$+++ happy birthday, wanda june +++$+++ 1971 +++$+++ 5.80 +++$+++ 144 +++$+++ ['comedy', 'drama']
+m83 +++$+++ hardcore +++$+++ 1979 +++$+++ 6.70 +++$+++ 2455 +++$+++ ['drama']
+m84 +++$+++ harold and maude +++$+++ 1971 +++$+++ 8.10 +++$+++ 28689 +++$+++ ['comedy', 'romance']
+m85 +++$+++ hellbound: hellraiser ii +++$+++ 1988 +++$+++ 6.20 +++$+++ 11273 +++$+++ ['drama', 'horror', 'thriller']
+m86 +++$+++ hellboy +++$+++ 2004 +++$+++ 6.80 +++$+++ 68619 +++$+++ ['action', 'adventure', 'fantasy']
+m87 +++$+++ hellraiser +++$+++ 1987 +++$+++ 7.00 +++$+++ 22922 +++$+++ ['horror']
+m88 +++$+++ high fidelity +++$+++ 2000 +++$+++ 7.60 +++$+++ 70464 +++$+++ ['comedy', 'drama', 'music', 'romance']
+m89 +++$+++ highlander +++$+++ 1986 +++$+++ 7.20 +++$+++ 41243 +++$+++ ['action', 'fantasy']
+m90 +++$+++ his girl friday +++$+++ 1940 +++$+++ 8.10 +++$+++ 20870 +++$+++ ['comedy', 'drama', 'romance']
+m91 +++$+++ hope and glory +++$+++ 1987 +++$+++ 7.40 +++$+++ 4965 +++$+++ ['drama', 'war']
+m92 +++$+++ house of 1000 corpses +++$+++ 2003 +++$+++ 5.60 +++$+++ 27033 +++$+++ ['horror']
+m93 +++$+++ human nature +++$+++ 2001 +++$+++ 6.30 +++$+++ 10017 +++$+++ ['comedy', 'drama']
+m94 +++$+++ the hustler +++$+++ 1961 +++$+++ 8.20 +++$+++ 25871 +++$+++ ['drama', 'romance', 'sport']
+m95 +++$+++ i am legend +++$+++ 2007 +++$+++ 7.10 +++$+++ 156084 +++$+++ ['drama', 'sci-fi', 'thriller']
+m96 +++$+++ invaders from mars +++$+++ 1953 +++$+++ 6.40 +++$+++ 2115 +++$+++ ['horror', 'sci-fi']
+m97 +++$+++ independence day +++$+++ 1996 +++$+++ 6.60 +++$+++ 151698 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m98 +++$+++ indiana jones and the last crusade +++$+++ 1989 +++$+++ 8.30 +++$+++ 174947 +++$+++ ['action', 'adventure', 'thriller', 'action', 'adventure', 'fantasy']
+m99 +++$+++ indiana jones and the temple of doom +++$+++ 1984 +++$+++ 7.50 +++$+++ 112054 +++$+++ ['action', 'adventure']
+m100 +++$+++ innerspace +++$+++ 1987 +++$+++ 6.50 +++$+++ 16854 +++$+++ ['action', 'adventure', 'comedy', 'crime', 'sci-fi']
+m101 +++$+++ the insider +++$+++ 1999 +++$+++ 8.00 +++$+++ 69660 +++$+++ ['biography', 'drama', 'thriller']
+m102 +++$+++ intolerable cruelty +++$+++ 2003 +++$+++ 6.40 +++$+++ 36739 +++$+++ ['comedy', 'romance', 'crime']
+m103 +++$+++ it happened one night +++$+++ 1934 +++$+++ 8.30 +++$+++ 25577 +++$+++ ['comedy', 'romance']
+m104 +++$+++ jfk +++$+++ 1991 +++$+++ 8.00 +++$+++ 60406 +++$+++ ['biography', 'drama', 'history', 'mystery', 'thriller']
+m105 +++$+++ jackie brown +++$+++ 1997 +++$+++ 7.60 +++$+++ 85496 +++$+++ ['crime', 'drama', 'thriller']
+m106 +++$+++ jacob's ladder +++$+++ 1990/I +++$+++ 7.50 +++$+++ 29182 +++$+++ ['drama', 'mystery', 'thriller']
+m107 +++$+++ jason x +++$+++ 2001 +++$+++ 4.40 +++$+++ 18199 +++$+++ ['horror', 'sci-fi']
+m108 +++$+++ jaws +++$+++ 1975 +++$+++ 8.30 +++$+++ 140982 +++$+++ ['thriller']
+m109 +++$+++ juno +++$+++ 2007 +++$+++ 7.90 +++$+++ 152436 +++$+++ ['comedy', 'drama']
+m110 +++$+++ kalifornia +++$+++ 1993 +++$+++ 6.70 +++$+++ 21830 +++$+++ ['crime', 'drama', 'thriller']
+m111 +++$+++ kids +++$+++ 1995 +++$+++ 6.80 +++$+++ 26546 +++$+++ ['crime', 'drama']
+m112 +++$+++ knight moves +++$+++ 1992 +++$+++ 5.80 +++$+++ 3272 +++$+++ ['mystery', 'thriller']
+m113 +++$+++ krull +++$+++ 1983 +++$+++ 5.90 +++$+++ 8246 +++$+++ ['fantasy', 'action', 'adventure', 'sci-fi']
+m114 +++$+++ l�on +++$+++ 1994 +++$+++ 8.60 +++$+++ 204901 +++$+++ ['crime', 'drama', 'thriller']
+m115 +++$+++ labor of love +++$+++ 1998 +++$+++ 4.50 +++$+++ 45 +++$+++ []
+m116 +++$+++ leaving las vegas +++$+++ 1995 +++$+++ 7.60 +++$+++ 42919 +++$+++ ['drama', 'romance']
+m117 +++$+++ legally blonde +++$+++ 2001 +++$+++ 6.20 +++$+++ 44558 +++$+++ ['comedy']
+m118 +++$+++ legend +++$+++ 1985 +++$+++ 6.20 +++$+++ 20907 +++$+++ ['fantasy', 'adventure', 'romance']
+m119 +++$+++ life as a house +++$+++ 2001 +++$+++ 7.50 +++$+++ 19468 +++$+++ ['drama', 'romance']
+m120 +++$+++ the life of david gale +++$+++ 2003 +++$+++ 7.30 +++$+++ 38237 +++$+++ ['drama', 'crime', 'thriller']
+m121 +++$+++ little nicky +++$+++ 2000 +++$+++ 5.00 +++$+++ 35489 +++$+++ ['fantasy', 'comedy', 'romance', 'animation', 'comedy', 'fantasy', 'horror']
+m122 +++$+++ logan's run +++$+++ 1976 +++$+++ 6.70 +++$+++ 15152 +++$+++ ['action', 'sci-fi']
+m123 +++$+++ lost highway +++$+++ 1997 +++$+++ 7.60 +++$+++ 42998 +++$+++ ['drama', 'horror', 'mystery', 'thriller']
+m124 +++$+++ lost horizon +++$+++ 1937 +++$+++ 7.80 +++$+++ 5509 +++$+++ ['adventure', 'drama', 'fantasy', 'mystery']
+m125 +++$+++ men in black +++$+++ 1997 +++$+++ 7.00 +++$+++ 121719 +++$+++ ['action', 'adventure', 'comedy', 'sci-fi']
+m126 +++$+++ minority report +++$+++ 2002 +++$+++ 7.70 +++$+++ 137009 +++$+++ ['action', 'crime', 'mystery', 'sci-fi', 'thriller', 'action', 'animation', 'drama', 'mystery', 'sci-fi', 'thriller']
+m127 +++$+++ made +++$+++ 2001 +++$+++ 6.40 +++$+++ 9774 +++$+++ ['comedy', 'crime', 'drama', 'thriller']
+m128 +++$+++ malcolm x +++$+++ 1992 +++$+++ 7.70 +++$+++ 23317 +++$+++ ['biography', 'drama', 'history']
+m129 +++$+++ man on fire +++$+++ 2004 +++$+++ 7.70 +++$+++ 76517 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m130 +++$+++ marty +++$+++ 1955 +++$+++ 7.70 +++$+++ 6379 +++$+++ ['drama', 'romance']
+m131 +++$+++ mash +++$+++ 1970 +++$+++ 7.80 +++$+++ 29358 +++$+++ ['comedy', 'war', 'drama']
+m132 +++$+++ meet john doe +++$+++ 1941 +++$+++ 7.70 +++$+++ 4841 +++$+++ ['comedy', 'drama', 'romance']
+m133 +++$+++ metro +++$+++ 1997 +++$+++ 5.30 +++$+++ 9601 +++$+++ ['action', 'comedy', 'crime', 'drama', 'thriller']
+m134 +++$+++ metropolis +++$+++ 1927 +++$+++ 8.40 +++$+++ 40730 +++$+++ ['adventure', 'drama', 'sci-fi']
+m135 +++$+++ mighty morphin power rangers +++$+++ 1994 +++$+++ 8.20 +++$+++ 35 +++$+++ ['action', 'family']
+m136 +++$+++ mobsters +++$+++ 1991 +++$+++ 5.50 +++$+++ 3574 +++$+++ ['crime', 'drama']
+m137 +++$+++ monkeybone +++$+++ 2001 +++$+++ 4.50 +++$+++ 8258 +++$+++ ['animation', 'comedy', 'fantasy', 'romance']
+m138 +++$+++ my mother dreams the satan's disciples in new york +++$+++ 1998 +++$+++ 6.70 +++$+++ 70 +++$+++ ['short']
+m139 +++$+++ mr. smith goes to washington +++$+++ 1939 +++$+++ 8.40 +++$+++ 33984 +++$+++ ['drama']
+m140 +++$+++ mr. deeds goes to town +++$+++ 1936 +++$+++ 8.00 +++$+++ 7594 +++$+++ ['comedy', 'romance']
+m141 +++$+++ mumford +++$+++ 1999 +++$+++ 6.80 +++$+++ 6333 +++$+++ ['comedy', 'drama']
+m142 +++$+++ the mummy +++$+++ 1999 +++$+++ 6.90 +++$+++ 96736 +++$+++ ['action', 'adventure', 'fantasy']
+m143 +++$+++ mystery men +++$+++ 1999 +++$+++ 5.90 +++$+++ 31817 +++$+++ ['action', 'comedy', 'fantasy']
+m144 +++$+++ napoleon +++$+++ 1995 +++$+++ 5.50 +++$+++ 486 +++$+++ ['family', 'adventure']
+m145 +++$+++ next friday +++$+++ 2000 +++$+++ 5.40 +++$+++ 10267 +++$+++ ['comedy']
+m146 +++$+++ nick of time +++$+++ 1995 +++$+++ 6.20 +++$+++ 15945 +++$+++ ['crime', 'drama', 'thriller']
+m147 +++$+++ the night of the hunter +++$+++ 1955 +++$+++ 8.20 +++$+++ 26211 +++$+++ ['drama', 'film-noir', 'thriller']
+m148 +++$+++ a nightmare on elm street +++$+++ 1984 +++$+++ 7.40 +++$+++ 47161 +++$+++ ['horror']
+m149 +++$+++ ninotchka +++$+++ 1939 +++$+++ 7.90 +++$+++ 6951 +++$+++ ['comedy', 'romance']
+m150 +++$+++ nixon +++$+++ 1995 +++$+++ 7.10 +++$+++ 13878 +++$+++ ['biography', 'drama']
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+m153 +++$+++ o brother, where art thou? +++$+++ 2000 +++$+++ 7.80 +++$+++ 95628 +++$+++ ['comedy', 'adventure', 'crime', 'music']
+m154 +++$+++ an officer and a gentleman +++$+++ 1982 +++$+++ 6.80 +++$+++ 16883 +++$+++ ['drama', 'romance']
+m155 +++$+++ panic room +++$+++ 2002 +++$+++ 6.90 +++$+++ 69824 +++$+++ ['thriller']
+m156 +++$+++ panther +++$+++ 1995/I +++$+++ 5.90 +++$+++ 1129 +++$+++ ['drama']
+m157 +++$+++ the patriot +++$+++ 2000 +++$+++ 6.90 +++$+++ 79207 +++$+++ ['action', 'drama', 'war']
+m158 +++$+++ pet sematary +++$+++ 1989 +++$+++ 6.30 +++$+++ 19851 +++$+++ ['drama', 'fantasy', 'horror', 'mystery', 'thriller']
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+m160 +++$+++ plastic man +++$+++ 1999 +++$+++ 6.70 +++$+++ 29 +++$+++ ['drama']
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+m162 +++$+++ pleasantville +++$+++ 1998 +++$+++ 7.50 +++$+++ 49669 +++$+++ ['comedy', 'drama', 'fantasy']
+m163 +++$+++ punch-drunk love +++$+++ 2002 +++$+++ 7.40 +++$+++ 50312 +++$+++ ['comedy', 'drama', 'romance']
+m164 +++$+++ quills +++$+++ 2000 +++$+++ 7.30 +++$+++ 22657 +++$+++ ['biography', 'drama', 'history', 'romance']
+m165 +++$+++ rko 281 +++$+++ 1999 +++$+++ 7.10 +++$+++ 3543 +++$+++ ['biography', 'drama']
+m166 +++$+++ raging bull +++$+++ 1980 +++$+++ 8.40 +++$+++ 91851 +++$+++ ['biography', 'drama', 'sport']
+m167 +++$+++ rear window +++$+++ 1954 +++$+++ 8.70 +++$+++ 121165 +++$+++ ['crime', 'mystery', 'romance', 'thriller']
+m168 +++$+++ rebel without a cause +++$+++ 1955 +++$+++ 7.90 +++$+++ 27791 +++$+++ ['drama', 'romance']
+m169 +++$+++ reindeer games +++$+++ 2000 +++$+++ 5.50 +++$+++ 16951 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m170 +++$+++ reservoir dogs +++$+++ 1992 +++$+++ 8.40 +++$+++ 217185 +++$+++ ['crime', 'mystery', 'thriller']
+m171 +++$+++ roughshod +++$+++ 1949 +++$+++ 6.90 +++$+++ 115 +++$+++ ['action', 'drama', 'romance', 'western']
+m172 +++$+++ scary movie 2 +++$+++ 2001 +++$+++ 4.70 +++$+++ 44511 +++$+++ ['comedy']
+m173 +++$+++ serial mom +++$+++ 1994 +++$+++ 6.40 +++$+++ 11077 +++$+++ ['comedy', 'thriller']
+m174 +++$+++ the seventh victim +++$+++ 1943 +++$+++ 6.90 +++$+++ 1596 +++$+++ ['drama', 'horror', 'thriller']
+m175 +++$+++ sex, lies, and videotape +++$+++ 1989 +++$+++ 7.10 +++$+++ 18505 +++$+++ ['drama']
+m176 +++$+++ shivers +++$+++ 1975 +++$+++ 6.50 +++$+++ 4940 +++$+++ ['horror', 'sci-fi']
+m177 +++$+++ shock treatment +++$+++ 1981 +++$+++ 5.40 +++$+++ 1884 +++$+++ ['comedy', 'musical']
+m178 +++$+++ sideways +++$+++ 2004 +++$+++ 7.80 +++$+++ 70349 +++$+++ ['comedy', 'drama', 'romance']
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+m182 +++$+++ the sixth sense +++$+++ 1999 +++$+++ 8.20 +++$+++ 244162 +++$+++ ['drama', 'mystery', 'thriller']
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+m187 +++$+++ solaris +++$+++ 2002 +++$+++ 6.20 +++$+++ 33541 +++$+++ ['drama', 'mystery', 'romance', 'sci-fi']
+m188 +++$+++ someone to watch over me +++$+++ 1987 +++$+++ 6.10 +++$+++ 3909 +++$+++ ['action', 'crime', 'drama', 'romance', 'thriller']
+m189 +++$+++ spider-man +++$+++ 2002 +++$+++ 7.40 +++$+++ 169898 +++$+++ ['action', 'adventure', 'fantasy', 'sci-fi', 'action', 'adventure', 'sci-fi', 'thriller']
+m190 +++$+++ stalag 17 +++$+++ 1953 +++$+++ 8.20 +++$+++ 21182 +++$+++ ['drama', 'war']
+m191 +++$+++ star trek: generations +++$+++ 1994 +++$+++ 6.50 +++$+++ 26662 +++$+++ ['action', 'mystery', 'sci-fi', 'thriller']
+m192 +++$+++ star trek iii: the search for spock +++$+++ 1984 +++$+++ 6.50 +++$+++ 22466 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m193 +++$+++ star trek: the wrath of khan +++$+++ 1982 +++$+++ 7.80 +++$+++ 36503 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m194 +++$+++ star trek iv: the voyage home +++$+++ 1986 +++$+++ 7.30 +++$+++ 26423 +++$+++ ['adventure', 'comedy', 'sci-fi']
+m195 +++$+++ star trek: insurrection +++$+++ 1998 +++$+++ 6.30 +++$+++ 26728 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m196 +++$+++ star trek: first contact +++$+++ 1996 +++$+++ 7.60 +++$+++ 45429 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m197 +++$+++ star trek vi: the undiscovered country +++$+++ 1991 +++$+++ 7.20 +++$+++ 23751 +++$+++ ['action', 'mystery', 'sci-fi', 'thriller']
+m198 +++$+++ star trek: nemesis +++$+++ 2002 +++$+++ 6.40 +++$+++ 28682 +++$+++ ['action', 'sci-fi', 'thriller']
+m199 +++$+++ starman +++$+++ 1984 +++$+++ 6.90 +++$+++ 14204 +++$+++ ['adventure', 'drama', 'romance', 'sci-fi']
+m200 +++$+++ strange days +++$+++ 1995 +++$+++ 7.10 +++$+++ 27744 +++$+++ ['action', 'crime', 'drama', 'fantasy', 'music', 'mystery', 'sci-fi', 'thriller']
+m201 +++$+++ suspect zero +++$+++ 2004 +++$+++ 5.80 +++$+++ 9914 +++$+++ ['crime', 'horror', 'sci-fi', 'thriller']
+m202 +++$+++ swingers +++$+++ 1996 +++$+++ 7.60 +++$+++ 32427 +++$+++ ['comedy', 'drama']
+m203 +++$+++ the godfather +++$+++ 1972 +++$+++ 9.20 +++$+++ 419312 +++$+++ ['crime', 'drama', 'thriller']
+m204 +++$+++ the talented mr. ripley +++$+++ 1999 +++$+++ 7.20 +++$+++ 64055 +++$+++ ['crime', 'drama', 'thriller']
+m205 +++$+++ taxi driver +++$+++ 1976 +++$+++ 8.60 +++$+++ 159525 +++$+++ ['drama', 'thriller']
+m206 +++$+++ the rock +++$+++ 1996 +++$+++ 7.30 +++$+++ 109533 +++$+++ ['action', 'adventure', 'thriller']
+m207 +++$+++ the majestic +++$+++ 2001 +++$+++ 6.80 +++$+++ 25057 +++$+++ ['drama', 'romance']
+m208 +++$+++ the birds +++$+++ 1963 +++$+++ 7.90 +++$+++ 53233 +++$+++ ['horror', 'romance', 'thriller']
+m209 +++$+++ the body snatcher +++$+++ 1945 +++$+++ 7.40 +++$+++ 2927 +++$+++ ['drama', 'horror', 'thriller']
+m210 +++$+++ the crow: salvation +++$+++ 2000 +++$+++ 4.70 +++$+++ 4566 +++$+++ ['action', 'crime', 'fantasy', 'mystery', 'thriller']
+m211 +++$+++ the day the earth stood still +++$+++ 2008 +++$+++ 5.50 +++$+++ 52489 +++$+++ ['drama', 'sci-fi', 'thriller']
+m212 +++$+++ the lost boys +++$+++ 1987 +++$+++ 7.00 +++$+++ 34826 +++$+++ ['comedy', 'fantasy', 'horror', 'thriller']
+m213 +++$+++ the thing +++$+++ 1982 +++$+++ 8.20 +++$+++ 80904 +++$+++ ['horror', 'mystery', 'sci-fi', 'thriller']
+m214 +++$+++ the time machine +++$+++ 2002 +++$+++ 5.70 +++$+++ 32900 +++$+++ ['sci-fi', 'adventure', 'action']
+m215 +++$+++ the jacket +++$+++ 2005 +++$+++ 7.10 +++$+++ 36581 +++$+++ ['drama', 'fantasy', 'mystery', 'sci-fi', 'thriller']
+m216 +++$+++ thelma & louise +++$+++ 1991 +++$+++ 7.30 +++$+++ 44372 +++$+++ ['crime', 'drama', 'thriller']
+m217 +++$+++ there's something about mary +++$+++ 1998 +++$+++ 7.20 +++$+++ 97546 +++$+++ ['comedy', 'romance']
+m218 +++$+++ thirteen days +++$+++ 2000 +++$+++ 7.30 +++$+++ 23732 +++$+++ ['drama', 'history', 'thriller']
+m219 +++$+++ thunderheart +++$+++ 1992 +++$+++ 6.60 +++$+++ 6662 +++$+++ ['crime', 'mystery', 'thriller', 'western']
+m220 +++$+++ top gun +++$+++ 1986 +++$+++ 6.50 +++$+++ 81087 +++$+++ ['action', 'drama', 'romance']
+m221 +++$+++ total recall +++$+++ 1990 +++$+++ 7.40 +++$+++ 71383 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m222 +++$+++ tremors +++$+++ 1990 +++$+++ 7.10 +++$+++ 30553 +++$+++ ['action', 'comedy', 'horror', 'thriller']
+m223 +++$+++ true believer +++$+++ 1989 +++$+++ 6.70 +++$+++ 1801 +++$+++ ['drama', 'crime']
+m224 +++$+++ twin peaks: fire walk with me +++$+++ 1992 +++$+++ 7.00 +++$+++ 21274 +++$+++ ['drama', 'horror', 'mystery', 'thriller']
+m225 +++$+++ the verdict +++$+++ 1982 +++$+++ 7.70 +++$+++ 10991 +++$+++ ['drama']
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+m227 +++$+++ new nightmare +++$+++ 1994 +++$+++ 6.30 +++$+++ 14975 +++$+++ ['fantasy', 'horror', 'mystery', 'thriller']
+m228 +++$+++ white squall +++$+++ 1996 +++$+++ 6.40 +++$+++ 8465 +++$+++ ['adventure', 'drama']
+m229 +++$+++ wild at heart +++$+++ 1990 +++$+++ 7.20 +++$+++ 29388 +++$+++ ['crime', 'romance', 'thriller']
+m230 +++$+++ wonder boys +++$+++ 2000 +++$+++ 7.50 +++$+++ 31853 +++$+++ ['drama', 'comedy']
+m231 +++$+++ the woodsman +++$+++ 2004 +++$+++ 7.40 +++$+++ 16090 +++$+++ ['drama']
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+m233 +++$+++ l'avventura +++$+++ 1960 +++$+++ 7.90 +++$+++ 6963 +++$+++ ['drama', 'mystery']
+m234 +++$+++ agnes of god +++$+++ 1985 +++$+++ 6.50 +++$+++ 3490 +++$+++ ['drama', 'mystery', 'thriller']
+m235 +++$+++ a hard day's night +++$+++ 1964 +++$+++ 7.60 +++$+++ 15431 +++$+++ ['comedy', 'music']
+m236 +++$+++ alien +++$+++ 1979 +++$+++ 8.50 +++$+++ 184471 +++$+++ ['adventure', 'horror', 'sci-fi', 'thriller']
+m237 +++$+++ alien vs. predator +++$+++ 1993 +++$+++ 7.80 +++$+++ 116 +++$+++ ['action', 'adventure', 'sci-fi']
+m238 +++$+++ all about eve +++$+++ 1950 +++$+++ 8.50 +++$+++ 35597 +++$+++ ['drama']
+m239 +++$+++ all the president's men +++$+++ 1976 +++$+++ 8.00 +++$+++ 32717 +++$+++ ['drama', 'history', 'thriller']
+m240 +++$+++ american pie +++$+++ 1999 +++$+++ 6.90 +++$+++ 107961 +++$+++ ['comedy', 'romance']
+m241 +++$+++ an american werewolf in paris +++$+++ 1997 +++$+++ 4.90 +++$+++ 10158 +++$+++ ['horror', 'romance', 'thriller', 'comedy']
+m242 +++$+++ anastasia +++$+++ 1997 +++$+++ 6.60 +++$+++ 16844 +++$+++ ['animation', 'adventure', 'drama', 'family', 'musical', 'animation', 'family']
+m243 +++$+++ annie hall +++$+++ 1977 +++$+++ 8.30 +++$+++ 66781 +++$+++ ['comedy', 'drama', 'romance']
+m244 +++$+++ the anniversary party +++$+++ 2001 +++$+++ 6.20 +++$+++ 5809 +++$+++ ['drama', 'comedy']
+m245 +++$+++ antz +++$+++ 1998 +++$+++ 6.80 +++$+++ 37825 +++$+++ ['animation', 'adventure', 'comedy', 'family']
+m246 +++$+++ the apartment +++$+++ 1960 +++$+++ 8.40 +++$+++ 37301 +++$+++ ['romance', 'comedy', 'drama']
+m247 +++$+++ apocalypse now +++$+++ 1979 +++$+++ 8.60 +++$+++ 176465 +++$+++ ['drama', 'war']
+m248 +++$+++ arctic blue +++$+++ 1993 +++$+++ 4.80 +++$+++ 464 +++$+++ ['action', 'thriller']
+m249 +++$+++ as good as it gets +++$+++ 1997 +++$+++ 7.80 +++$+++ 93201 +++$+++ ['comedy', 'drama', 'romance']
+m250 +++$+++ assassins +++$+++ 1995 +++$+++ 6.00 +++$+++ 23681 +++$+++ ['action', 'thriller', 'crime']
+m251 +++$+++ asylum +++$+++ 2005 +++$+++ 6.10 +++$+++ 2395 +++$+++ ['drama', 'romance', 'thriller']
+m252 +++$+++ a walk to remember +++$+++ 2002 +++$+++ 7.10 +++$+++ 38751 +++$+++ ['drama', 'romance']
+m253 +++$+++ back to the future +++$+++ 1985 +++$+++ 8.40 +++$+++ 207376 +++$+++ ['adventure', 'family', 'sci-fi']
+m254 +++$+++ badlands +++$+++ 1973 +++$+++ 7.90 +++$+++ 16753 +++$+++ ['crime', 'drama', 'romance', 'thriller']
+m255 +++$+++ the adventures of buckaroo banzai across the 8th dimension +++$+++ 1984 +++$+++ 6.00 +++$+++ 10046 +++$+++ ['adventure', 'romance', 'comedy', 'sci-fi']
+m256 +++$+++ barton fink +++$+++ 1991 +++$+++ 7.80 +++$+++ 33119 +++$+++ ['drama', 'mystery', 'thriller']
+m257 +++$+++ basic instinct +++$+++ 1992 +++$+++ 6.90 +++$+++ 57615 +++$+++ ['mystery', 'romance', 'thriller']
+m258 +++$+++ basquiat +++$+++ 1996 +++$+++ 6.70 +++$+++ 8016 +++$+++ ['biography', 'drama']
+m259 +++$+++ batman returns +++$+++ 1992 +++$+++ 6.90 +++$+++ 79770 +++$+++ ['action', 'crime', 'fantasy', 'thriller', 'animation', 'crime', 'fantasy', 'action', 'thriller']
+m260 +++$+++ batman and robin +++$+++ 1949 +++$+++ 6.50 +++$+++ 443 +++$+++ ['action', 'adventure', 'crime', 'drama', 'sci-fi']
+m261 +++$+++ batman forever +++$+++ 1995 +++$+++ 5.40 +++$+++ 77223 +++$+++ ['action', 'crime', 'fantasy', 'thriller']
+m262 +++$+++ batman +++$+++ 1989 +++$+++ 7.60 +++$+++ 112731 +++$+++ ['crime', 'drama', 'thriller', 'action', 'adventure', 'crime']
+m263 +++$+++ bean +++$+++ 1997 +++$+++ 5.80 +++$+++ 27131 +++$+++ ['comedy', 'family']
+m264 +++$+++ beavis and butt-head do america +++$+++ 1996 +++$+++ 6.60 +++$+++ 23149 +++$+++ ['animation', 'adventure', 'comedy', 'crime']
+m265 +++$+++ beetle juice +++$+++ 1988 +++$+++ 7.30 +++$+++ 62164 +++$+++ ['comedy', 'fantasy']
+m266 +++$+++ being there +++$+++ 1979 +++$+++ 8.00 +++$+++ 25085 +++$+++ ['drama', 'comedy']
+m267 +++$+++ being john malkovich +++$+++ 1999 +++$+++ 7.90 +++$+++ 115008 +++$+++ ['comedy', 'drama', 'fantasy', 'romance']
+m268 +++$+++ beloved +++$+++ 1998/I +++$+++ 5.60 +++$+++ 4014 +++$+++ ['drama', 'mystery']
+m269 +++$+++ the big lebowski +++$+++ 1998 +++$+++ 8.20 +++$+++ 182170 +++$+++ ['comedy', 'crime', 'mystery']
+m270 +++$+++ the black dahlia +++$+++ 2006 +++$+++ 5.60 +++$+++ 35706 +++$+++ ['crime', 'drama', 'history', 'mystery', 'thriller']
+m271 +++$+++ blade ii +++$+++ 2002 +++$+++ 6.60 +++$+++ 51826 +++$+++ ['action', 'fantasy', 'horror', 'thriller', 'action', 'adventure', 'horror']
+m272 +++$+++ blade +++$+++ 1998 +++$+++ 7.00 +++$+++ 65885 +++$+++ ['action', 'adventure', 'fantasy', 'horror', 'thriller']
+m273 +++$+++ book of shadows: blair witch 2 +++$+++ 2000 +++$+++ 4.10 +++$+++ 16285 +++$+++ ['crime', 'horror', 'mystery', 'thriller']
+m274 +++$+++ blood simple. +++$+++ 1984 +++$+++ 7.80 +++$+++ 28901 +++$+++ ['crime', 'drama', 'thriller']
+m275 +++$+++ bloodmoon +++$+++ 1997 +++$+++ 4.50 +++$+++ 269 +++$+++ ['action', 'thriller']
+m276 +++$+++ blow +++$+++ 2001 +++$+++ 7.40 +++$+++ 71334 +++$+++ ['biography', 'crime', 'drama']
+m277 +++$+++ la battaglia di algeri +++$+++ 1966 +++$+++ 8.20 +++$+++ 14339 +++$+++ ['drama', 'history', 'war']
+m278 +++$+++ body of evidence +++$+++ 1993 +++$+++ 4.10 +++$+++ 6052 +++$+++ ['drama', 'romance', 'thriller']
+m279 +++$+++ the bridges of madison county +++$+++ 1995 +++$+++ 7.20 +++$+++ 22252 +++$+++ ['drama', 'romance']
+m280 +++$+++ bones +++$+++ 2001 +++$+++ 4.00 +++$+++ 3525 +++$+++ ['crime', 'horror']
+m281 +++$+++ bound +++$+++ 1996 +++$+++ 7.50 +++$+++ 23772 +++$+++ ['crime', 'drama', 'thriller']
+m282 +++$+++ the bourne identity +++$+++ 2002 +++$+++ 7.70 +++$+++ 124918 +++$+++ ['action', 'adventure', 'mystery', 'thriller']
+m283 +++$+++ brazil +++$+++ 1985 +++$+++ 8.00 +++$+++ 77350 +++$+++ ['drama', 'fantasy', 'sci-fi']
+m284 +++$+++ bringing out the dead +++$+++ 1999 +++$+++ 6.80 +++$+++ 31285 +++$+++ ['drama', 'thriller']
+m285 +++$+++ broadcast news +++$+++ 1987 +++$+++ 7.10 +++$+++ 10559 +++$+++ ['comedy', 'drama', 'romance']
+m286 +++$+++ a bucket of blood +++$+++ 1959 +++$+++ 6.80 +++$+++ 1892 +++$+++ ['comedy', 'horror']
+m287 +++$+++ buffy the vampire slayer +++$+++ 1992 +++$+++ 5.30 +++$+++ 16206 +++$+++ ['horror', 'comedy', 'action']
+m288 +++$+++ bull durham +++$+++ 1988 +++$+++ 7.00 +++$+++ 18941 +++$+++ ['comedy', 'romance', 'sport']
+m289 +++$+++ casino +++$+++ 1995 +++$+++ 8.10 +++$+++ 111223 +++$+++ ['biography', 'crime', 'drama']
+m290 +++$+++ catwoman +++$+++ 2004 +++$+++ 3.20 +++$+++ 35065 +++$+++ ['action', 'crime', 'fantasy']
+m291 +++$+++ cellular +++$+++ 2004 +++$+++ 6.50 +++$+++ 32920 +++$+++ ['action', 'crime', 'thriller']
+m292 +++$+++ the crying game +++$+++ 1992 +++$+++ 7.30 +++$+++ 21394 +++$+++ ['drama', 'thriller']
+m293 +++$+++ charade +++$+++ 1963 +++$+++ 8.00 +++$+++ 22815 +++$+++ ['comedy', 'mystery', 'romance', 'thriller']
+m294 +++$+++ cherry falls +++$+++ 2000 +++$+++ 4.80 +++$+++ 5669 +++$+++ ['horror', 'mystery', 'thriller']
+m295 +++$+++ chill factor +++$+++ 1999 +++$+++ 4.90 +++$+++ 5381 +++$+++ ['action', 'adventure', 'comedy', 'thriller']
+m296 +++$+++ chinatown +++$+++ 1974 +++$+++ 8.50 +++$+++ 80698 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m297 +++$+++ nuovo cinema paradiso +++$+++ 1988 +++$+++ 8.50 +++$+++ 46801 +++$+++ ['comedy', 'drama', 'romance']
+m298 +++$+++ citizen kane +++$+++ 1941 +++$+++ 8.60 +++$+++ 140517 +++$+++ ['drama', 'mystery']
+m299 +++$+++ clerks. +++$+++ 1994 +++$+++ 8.00 +++$+++ 90972 +++$+++ ['comedy']
+m300 +++$+++ cliffhanger +++$+++ 1993 +++$+++ 6.20 +++$+++ 34880 +++$+++ ['action', 'adventure', 'thriller']
+m301 +++$+++ a clockwork orange +++$+++ 1971 +++$+++ 8.50 +++$+++ 197372 +++$+++ ['crime', 'drama', 'sci-fi']
+m302 +++$+++ collateral +++$+++ 2004 +++$+++ 7.80 +++$+++ 106866 +++$+++ ['crime', 'drama', 'thriller']
+m303 +++$+++ conspiracy theory +++$+++ 1997 +++$+++ 6.50 +++$+++ 36045 +++$+++ ['action', 'crime', 'mystery', 'romance', 'thriller']
+m304 +++$+++ contact +++$+++ 1997 +++$+++ 7.40 +++$+++ 75043 +++$+++ ['drama', 'mystery', 'sci-fi', 'thriller']
+m305 +++$+++ cool hand luke +++$+++ 1967 +++$+++ 8.30 +++$+++ 46514 +++$+++ ['crime', 'drama']
+m306 +++$+++ copycat +++$+++ 1995 +++$+++ 6.50 +++$+++ 17335 +++$+++ ['crime', 'mystery', 'thriller']
+m307 +++$+++ crash +++$+++ 2004/I +++$+++ 8.00 +++$+++ 174003 +++$+++ ['crime', 'drama']
+m308 +++$+++ crazy love +++$+++ 2007/I +++$+++ 7.00 +++$+++ 944 +++$+++ ['documentary']
+m309 +++$+++ crime spree +++$+++ 2003 +++$+++ 6.40 +++$+++ 3129 +++$+++ ['action', 'comedy', 'crime']
+m310 +++$+++ crouching tiger, hidden dragon +++$+++ 2003 +++$+++ 8.00 +++$+++ 1091 +++$+++ ['action']
+m311 +++$+++ the crow +++$+++ 1994 +++$+++ 7.60 +++$+++ 55886 +++$+++ ['action', 'crime', 'fantasy', 'thriller']
+m312 +++$+++ cruel intentions +++$+++ 1999 +++$+++ 6.70 +++$+++ 67532 +++$+++ ['drama', 'romance', 'thriller']
+m313 +++$+++ the curse of the cat people +++$+++ 1944 +++$+++ 6.90 +++$+++ 2067 +++$+++ ['drama', 'fantasy', 'horror']
+m314 +++$+++ the curse +++$+++ 1987 +++$+++ 4.50 +++$+++ 627 +++$+++ ['sci-fi', 'horror']
+m315 +++$+++ dark city +++$+++ 1998 +++$+++ 7.80 +++$+++ 64262 +++$+++ ['mystery', 'sci-fi', 'thriller']
+m316 +++$+++ dave +++$+++ 1993 +++$+++ 6.80 +++$+++ 20807 +++$+++ ['comedy', 'romance']
+m317 +++$+++ day of the dead +++$+++ 1985 +++$+++ 7.00 +++$+++ 22746 +++$+++ ['horror', 'sci-fi']
+m318 +++$+++ dead poets society +++$+++ 1989 +++$+++ 7.80 +++$+++ 90842 +++$+++ ['drama']
+m319 +++$+++ deep rising +++$+++ 1998 +++$+++ 5.70 +++$+++ 12638 +++$+++ ['action', 'horror', 'sci-fi', 'thriller']
+m320 +++$+++ the deer hunter +++$+++ 1978 +++$+++ 8.20 +++$+++ 89841 +++$+++ ['drama', 'war']
+m321 +++$+++ demolition man +++$+++ 1993 +++$+++ 6.30 +++$+++ 46318 +++$+++ ['action', 'crime', 'sci-fi']
+m322 +++$+++ the devil and daniel webster +++$+++ 2004 +++$+++ 5.70 +++$+++ 1824 +++$+++ ['comedy', 'drama', 'fantasy']
+m323 +++$+++ die hard +++$+++ 1988 +++$+++ 8.30 +++$+++ 185430 +++$+++ ['action', 'crime', 'thriller']
+m324 +++$+++ dog day afternoon +++$+++ 1975 +++$+++ 8.20 +++$+++ 61120 +++$+++ ['crime', 'drama']
+m325 +++$+++ domino +++$+++ 2005 +++$+++ 5.90 +++$+++ 32949 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m326 +++$+++ do the right thing +++$+++ 1989 +++$+++ 7.90 +++$+++ 27164 +++$+++ ['drama']
+m327 +++$+++ dumb and dumberer: when harry met lloyd +++$+++ 2003 +++$+++ 3.30 +++$+++ 14984 +++$+++ ['comedy']
+m328 +++$+++ dune +++$+++ 1984 +++$+++ 6.50 +++$+++ 38954 +++$+++ ['action', 'adventure', 'sci-fi']
+m329 +++$+++ ed wood +++$+++ 1994 +++$+++ 8.10 +++$+++ 75704 +++$+++ ['biography', 'comedy', 'drama']
+m330 +++$+++ edtv +++$+++ 1999 +++$+++ 6.10 +++$+++ 21865 +++$+++ ['comedy']
+m331 +++$+++ election +++$+++ 1999 +++$+++ 7.40 +++$+++ 37868 +++$+++ ['comedy', 'drama']
+m332 +++$+++ l.a. confidential +++$+++ 1997 +++$+++ 8.40 +++$+++ 168009 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m333 +++$+++ enemy of the state +++$+++ 1998 +++$+++ 7.20 +++$+++ 67504 +++$+++ ['action', 'drama', 'thriller']
+m334 +++$+++ the english patient +++$+++ 1996 +++$+++ 7.30 +++$+++ 55247 +++$+++ ['romance', 'drama', 'war']
+m335 +++$+++ entrapment +++$+++ 1999 +++$+++ 6.10 +++$+++ 41120 +++$+++ ['action', 'crime', 'romance', 'thriller']
+m336 +++$+++ erin brockovich +++$+++ 2000 +++$+++ 7.20 +++$+++ 55641 +++$+++ ['biography', 'drama', 'romance']
+m337 +++$+++ star wars: the empire strikes back +++$+++ 1982 +++$+++ 8.00 +++$+++ 42 +++$+++ ['animation', 'adventure', 'action', 'fantasy']
+m338 +++$+++ escape from the planet of the apes +++$+++ 1971 +++$+++ 6.10 +++$+++ 7754 +++$+++ ['sci-fi', 'thriller']
+m339 +++$+++ escape from l.a. +++$+++ 1996 +++$+++ 5.30 +++$+++ 23551 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m340 +++$+++ excalibur +++$+++ 1981 +++$+++ 7.40 +++$+++ 24550 +++$+++ ['adventure', 'drama', 'fantasy']
+m341 +++$+++ the exorcist +++$+++ 1973 +++$+++ 8.10 +++$+++ 106383 +++$+++ ['horror', 'thriller']
+m342 +++$+++ the fabulous baker boys +++$+++ 1989 +++$+++ 6.70 +++$+++ 9433 +++$+++ ['drama', 'music', 'romance']
+m343 +++$+++ face/off +++$+++ 1997 +++$+++ 7.30 +++$+++ 103386 +++$+++ ['action', 'crime', 'drama', 'sci-fi', 'thriller']
+m344 +++$+++ the family man +++$+++ 2000 +++$+++ 6.60 +++$+++ 34509 +++$+++ ['comedy', 'fantasy', 'drama', 'romance']
+m345 +++$+++ the fantastic four +++$+++ 1994 +++$+++ 3.80 +++$+++ 1472 +++$+++ ['action', 'adventure', 'sci-fi', 'fantasy']
+m346 +++$+++ fantastic voyage +++$+++ 1966 +++$+++ 6.80 +++$+++ 5231 +++$+++ ['adventure', 'sci-fi']
+m347 +++$+++ fargo +++$+++ 1996 +++$+++ 8.30 +++$+++ 168176 +++$+++ ['crime', 'drama', 'thriller']
+m348 +++$+++ fight club +++$+++ 1999 +++$+++ 8.80 +++$+++ 391697 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m349 +++$+++ final destination 2 +++$+++ 2003 +++$+++ 6.40 +++$+++ 36312 +++$+++ ['horror', 'thriller']
+m350 +++$+++ final destination +++$+++ 2000 +++$+++ 6.80 +++$+++ 53422 +++$+++ ['horror', 'thriller']
+m351 +++$+++ rambo: first blood part ii +++$+++ 1985 +++$+++ 5.80 +++$+++ 39147 +++$+++ ['action', 'adventure', 'thriller']
+m352 +++$+++ the fisher king +++$+++ 1991 +++$+++ 7.50 +++$+++ 28310 +++$+++ ['comedy', 'drama', 'romance']
+m353 +++$+++ five easy pieces +++$+++ 1970 +++$+++ 7.50 +++$+++ 11969 +++$+++ ['drama']
+m354 +++$+++ five feet high and rising +++$+++ 2000 +++$+++ 6.50 +++$+++ 326 +++$+++ ['short']
+m355 +++$+++ fletch +++$+++ 1985 +++$+++ 6.70 +++$+++ 17336 +++$+++ ['comedy', 'crime', 'mystery']
+m356 +++$+++ the adventures of ford fairlane +++$+++ 1990 +++$+++ 5.60 +++$+++ 9051 +++$+++ ['action', 'adventure', 'comedy', 'music']
+m357 +++$+++ the french connection +++$+++ 1971 +++$+++ 7.90 +++$+++ 34062 +++$+++ ['action', 'crime', 'thriller']
+m358 +++$+++ frequency +++$+++ 2000 +++$+++ 7.30 +++$+++ 36396 +++$+++ ['crime', 'drama', 'sci-fi', 'thriller']
+m359 +++$+++ friday the 13th part iii +++$+++ 1982 +++$+++ 5.10 +++$+++ 11718 +++$+++ ['horror']
+m360 +++$+++ jason lives: friday the 13th part vi +++$+++ 1986 +++$+++ 5.30 +++$+++ 9919 +++$+++ ['comedy', 'horror', 'thriller']
+m361 +++$+++ jason goes to hell: the final friday +++$+++ 1993 +++$+++ 4.10 +++$+++ 8894 +++$+++ ['horror', 'thriller']
+m362 +++$+++ friday the 13th part viii: jason takes manhattan +++$+++ 1989 +++$+++ 3.90 +++$+++ 10288 +++$+++ ['horror']
+m363 +++$+++ game 6 +++$+++ 2005 +++$+++ 6.00 +++$+++ 1414 +++$+++ ['comedy', 'drama', 'sport']
+m364 +++$+++ gandhi +++$+++ 1982 +++$+++ 8.20 +++$+++ 52160 +++$+++ ['biography', 'drama', 'history']
+m365 +++$+++ gattaca +++$+++ 1997 +++$+++ 7.80 +++$+++ 72063 +++$+++ ['drama', 'romance', 'sci-fi', 'thriller']
+m366 +++$+++ the getaway +++$+++ 1972 +++$+++ 7.50 +++$+++ 10151 +++$+++ ['action', 'adventure', 'crime', 'thriller']
+m367 +++$+++ get carter +++$+++ 2000 +++$+++ 4.80 +++$+++ 14319 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m368 +++$+++ glengarry glen ross +++$+++ 1992 +++$+++ 7.90 +++$+++ 32882 +++$+++ ['drama']
+m369 +++$+++ the godfather: part ii +++$+++ 1974 +++$+++ 9.00 +++$+++ 251290 +++$+++ ['crime', 'drama', 'thriller']
+m370 +++$+++ gods and monsters +++$+++ 1998 +++$+++ 7.50 +++$+++ 16063 +++$+++ ['biography', 'drama']
+m371 +++$+++ gone in sixty seconds +++$+++ 2000 +++$+++ 6.00 +++$+++ 74474 +++$+++ ['action', 'crime', 'thriller']
+m372 +++$+++ goodfellas +++$+++ 1990 +++$+++ 8.80 +++$+++ 234582 +++$+++ ['crime', 'drama', 'thriller']
+m373 +++$+++ good will hunting +++$+++ 1997 +++$+++ 8.10 +++$+++ 154419 +++$+++ ['drama']
+m374 +++$+++ the grapes of wrath +++$+++ 1940 +++$+++ 8.30 +++$+++ 23850 +++$+++ ['drama']
+m375 +++$+++ grosse pointe blank +++$+++ 1997 +++$+++ 7.40 +++$+++ 41877 +++$+++ ['comedy', 'crime', 'romance', 'thriller']
+m376 +++$+++ the horse whisperer +++$+++ 1998 +++$+++ 6.40 +++$+++ 15953 +++$+++ ['drama', 'romance', 'western']
+m377 +++$+++ hackers +++$+++ 1995 +++$+++ 5.80 +++$+++ 27009 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m378 +++$+++ halloween 4: the return of michael myers +++$+++ 1988 +++$+++ 5.60 +++$+++ 11315 +++$+++ ['horror', 'thriller']
+m379 +++$+++ halloween +++$+++ 1978 +++$+++ 7.90 +++$+++ 64690 +++$+++ ['horror', 'thriller']
+m380 +++$+++ hannah and her sisters +++$+++ 1986 +++$+++ 7.90 +++$+++ 21088 +++$+++ ['comedy', 'drama', 'romance']
+m381 +++$+++ hannibal +++$+++ 2001 +++$+++ 6.40 +++$+++ 75788 +++$+++ ['crime', 'thriller']
+m382 +++$+++ happy campers +++$+++ 2001 +++$+++ 5.40 +++$+++ 1785 +++$+++ ['comedy']
+m383 +++$+++ heathers +++$+++ 1989 +++$+++ 7.30 +++$+++ 25236 +++$+++ ['comedy', 'crime']
+m384 +++$+++ heavenly creatures +++$+++ 1994 +++$+++ 7.60 +++$+++ 27211 +++$+++ ['crime', 'drama', 'fantasy', 'thriller']
+m385 +++$+++ hellraiser: hellseeker +++$+++ 2002 +++$+++ 4.90 +++$+++ 3400 +++$+++ ['horror', 'mystery', 'thriller']
+m386 +++$+++ hero +++$+++ 1992/I +++$+++ 6.30 +++$+++ 10573 +++$+++ ['comedy', 'drama']
+m387 +++$+++ hider in the house +++$+++ 1989 +++$+++ 5.60 +++$+++ 394 +++$+++ ['thriller']
+m388 +++$+++ highlander iii: the sorcerer +++$+++ 1994 +++$+++ 3.90 +++$+++ 7831 +++$+++ ['fantasy', 'sci-fi', 'action']
+m389 +++$+++ hostage +++$+++ 2005/I +++$+++ 6.60 +++$+++ 37868 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m390 +++$+++ hotel rwanda +++$+++ 2004 +++$+++ 8.30 +++$+++ 94404 +++$+++ ['adventure', 'drama', 'history', 'thriller', 'war']
+m391 +++$+++ house on haunted hill +++$+++ 1999 +++$+++ 5.30 +++$+++ 23127 +++$+++ ['horror', 'mystery', 'thriller']
+m392 +++$+++ house of the damned +++$+++ 1996 +++$+++ 4.30 +++$+++ 216 +++$+++ ['action', 'horror', 'thriller']
+m393 +++$+++ hellraiser iii: hell on earth +++$+++ 1992 +++$+++ 5.10 +++$+++ 7716 +++$+++ ['horror']
+m394 +++$+++ hudson hawk +++$+++ 1991 +++$+++ 5.40 +++$+++ 22116 +++$+++ ['action', 'adventure', 'comedy', 'action', 'adventure', 'comedy', 'crime']
+m395 +++$+++ the hudsucker proxy +++$+++ 1994 +++$+++ 7.40 +++$+++ 32691 +++$+++ ['comedy', 'drama', 'fantasy', 'romance']
+m396 +++$+++ i walked with a zombie +++$+++ 1943 +++$+++ 7.30 +++$+++ 3420 +++$+++ ['horror']
+m397 +++$+++ the ice storm +++$+++ 1997 +++$+++ 7.50 +++$+++ 27724 +++$+++ ['drama']
+m398 +++$+++ insomnia +++$+++ 2002/I +++$+++ 7.30 +++$+++ 69635 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m399 +++$+++ interview with the vampire: the vampire chronicles +++$+++ 1994 +++$+++ 7.40 +++$+++ 80077 +++$+++ ['drama', 'fantasy']
+m400 +++$+++ i still know what you did last summer +++$+++ 1998 +++$+++ 4.10 +++$+++ 23543 +++$+++ ['horror', 'mystery', 'thriller']
+m401 +++$+++ isle of the dead +++$+++ 1945 +++$+++ 6.60 +++$+++ 1405 +++$+++ ['drama', 'horror', 'mystery', 'thriller']
+m402 +++$+++ it's a wonderful life +++$+++ 1946 +++$+++ 8.70 +++$+++ 103290 +++$+++ ['drama', 'fantasy', 'romance']
+m403 +++$+++ jaws 2 +++$+++ 1978 +++$+++ 5.60 +++$+++ 18995 +++$+++ ['thriller']
+m404 +++$+++ jaws 3-d +++$+++ 1983 +++$+++ 3.30 +++$+++ 12839 +++$+++ ['thriller']
+m405 +++$+++ jaws: the revenge +++$+++ 1987 +++$+++ 2.60 +++$+++ 15727 +++$+++ ['thriller']
+m406 +++$+++ the jazz singer +++$+++ 1927 +++$+++ 6.80 +++$+++ 3252 +++$+++ ['drama', 'music', 'romance']
+m407 +++$+++ jennifer eight +++$+++ 1992 +++$+++ 6.20 +++$+++ 6224 +++$+++ ['thriller', 'mystery']
+m408 +++$+++ jerry maguire +++$+++ 1996 +++$+++ 7.30 +++$+++ 79706 +++$+++ ['comedy', 'drama', 'romance', 'sport']
+m409 +++$+++ jurassic park iii +++$+++ 2001 +++$+++ 5.70 +++$+++ 62564 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m410 +++$+++ the lost world: jurassic park +++$+++ 1997 +++$+++ 6.00 +++$+++ 78169 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller', 'action', 'adventure', 'animation', 'horror', 'sci-fi', 'thriller']
+m411 +++$+++ jurassic park +++$+++ 1993 +++$+++ 7.90 +++$+++ 153737 +++$+++ ['action', 'adventure', 'family', 'sci-fi', 'action', 'adventure', 'fantasy', 'sci-fi']
+m412 +++$+++ freddy vs. jason +++$+++ 2003 +++$+++ 5.80 +++$+++ 39850 +++$+++ ['horror', 'thriller']
+m413 +++$+++ kafka +++$+++ 1991 +++$+++ 6.90 +++$+++ 4467 +++$+++ ['comedy', 'drama', 'mystery', 'sci-fi', 'thriller']
+m414 +++$+++ king kong +++$+++ 2005 +++$+++ 7.60 +++$+++ 134187 +++$+++ ['adventure', 'drama', 'romance']
+m415 +++$+++ klute +++$+++ 1971 +++$+++ 7.20 +++$+++ 6901 +++$+++ ['mystery', 'romance', 'thriller']
+m416 +++$+++ kramer vs. kramer +++$+++ 1979 +++$+++ 7.70 +++$+++ 25836 +++$+++ ['drama']
+m417 +++$+++ kundun +++$+++ 1997 +++$+++ 7.10 +++$+++ 10378 +++$+++ ['biography', 'drama', 'history', 'war']
+m418 +++$+++ lake placid +++$+++ 1999 +++$+++ 5.40 +++$+++ 19621 +++$+++ ['action', 'comedy', 'horror', 'thriller']
+m419 +++$+++ the silence of the lambs +++$+++ 1991 +++$+++ 8.70 +++$+++ 251237 +++$+++ ['crime', 'thriller']
+m420 +++$+++ last of the mohicans +++$+++ 1977 +++$+++ 7.30 +++$+++ 71 +++$+++ ['action', 'adventure', 'war']
+m421 +++$+++ leviathan +++$+++ 1989 +++$+++ 5.30 +++$+++ 4751 +++$+++ ['adventure', 'horror', 'mystery', 'sci-fi', 'thriller']
+m422 +++$+++ lock, stock and two smoking barrels +++$+++ 1998 +++$+++ 8.20 +++$+++ 129117 +++$+++ ['crime', 'thriller']
+m423 +++$+++ lone star +++$+++ 1996 +++$+++ 7.60 +++$+++ 14666 +++$+++ ['drama', 'mystery', 'romance', 'western']
+m424 +++$+++ lord of illusions +++$+++ 1995 +++$+++ 5.60 +++$+++ 5031 +++$+++ ['fantasy', 'horror', 'mystery', 'thriller']
+m425 +++$+++ lost in translation +++$+++ 2003 +++$+++ 7.90 +++$+++ 132645 +++$+++ ['drama', 'romance']
+m426 +++$+++ lost souls +++$+++ 2000 +++$+++ 4.60 +++$+++ 6663 +++$+++ ['drama', 'horror', 'thriller', 'romance']
+m427 +++$+++ love & basketball +++$+++ 2000 +++$+++ 6.70 +++$+++ 5907 +++$+++ ['drama', 'romance', 'sport']
+m428 +++$+++ magnolia +++$+++ 1999 +++$+++ 8.00 +++$+++ 123476 +++$+++ ['drama']
+m429 +++$+++ the man in the iron mask +++$+++ 1998/I +++$+++ 6.10 +++$+++ 36684 +++$+++ ['action', 'adventure', 'drama', 'history']
+m430 +++$+++ manhunt +++$+++ 2003 +++$+++ 8.70 +++$+++ 736 +++$+++ ['action', 'adventure', 'crime', 'horror', 'thriller']
+m431 +++$+++ manhunter +++$+++ 1986 +++$+++ 7.20 +++$+++ 21649 +++$+++ ['crime', 'thriller']
+m432 +++$+++ man on the moon +++$+++ 1999 +++$+++ 7.40 +++$+++ 49915 +++$+++ ['biography', 'comedy', 'drama']
+m433 +++$+++ the matrix +++$+++ 1999 +++$+++ 8.70 +++$+++ 389480 +++$+++ ['action', 'adventure', 'adventure', 'adventure', 'sci-fi']
+m434 +++$+++ halloween iii: season of the witch +++$+++ 1982 +++$+++ 3.80 +++$+++ 13037 +++$+++ ['horror', 'mystery', 'sci-fi']
+m435 +++$+++ meet joe black +++$+++ 1998 +++$+++ 6.90 +++$+++ 56839 +++$+++ ['drama', 'fantasy', 'mystery', 'romance']
+m436 +++$+++ memento +++$+++ 2000 +++$+++ 8.70 +++$+++ 281027 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m437 +++$+++ miami vice +++$+++ 2006 +++$+++ 6.00 +++$+++ 52448 +++$+++ ['action', 'crime', 'drama', 'thriller']
+m438 +++$+++ midnight cowboy +++$+++ 1969 +++$+++ 8.00 +++$+++ 34405 +++$+++ ['drama']
+m439 +++$+++ midnight express +++$+++ 1978 +++$+++ 7.60 +++$+++ 24103 +++$+++ ['biography', 'crime', 'drama', 'thriller']
+m440 +++$+++ mimic +++$+++ 1997 +++$+++ 5.70 +++$+++ 14916 +++$+++ ['drama', 'horror', 'sci-fi']
+m441 +++$+++ misery +++$+++ 1990 +++$+++ 7.80 +++$+++ 41563 +++$+++ ['thriller']
+m442 +++$+++ mission: impossible ii +++$+++ 2000 +++$+++ 5.70 +++$+++ 86452 +++$+++ ['action', 'adventure', 'thriller']
+m443 +++$+++ mission: impossible +++$+++ 1996 +++$+++ 6.90 +++$+++ 87677 +++$+++ ['action', 'adventure', 'thriller']
+m444 +++$+++ moonstruck +++$+++ 1987 +++$+++ 7.10 +++$+++ 15879 +++$+++ ['comedy', 'romance', 'drama']
+m445 +++$+++ monty python and the holy grail +++$+++ 1975 +++$+++ 8.40 +++$+++ 157683 +++$+++ ['adventure', 'comedy']
+m446 +++$+++ mrs brown +++$+++ 1997 +++$+++ 7.20 +++$+++ 5701 +++$+++ ['biography', 'drama', 'history', 'romance']
+m447 +++$+++ arcade +++$+++ 1993 +++$+++ 4.60 +++$+++ 668 +++$+++ ['sci-fi']
+m448 +++$+++ mulholland dr. +++$+++ 2001 +++$+++ 8.00 +++$+++ 105859 +++$+++ ['drama', 'mystery', 'thriller']
+m449 +++$+++ "murderland" +++$+++ 2009 +++$+++ 5.80 +++$+++ 88 +++$+++ ['crime', 'drama', 'mystery']
+m450 +++$+++ my girl 2 +++$+++ 1994 +++$+++ 4.80 +++$+++ 5689 +++$+++ ['comedy', 'drama', 'family', 'romance']
+m451 +++$+++ my girl +++$+++ 1991 +++$+++ 6.40 +++$+++ 17967 +++$+++ ['comedy', 'drama', 'family', 'romance']
+m452 +++$+++ my best friend's wedding +++$+++ 1997 +++$+++ 6.20 +++$+++ 37682 +++$+++ ['comedy', 'romance']
+m453 +++$+++ nashville +++$+++ 1975 +++$+++ 7.70 +++$+++ 9315 +++$+++ ['drama', 'music']
+m454 +++$+++ natural born killers +++$+++ 1994 +++$+++ 7.10 +++$+++ 73047 +++$+++ ['action', 'crime', 'drama', 'romance', 'thriller']
+m455 +++$+++ nothing but a man +++$+++ 1964 +++$+++ 7.80 +++$+++ 448 +++$+++ ['drama', 'romance']
+m456 +++$+++ the negotiator +++$+++ 1998 +++$+++ 7.20 +++$+++ 46983 +++$+++ ['action', 'crime', 'drama', 'mystery', 'thriller']
+m457 +++$+++ neuromancer +++$+++ 1988 +++$+++ 9.30 +++$+++ 48 +++$+++ ['action', 'adventure', 'animation', 'sci-fi']
+m458 +++$+++ never been kissed +++$+++ 1999 +++$+++ 5.70 +++$+++ 27409 +++$+++ ['comedy', 'drama', 'romance']
+m459 +++$+++ the nightmare before christmas +++$+++ 1993 +++$+++ 8.00 +++$+++ 81753 +++$+++ ['animation', 'family', 'fantasy', 'musical']
+m460 +++$+++ a nightmare on elm street part 2: freddy's revenge +++$+++ 1985 +++$+++ 4.90 +++$+++ 16582 +++$+++ ['fantasy', 'horror', 'thriller']
+m461 +++$+++ a nightmare on elm street 3: dream warriors +++$+++ 1987 +++$+++ 6.30 +++$+++ 17734 +++$+++ ['fantasy', 'horror', 'thriller']
+m462 +++$+++ notting hill +++$+++ 1999 +++$+++ 6.90 +++$+++ 67217 +++$+++ ['comedy', 'romance']
+m463 +++$+++ one flew over the cuckoo's nest +++$+++ 1975 +++$+++ 8.90 +++$+++ 219739 +++$+++ ['drama']
+m464 +++$+++ only you +++$+++ 1994 +++$+++ 6.20 +++$+++ 6620 +++$+++ ['comedy', 'romance']
+m465 +++$+++ on the waterfront +++$+++ 1954 +++$+++ 8.40 +++$+++ 42010 +++$+++ ['crime', 'drama', 'romance']
+m466 +++$+++ orgy of the dead +++$+++ 1965 +++$+++ 2.50 +++$+++ 1395 +++$+++ ['fantasy', 'horror']
+m467 +++$+++ out of sight +++$+++ 1998 +++$+++ 7.20 +++$+++ 38595 +++$+++ ['comedy', 'crime', 'romance', 'thriller']
+m468 +++$+++ pearl harbor +++$+++ 2001 +++$+++ 5.40 +++$+++ 97519 +++$+++ ['action', 'drama', 'romance', 'war']
+m469 +++$+++ peggy sue got married +++$+++ 1986 +++$+++ 6.30 +++$+++ 12574 +++$+++ ['comedy', 'drama', 'fantasy', 'romance']
+m470 +++$+++ pet sematary ii +++$+++ 1992 +++$+++ 4.20 +++$+++ 6014 +++$+++ ['action', 'horror', 'thriller']
+m471 +++$+++ philadelphia +++$+++ 1993 +++$+++ 7.60 +++$+++ 54221 +++$+++ ['drama']
+m472 +++$+++ pitch black +++$+++ 2000 +++$+++ 7.00 +++$+++ 55982 +++$+++ ['action', 'sci-fi', 'thriller']
+m473 +++$+++ planet of the apes +++$+++ 2001 +++$+++ 5.50 +++$+++ 73596 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller', 'animation', 'action', 'adventure', 'sci-fi']
+m474 +++$+++ platoon +++$+++ 1986 +++$+++ 8.20 +++$+++ 110867 +++$+++ ['action', 'drama', 'war']
+m475 +++$+++ playback +++$+++ 1996 +++$+++ 3.60 +++$+++ 164 +++$+++ ['thriller']
+m476 +++$+++ the ploughman's lunch +++$+++ 1983 +++$+++ 6.80 +++$+++ 154 +++$+++ ['drama']
+m477 +++$+++ point break +++$+++ 1991 +++$+++ 6.90 +++$+++ 37722 +++$+++ ['action', 'adventure', 'crime', 'drama', 'sport', 'thriller']
+m478 +++$+++ predator +++$+++ 1987 +++$+++ 7.80 +++$+++ 90730 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m479 +++$+++ pretty woman +++$+++ 1990 +++$+++ 6.70 +++$+++ 61642 +++$+++ ['comedy', 'romance']
+m480 +++$+++ the princess bride +++$+++ 1987 +++$+++ 8.10 +++$+++ 125281 +++$+++ ['adventure', 'comedy', 'family', 'fantasy', 'romance']
+m481 +++$+++ the producers +++$+++ 1968 +++$+++ 7.70 +++$+++ 19432 +++$+++ ['comedy']
+m482 +++$+++ psycho +++$+++ 1960 +++$+++ 8.70 +++$+++ 146915 +++$+++ ['drama', 'horror', 'mystery', 'thriller', 'thriller']
+m483 +++$+++ maniac +++$+++ 1980 +++$+++ 6.20 +++$+++ 3382 +++$+++ ['drama', 'horror', 'thriller']
+m484 +++$+++ vampyr +++$+++ 1932 +++$+++ 7.60 +++$+++ 4005 +++$+++ ['fantasy', 'horror']
+m485 +++$+++ mystery of the wax museum +++$+++ 1933 +++$+++ 6.80 +++$+++ 1740 +++$+++ ['horror', 'mystery', 'thriller']
+m486 +++$+++ quantum project +++$+++ 2000 +++$+++ 4.80 +++$+++ 47 +++$+++ ['short', 'adventure', 'drama', 'romance']
+m487 +++$+++ rambling rose +++$+++ 1991 +++$+++ 6.60 +++$+++ 2631 +++$+++ ['drama']
+m488 +++$+++ red white black & blue +++$+++ 2006 +++$+++ 6.30 +++$+++ 37 +++$+++ ['documentary']
+m489 +++$+++ star wars: episode vi - return of the jedi +++$+++ 1983 +++$+++ 8.30 +++$+++ 215058 +++$+++ ['action', 'adventure', 'fantasy', 'sci-fi']
+m490 +++$+++ the rocky horror picture show +++$+++ 1975 +++$+++ 7.10 +++$+++ 42713 +++$+++ ['comedy', 'musical']
+m491 +++$+++ rocky +++$+++ 1976 +++$+++ 8.10 +++$+++ 100022 +++$+++ ['drama', 'romance', 'sport']
+m492 +++$+++ who framed roger rabbit +++$+++ 1988 +++$+++ 7.60 +++$+++ 54375 +++$+++ ['animation', 'comedy', 'crime', 'family', 'fantasy', 'mystery', 'animation', 'adventure', 'action', 'fantasy']
+m493 +++$+++ romeo and juliet +++$+++ 1968/I +++$+++ 7.80 +++$+++ 12360 +++$+++ ['drama', 'romance']
+m494 +++$+++ ronin +++$+++ 1998 +++$+++ 7.20 +++$+++ 58201 +++$+++ ['action', 'crime', 'thriller']
+m495 +++$+++ route 9 +++$+++ 1998 +++$+++ 6.00 +++$+++ 557 +++$+++ ['crime', 'drama']
+m496 +++$+++ rush hour 2 +++$+++ 2001 +++$+++ 6.50 +++$+++ 52716 +++$+++ ['action', 'comedy', 'crime', 'thriller']
+m497 +++$+++ rush hour +++$+++ 1998 +++$+++ 6.80 +++$+++ 56376 +++$+++ ['action', 'comedy', 'thriller', 'crime']
+m498 +++$+++ runaway bride +++$+++ 1999 +++$+++ 5.20 +++$+++ 28793 +++$+++ ['comedy', 'romance']
+m499 +++$+++ sleepless in seattle +++$+++ 1993 +++$+++ 6.60 +++$+++ 44179 +++$+++ ['comedy', 'romance', 'drama']
+m500 +++$+++ salt of the earth +++$+++ 1954 +++$+++ 7.60 +++$+++ 1085 +++$+++ ['drama', 'history']
+m501 +++$+++ the salton sea +++$+++ 2002 +++$+++ 7.10 +++$+++ 16669 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m502 +++$+++ saving private ryan +++$+++ 1998 +++$+++ 8.50 +++$+++ 275666 +++$+++ ['action', 'drama', 'history', 'war']
+m503 +++$+++ say anything... +++$+++ 1989 +++$+++ 7.50 +++$+++ 25220 +++$+++ ['comedy', 'drama', 'romance']
+m504 +++$+++ schindler's list +++$+++ 1993 +++$+++ 8.90 +++$+++ 282473 +++$+++ ['biography', 'drama', 'history', 'war']
+m505 +++$+++ scream 2 +++$+++ 1997 +++$+++ 5.90 +++$+++ 48988 +++$+++ ['horror', 'mystery', 'thriller']
+m506 +++$+++ scream 3 +++$+++ 2000 +++$+++ 5.30 +++$+++ 38870 +++$+++ ['horror', 'mystery', 'thriller']
+m507 +++$+++ scream +++$+++ 1996/I +++$+++ 7.20 +++$+++ 92543 +++$+++ ['crime', 'horror', 'mystery', 'thriller']
+m508 +++$+++ seven +++$+++ 1979 +++$+++ 6.10 +++$+++ 259 +++$+++ ['action', 'drama']
+m509 +++$+++ the searchers +++$+++ 1956 +++$+++ 8.10 +++$+++ 30138 +++$+++ ['adventure', 'drama', 'western']
+m510 +++$+++ seven days to live +++$+++ 2000 +++$+++ 5.20 +++$+++ 1150 +++$+++ ['drama', 'horror', 'thriller']
+m511 +++$+++ shakespeare in love +++$+++ 1998 +++$+++ 7.40 +++$+++ 78654 +++$+++ ['comedy', 'drama', 'romance']
+m512 +++$+++ shallow grave +++$+++ 1994 +++$+++ 7.40 +++$+++ 21611 +++$+++ ['comedy', 'crime', 'drama', 'thriller']
+m513 +++$+++ shampoo +++$+++ 1975 +++$+++ 6.20 +++$+++ 4406 +++$+++ ['drama', 'romance']
+m514 +++$+++ the shining +++$+++ 1980 +++$+++ 8.50 +++$+++ 182077 +++$+++ ['horror', 'mystery', 'thriller']
+m515 +++$+++ silver bullet +++$+++ 1985 +++$+++ 5.90 +++$+++ 6498 +++$+++ ['adventure', 'drama', 'horror', 'mystery', 'thriller']
+m516 +++$+++ sister act +++$+++ 1992 +++$+++ 5.90 +++$+++ 25196 +++$+++ ['comedy', 'crime', 'music']
+m517 +++$+++ sleepy hollow +++$+++ 1999 +++$+++ 7.50 +++$+++ 108951 +++$+++ ['fantasy', 'mystery', 'thriller']
+m518 +++$+++ sling blade +++$+++ 1996 +++$+++ 8.00 +++$+++ 42087 +++$+++ ['drama']
+m519 +++$+++ smoke +++$+++ 1995 +++$+++ 7.50 +++$+++ 15482 +++$+++ ['comedy', 'drama']
+m520 +++$+++ snow falling on cedars +++$+++ 1999 +++$+++ 6.70 +++$+++ 8483 +++$+++ ['drama', 'mystery', 'romance', 'thriller']
+m521 +++$+++ soldier +++$+++ 1998/I +++$+++ 5.60 +++$+++ 16446 +++$+++ ['action', 'drama', 'sci-fi']
+m522 +++$+++ some like it hot +++$+++ 1959 +++$+++ 8.40 +++$+++ 68749 +++$+++ ['comedy']
+m523 +++$+++ the wedding date +++$+++ 2005 +++$+++ 5.50 +++$+++ 12340 +++$+++ ['comedy', 'romance']
+m524 +++$+++ sounder +++$+++ 1972 +++$+++ 7.80 +++$+++ 1298 +++$+++ ['drama']
+m525 +++$+++ south park: bigger longer & uncut +++$+++ 1999 +++$+++ 7.80 +++$+++ 80257 +++$+++ ['animation', 'comedy', 'musical']
+m526 +++$+++ spacejacked +++$+++ 1997 +++$+++ 2.90 +++$+++ 105 +++$+++ ['action', 'sci-fi']
+m527 +++$+++ spare me +++$+++ 1992 +++$+++ 5.80 +++$+++ 26 +++$+++ ['thriller']
+m528 +++$+++ sphere +++$+++ 1998 +++$+++ 5.60 +++$+++ 31924 +++$+++ ['drama', 'horror', 'mystery', 'sci-fi', 'thriller']
+m529 +++$+++ star wars +++$+++ 1977 +++$+++ 8.80 +++$+++ 326619 +++$+++ ['action', 'adventure', 'fantasy', 'sci-fi']
+m530 +++$+++ starship troopers +++$+++ 1997 +++$+++ 7.10 +++$+++ 84874 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m531 +++$+++ star trek: the motion picture +++$+++ 1979 +++$+++ 6.20 +++$+++ 25723 +++$+++ ['adventure', 'fantasy', 'mystery', 'sci-fi']
+m532 +++$+++ state and main +++$+++ 2000 +++$+++ 6.80 +++$+++ 13163 +++$+++ ['comedy', 'drama']
+m533 +++$+++ stepmom +++$+++ 1998 +++$+++ 6.20 +++$+++ 18714 +++$+++ ['comedy', 'drama']
+m534 +++$+++ storytelling +++$+++ 2001 +++$+++ 6.70 +++$+++ 9677 +++$+++ ['comedy', 'drama']
+m535 +++$+++ stranglehold +++$+++ 2007 +++$+++ 8.60 +++$+++ 907 +++$+++ ['action', 'adventure', 'crime']
+m536 +++$+++ dr. strangelove or: how i learned to stop worrying and love the bomb +++$+++ 1964 +++$+++ 8.60 +++$+++ 158529 +++$+++ ['comedy', 'drama']
+m537 +++$+++ suburbia +++$+++ 1996 +++$+++ 6.50 +++$+++ 4552 +++$+++ ['comedy', 'drama']
+m538 +++$+++ sugar & spice +++$+++ 2001 +++$+++ 5.20 +++$+++ 6981 +++$+++ ['comedy', 'crime']
+m539 +++$+++ sunset blvd. +++$+++ 1950 +++$+++ 8.70 +++$+++ 55994 +++$+++ ['drama', 'film-noir']
+m540 +++$+++ supergirl +++$+++ 1984 +++$+++ 4.10 +++$+++ 6576 +++$+++ ['action', 'adventure', 'sci-fi']
+m541 +++$+++ superman iii +++$+++ 1983 +++$+++ 4.70 +++$+++ 18340 +++$+++ ['action', 'adventure', 'comedy', 'fantasy', 'sci-fi']
+m542 +++$+++ superman ii +++$+++ 1980 +++$+++ 6.70 +++$+++ 29941 +++$+++ ['action', 'adventure', 'fantasy', 'romance', 'sci-fi']
+m543 +++$+++ superman iv: the quest for peace +++$+++ 1987 +++$+++ 3.40 +++$+++ 15332 +++$+++ ['action', 'adventure', 'family', 'fantasy', 'sci-fi']
+m544 +++$+++ superman +++$+++ 1978 +++$+++ 7.30 +++$+++ 51172 +++$+++ ['action', 'family', 'sci-fi']
+m545 +++$+++ the sweet hereafter +++$+++ 1997 +++$+++ 7.80 +++$+++ 16463 +++$+++ ['drama']
+m546 +++$+++ sweet smell of success +++$+++ 1957 +++$+++ 8.20 +++$+++ 9014 +++$+++ ['drama', 'film-noir']
+m547 +++$+++ terminator 2: judgment day +++$+++ 1991 +++$+++ 8.50 +++$+++ 243230 +++$+++ ['action', 'sci-fi', 'thriller']
+m548 +++$+++ taking sides +++$+++ 2001 +++$+++ 7.20 +++$+++ 1950 +++$+++ ['drama', 'music', 'war']
+m549 +++$+++ the terminator +++$+++ 1984 +++$+++ 8.10 +++$+++ 183538 +++$+++ ['action', 'sci-fi', 'thriller']
+m550 +++$+++ the game +++$+++ 1997 +++$+++ 7.70 +++$+++ 75861 +++$+++ ['drama', 'mystery', 'thriller']
+m551 +++$+++ the haunting +++$+++ 1999 +++$+++ 4.60 +++$+++ 32086 +++$+++ ['horror', 'thriller', 'mystery']
+m552 +++$+++ the limey +++$+++ 1999 +++$+++ 7.00 +++$+++ 15465 +++$+++ ['crime', 'drama', 'mystery', 'thriller']
+m553 +++$+++ the man who wasn't there +++$+++ 2001 +++$+++ 7.70 +++$+++ 41452 +++$+++ ['crime', 'drama']
+m554 +++$+++ the relic +++$+++ 1997 +++$+++ 5.40 +++$+++ 10340 +++$+++ ['horror', 'mystery', 'thriller']
+m555 +++$+++ the truman show +++$+++ 1998 +++$+++ 8.00 +++$+++ 160825 +++$+++ ['drama']
+m556 +++$+++ the x files +++$+++ 1998 +++$+++ 6.80 +++$+++ 39090 +++$+++ ['crime', 'horror', 'mystery', 'sci-fi', 'thriller']
+m557 +++$+++ the cell +++$+++ 2000 +++$+++ 6.20 +++$+++ 37417 +++$+++ ['drama', 'fantasy', 'horror', 'sci-fi', 'thriller']
+m558 +++$+++ the third man +++$+++ 1949 +++$+++ 8.50 +++$+++ 51872 +++$+++ ['film-noir', 'mystery', 'thriller']
+m559 +++$+++ the beach +++$+++ 2000/I +++$+++ 6.30 +++$+++ 55734 +++$+++ ['adventure', 'drama', 'romance', 'thriller']
+m560 +++$+++ the believer +++$+++ 2001 +++$+++ 7.30 +++$+++ 11345 +++$+++ ['drama']
+m561 +++$+++ le grand bleu +++$+++ 1988 +++$+++ 7.40 +++$+++ 18313 +++$+++ ['adventure', 'drama', 'romance']
+m562 +++$+++ the hebrew hammer +++$+++ 2003 +++$+++ 6.20 +++$+++ 2900 +++$+++ ['comedy']
+m563 +++$+++ the leopard man +++$+++ 1943 +++$+++ 6.90 +++$+++ 1518 +++$+++ ['drama', 'horror', 'mystery', 'thriller']
+m564 +++$+++ the lost son +++$+++ 1999 +++$+++ 6.50 +++$+++ 911 +++$+++ ['crime', 'drama', 'romance', 'thriller']
+m565 +++$+++ the messenger +++$+++ 2009/I +++$+++ 7.40 +++$+++ 9224 +++$+++ ['drama', 'romance', 'war']
+m566 +++$+++ the pianist +++$+++ 2002 +++$+++ 8.50 +++$+++ 138080 +++$+++ ['biography', 'drama', 'war']
+m567 +++$+++ the piano +++$+++ 1993 +++$+++ 7.50 +++$+++ 28525 +++$+++ ['drama', 'romance']
+m568 +++$+++ the sting +++$+++ 1973 +++$+++ 8.40 +++$+++ 67231 +++$+++ ['comedy', 'crime', 'drama']
+m569 +++$+++ the thin man +++$+++ 1934 +++$+++ 8.10 +++$+++ 11936 +++$+++ ['comedy', 'crime', 'drama', 'mystery', 'romance']
+m570 +++$+++ three kings +++$+++ 1999 +++$+++ 7.30 +++$+++ 69757 +++$+++ ['action', 'adventure', 'comedy', 'drama', 'war']
+m571 +++$+++ thx 1138 +++$+++ 1971 +++$+++ 6.80 +++$+++ 15741 +++$+++ ['drama', 'mystery', 'sci-fi', 'thriller']
+m572 +++$+++ ticker +++$+++ 2001 +++$+++ 3.30 +++$+++ 3933 +++$+++ ['action', 'thriller', 'crime']
+m573 +++$+++ trouble in paradise +++$+++ 1932 +++$+++ 8.20 +++$+++ 4781 +++$+++ ['comedy', 'crime', 'romance']
+m574 +++$+++ titanic +++$+++ 1997 +++$+++ 7.40 +++$+++ 244771 +++$+++ ['drama', 'history', 'romance']
+m575 +++$+++ transatlantic merry-go-round +++$+++ 1934 +++$+++ 6.90 +++$+++ 53 +++$+++ ['comedy', 'musical', 'mystery', 'romance']
+m576 +++$+++ tombstone +++$+++ 1993 +++$+++ 7.70 +++$+++ 44204 +++$+++ ['action', 'drama', 'history', 'romance', 'western']
+m577 +++$+++ tomorrow never dies +++$+++ 1997 +++$+++ 6.40 +++$+++ 47198 +++$+++ ['action', 'adventure', 'thriller']
+m578 +++$+++ to sleep with anger +++$+++ 1990 +++$+++ 6.80 +++$+++ 490 +++$+++ ['drama']
+m579 +++$+++ toy story +++$+++ 1995 +++$+++ 8.20 +++$+++ 156231 +++$+++ ['animation', 'adventure', 'comedy', 'family', 'fantasy']
+m580 +++$+++ the magic toyshop +++$+++ 1987 +++$+++ 6.20 +++$+++ 90 +++$+++ ['fantasy']
+m581 +++$+++ traffic +++$+++ 2000 +++$+++ 7.80 +++$+++ 86662 +++$+++ ['crime', 'drama', 'thriller']
+m582 +++$+++ trainspotting +++$+++ 1996 +++$+++ 8.20 +++$+++ 153782 +++$+++ ['crime', 'drama']
+m583 +++$+++ star trek v: the final frontier +++$+++ 1989 +++$+++ 5.00 +++$+++ 20749 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller']
+m584 +++$+++ tron +++$+++ 1982 +++$+++ 6.70 +++$+++ 25903 +++$+++ ['action', 'adventure', 'sci-fi', 'thriller', 'sci-fi']
+m585 +++$+++ true lies +++$+++ 1994 +++$+++ 7.20 +++$+++ 81801 +++$+++ ['action', 'thriller']
+m586 +++$+++ true romance +++$+++ 1993 +++$+++ 7.90 +++$+++ 74762 +++$+++ ['crime', 'romance', 'thriller']
+m587 +++$+++ twelve monkeys +++$+++ 1995 +++$+++ 8.10 +++$+++ 173320 +++$+++ ['mystery', 'sci-fi', 'thriller']
+m588 +++$+++ u-turn +++$+++ 1973 +++$+++ 5.80 +++$+++ 37 +++$+++ ['drama']
+m589 +++$+++ u turn +++$+++ 1997 +++$+++ 6.70 +++$+++ 25388 +++$+++ ['crime', 'drama', 'thriller']
+m590 +++$+++ unbreakable +++$+++ 2000 +++$+++ 7.30 +++$+++ 98767 +++$+++ ['drama', 'fantasy', 'mystery', 'thriller']
+m591 +++$+++ unforgiven +++$+++ 1992 +++$+++ 8.30 +++$+++ 101270 +++$+++ ['drama', 'western']
+m592 +++$+++ the usual suspects +++$+++ 1995 +++$+++ 8.70 +++$+++ 272137 +++$+++ ['crime', 'mystery', 'thriller']
+m593 +++$+++ verdict +++$+++ 1974 +++$+++ 5.70 +++$+++ 135 +++$+++ ['drama']
+m594 +++$+++ vertigo +++$+++ 1958 +++$+++ 8.60 +++$+++ 93524 +++$+++ ['crime', 'mystery', 'romance', 'thriller']
+m595 +++$+++ very bad things +++$+++ 1998 +++$+++ 6.00 +++$+++ 22977 +++$+++ ['comedy', 'crime', 'thriller']
+m596 +++$+++ viridiana +++$+++ 1961 +++$+++ 8.20 +++$+++ 6288 +++$+++ ['drama']
+m597 +++$+++ virtuosity +++$+++ 1995 +++$+++ 5.30 +++$+++ 11191 +++$+++ ['action', 'crime', 'sci-fi', 'thriller']
+m598 +++$+++ wag the dog +++$+++ 1997 +++$+++ 7.10 +++$+++ 36448 +++$+++ ['comedy', 'drama']
+m599 +++$+++ wall street +++$+++ 1987 +++$+++ 7.30 +++$+++ 39082 +++$+++ ['crime', 'drama']
+m600 +++$+++ waxwork +++$+++ 1988 +++$+++ 6.00 +++$+++ 2596 +++$+++ ['comedy', 'fantasy', 'horror']
+m601 +++$+++ what lies beneath +++$+++ 2000 +++$+++ 6.50 +++$+++ 46195 +++$+++ ['drama', 'horror', 'mystery', 'thriller']
+m602 +++$+++ what women want +++$+++ 2000 +++$+++ 6.30 +++$+++ 55269 +++$+++ ['comedy', 'fantasy', 'romance']
+m603 +++$+++ the witching hour +++$+++ 1996 +++$+++ 6.50 +++$+++ 69 +++$+++ ['documentary', 'short']
+m604 +++$+++ white angel +++$+++ 1994 +++$+++ 4.40 +++$+++ 99 +++$+++ ['drama', 'thriller']
+m605 +++$+++ who's your daddy? +++$+++ 2003/I +++$+++ 4.50 +++$+++ 2267 +++$+++ ['comedy']
+m606 +++$+++ wild things +++$+++ 1998 +++$+++ 6.60 +++$+++ 40523 +++$+++ ['crime', 'mystery', 'thriller']
+m607 +++$+++ wild wild west +++$+++ 1999 +++$+++ 4.30 +++$+++ 54943 +++$+++ ['action', 'western', 'comedy', 'sci-fi']
+m608 +++$+++ willow +++$+++ 1988 +++$+++ 7.10 +++$+++ 33506 +++$+++ ['action', 'adventure', 'drama', 'fantasy', 'romance']
+m609 +++$+++ witness +++$+++ 1985 +++$+++ 7.60 +++$+++ 30705 +++$+++ ['drama', 'romance', 'thriller']
+m610 +++$+++ the wizard of oz +++$+++ 1939 +++$+++ 8.30 +++$+++ 104873 +++$+++ ['adventure', 'family', 'fantasy', 'musical']
+m611 +++$+++ the world is not enough +++$+++ 1999 +++$+++ 6.30 +++$+++ 60047 +++$+++ ['action', 'adventure', 'thriller']
+m612 +++$+++ watchmen +++$+++ 2009 +++$+++ 7.80 +++$+++ 135229 +++$+++ ['action', 'crime', 'fantasy', 'mystery', 'sci-fi', 'thriller']
+m613 +++$+++ xxx +++$+++ 2002 +++$+++ 5.60 +++$+++ 53505 +++$+++ ['action', 'adventure', 'crime']
+m614 +++$+++ x-men +++$+++ 2000 +++$+++ 7.40 +++$+++ 122149 +++$+++ ['action', 'sci-fi']
+m615 +++$+++ young frankenstein +++$+++ 1974 +++$+++ 8.00 +++$+++ 57618 +++$+++ ['comedy', 'sci-fi']
+m616 +++$+++ zulu dawn +++$+++ 1979 +++$+++ 6.40 +++$+++ 1911 +++$+++ ['action', 'adventure', 'drama', 'history', 'war']

tutorial/cornell_movie-dialogs_corpus/raw_script_urls.txt

@@ -0,0 +1,617 @@
+m0 +++$+++ 10 things i hate about you +++$+++ http://www.dailyscript.com/scripts/10Things.html
+m1 +++$+++ 1492: conquest of paradise +++$+++ http://www.hundland.org/scripts/1492-ConquestOfParadise.txt
+m2 +++$+++ 15 minutes +++$+++ http://www.dailyscript.com/scripts/15minutes.html
+m3 +++$+++ 2001: a space odyssey +++$+++ http://www.scifiscripts.com/scripts/2001.txt
+m4 +++$+++ 48 hrs. +++$+++ http://www.awesomefilm.com/script/48hours.txt
+m5 +++$+++ the fifth element +++$+++ http://www.scifiscripts.com/scripts/5thelement.txt
+m6 +++$+++ 8mm +++$+++ http://www.dailyscript.com/scripts/eight-millimeter.html
+m7 +++$+++ a nightmare on elm street 4: the dream master +++$+++ http://www.hundland.org/scripts/A-Nightmare-on-Elm-Street-4.txt
+m8 +++$+++ a nightmare on elm street: the dream child +++$+++ http://www.hundland.org/scripts/A-Nightmare-on-Elm-Street-5.txt
+m9 +++$+++ the atomic submarine +++$+++ http://leonscripts.tripod.com/scripts/ATOMICSUB.htm
+m10 +++$+++ affliction +++$+++ http://www.dailyscript.com/scripts/Affliction.txt
+m11 +++$+++ air force one +++$+++ http://www.dailyscript.com/scripts/AirForceOne_TXT.html
+m12 +++$+++ airplane ii: the sequel +++$+++ http://www.dailyscript.com/scripts/Airplane2_script.htm
+m13 +++$+++ airplane! +++$+++ http://www.dailyscript.com/scripts/Airplane_script.htm
+m14 +++$+++ alien nation +++$+++ http://www.dailyscript.com/scripts/Alien_Nation_Bannon_Cameron_October_1987.html
+m15 +++$+++ aliens +++$+++ http://www.horrorlair.com/scripts/aliens.html
+m16 +++$+++ amadeus +++$+++ http://www.dailyscript.com/scripts/Amadeus.txt
+m17 +++$+++ an american werewolf in london +++$+++ http://www.dailyscript.com/scripts/American%20Werewolf%20In%20London,%20An.txt
+m18 +++$+++ american madness +++$+++ http://www.dailyscript.com/scripts/American_Madness.html
+m19 +++$+++ american outlaws +++$+++ http://www.dailyscript.com/scripts/American_Outlaws.html
+m20 +++$+++ american psycho +++$+++ http://www.dailyscript.com/scripts/american_psycho_unproduced.html
+m21 +++$+++ antitrust +++$+++ http://www.dailyscript.com/scripts/Antitrust.txt
+m22 +++$+++ austin powers: international man of mystery +++$+++ http://www.dailyscript.com/scripts/Austin%20Powers%20-%20International%20Man%20Of%20Mystery.txt
+m23 +++$+++ the avengers +++$+++ http://www.dailyscript.com/scripts/Avengers.html
+m24 +++$+++ bachelor party +++$+++ http://www.dailyscript.com/scripts/Bachelor%20Party.txt
+m25 +++$+++ backdraft +++$+++ http://www.dailyscript.com/scripts/Backdraft%20(1991).txt
+m26 +++$+++ bad lieutenant +++$+++ http://www.dailyscript.com/scripts/Bad%20Lieutenant.txt
+m27 +++$+++ bamboozled +++$+++ http://www.awesomefilm.com/script/Bamboozled.txt
+m28 +++$+++ barry lyndon +++$+++ http://www.dailyscript.com/scripts/BarryLyndon.html
+m29 +++$+++ basic +++$+++ http://www.dailyscript.com/scripts/Basic.txt
+m30 +++$+++ big fish +++$+++ http://www.dailyscript.com/scripts/Big+Fish.txt
+m31 +++$+++ birthday girl +++$+++ http://www.dailyscript.com/scripts/Birthday+Girl.txt
+m32 +++$+++ black snake moan +++$+++ http://www.dailyscript.com/scripts/Black%20Snake%20Moan.txt
+m33 +++$+++ black rain +++$+++ http://www.angelfire.com/movies/ridleyscott/script/BlackRain.txt
+m34 +++$+++ blade runner +++$+++ http://www.dailyscript.com/scripts/blade-runner_shooting.html
+m35 +++$+++ blast from the past +++$+++ http://www.hundland.org/scripts/Blast-from-the-Past.htm
+m36 +++$+++ blue velvet +++$+++ http://www.dailyscript.com/scripts/Blue+Velvet.txt
+m37 +++$+++ the boondock saints +++$+++ http://www.dailyscript.com/scripts/Boondock%20Saints,%20The.txt
+m38 +++$+++ bottle rocket +++$+++ http://www.dailyscript.com/scripts/Bottle+Rocket.txt
+m39 +++$+++ the bourne supremacy +++$+++ http://www.dailyscript.com/scripts/Bourne+Supremacy,+The.txt
+m40 +++$+++ braveheart +++$+++ http://www.dailyscript.com/scripts/Braveheart.txt
+m41 +++$+++ the butterfly effect +++$+++ http://www.dailyscript.com/scripts/Butterfly+Effect,+The.txt
+m42 +++$+++ casablanca +++$+++ http://www.dailyscript.com/scripts/Casablanca.txt
+m43 +++$+++ cast away +++$+++ http://www.hundland.org/scripts/CastAway.txt
+m44 +++$+++ the cider house rules +++$+++ http://www.dailyscript.com/scripts/Cider%20House%20Rules,%20The.txt
+m45 +++$+++ confidence +++$+++ http://www.dailyscript.com/scripts/Confidence.txt
+m46 +++$+++ croupier +++$+++ http://www.dailyscript.com/scripts/Croupier.txt
+m47 +++$+++ dark star +++$+++ http://www.dailyscript.com/scripts/Dark+Star.txt
+m48 +++$+++ dark angel +++$+++ http://www.hundland.org/scripts/DarkAngel.txt
+m49 +++$+++ detroit rock city +++$+++ http://www.dailyscript.com/scripts/Detroit+Rock+City.txt
+m50 +++$+++ donnie darko +++$+++ http://www.dailyscript.com/scripts/Donnie+Darko.txt
+m51 +++$+++ drop dead gorgeous +++$+++ http://www.dailyscript.com/scripts/Drop+Dead+Gorgeous.txt
+m52 +++$+++ duck soup +++$+++ http://www.dailyscript.com/scripts/Duck%20Soup.txt
+m53 +++$+++ the elephant man +++$+++ http://www.dailyscript.com/scripts/Elephant+Man,+The.txt
+m54 +++$+++ erik the viking +++$+++ http://www.dailyscript.com/scripts/Erik+The+Viking.txt
+m55 +++$+++ eternal sunshine of the spotless mind +++$+++ http://www.dailyscript.com/scripts/Eternal+Sunshine+Of+The+Spotless+Mind.txt
+m56 +++$+++ even cowgirls get the blues +++$+++ http://www.dailyscript.com/scripts/Even%20Cowgirls%20Get%20The%20Blues.txt
+m57 +++$+++ event horizon +++$+++ http://www.dailyscript.com/scripts/Event%20Horizon.txt
+m58 +++$+++ fantastic four +++$+++ http://www.dailyscript.com/scripts/Fantastic+Four.txt
+m59 +++$+++ fast times at ridgemont high +++$+++ http://www.dailyscript.com/scripts/Fast+Times+At+Ridgemont+High.txt
+m60 +++$+++ fear and loathing in las vegas +++$+++ http://www.dailyscript.com/scripts/Fear+&+Loathing+In+Las+Vegas.txt
+m61 +++$+++ feast +++$+++ http://www.dailyscript.com/scripts/Feast%20-%20Revised%20Draft.txt
+m62 +++$+++ frances +++$+++ http://www.dailyscript.com/scripts/Frances.txt
+m63 +++$+++ frankenstein +++$+++ http://www.dailyscript.com/scripts/Frankenstein+(1994).txt
+m64 +++$+++ friday the 13th +++$+++ http://www.dailyscript.com/scripts/Friday%20The%2013th.txt
+m65 +++$+++ from dusk till dawn +++$+++ http://www.dailyscript.com/scripts/From%20Dusk%20Till%20Dawn.txt
+m66 +++$+++ g.i. jane +++$+++ http://www.angelfire.com/movies/ridleyscott/script/GIJane.txt
+m67 +++$+++ godzilla +++$+++ http://www.scifiscripts.com/scripts/GODZILLAfirst.txt
+m68 +++$+++ galaxy quest +++$+++ http://www.scifiscripts.com/scripts/GalaxyQuest.txt
+m69 +++$+++ george washington +++$+++ http://www.fortunecity.com/tattooine/clarke/38/scripts/GeorgeWashington.txt
+m70 +++$+++ get shorty +++$+++ http://www.dailyscript.com/scripts/Get+Shorty.txt
+m71 +++$+++ ghost ship +++$+++ http://www.whiskeyloosetongue.com/scripts/ghostship.html
+m72 +++$+++ ghost world +++$+++ http://www.dailyscript.com/scripts/Ghost%20World.txt
+m73 +++$+++ the ghost and the darkness +++$+++ http://www.dailyscript.com/scripts/Ghost%20And%20The%20Darkness,%20The.txt
+m74 +++$+++ ghostbusters ii +++$+++ http://www.scifiscripts.com/scripts/Ghostbusters-2.txt
+m75 +++$+++ ghostbusters +++$+++ http://www.scifiscripts.com/scripts/Ghostbusters.txt
+m76 +++$+++ gladiator +++$+++ http://www.angelfire.com/movies/ridleyscott/script/gladiator_seconddraft.txt
+m77 +++$+++ the graduate +++$+++ http://www.dailyscript.com/scripts/Graduate,+The.txt
+m78 +++$+++ grand hotel +++$+++ http://www.dailyscript.com/scripts/Grand%20Hotel.txt
+m79 +++$+++ the grifters +++$+++ http://www.dailyscript.com/scripts/Grifters_Westlake.html
+m80 +++$+++ halloween h20: 20 years later +++$+++ http://www.horrorlair.com/scripts/H20.txt
+m81 +++$+++ halloween: the curse of michael myers +++$+++ http://www.dailyscript.com/scripts/Halloween_6_The_Curse_of_Michael_Myers.html
+m82 +++$+++ happy birthday, wanda june +++$+++ http://www.dailyscript.com/scripts/Happy_Birthday_Wanda_JUne.html
+m83 +++$+++ hardcore +++$+++ http://www.dailyscript.com/scripts/Hardcore.txt
+m84 +++$+++ harold and maude +++$+++ http://home.online.no/~bhundlan/scripts/Harold-and-Maude.htm
+m85 +++$+++ hellbound: hellraiser ii +++$+++ http://www.dailyscript.com/scripts/Hellbound+-+Hellraiser+II.txt
+m86 +++$+++ hellboy +++$+++ http://www.dailyscript.com/scripts/Hellboy.txt
+m87 +++$+++ hellraiser +++$+++ http://www.dailyscript.com/scripts/Hellraiser.txt
+m88 +++$+++ high fidelity +++$+++ http://www.dailyscript.com/scripts/High%20Fidelity.txt
+m89 +++$+++ highlander +++$+++ http://www.scifiscripts.com/scripts/Highlander_final.txt
+m90 +++$+++ his girl friday +++$+++ http://www.dailyscript.com/scripts/His%20Girl%20Friday.txt
+m91 +++$+++ hope and glory +++$+++ http://www.dailyscript.com/scripts/Hope+And+Glory.txt
+m92 +++$+++ house of 1000 corpses +++$+++ http://www.dailyscript.com/scripts/House%20Of%201000%20Corpses.txt
+m93 +++$+++ human nature +++$+++ http://www.dailyscript.com/scripts/Human+Nature.txt
+m94 +++$+++ the hustler +++$+++ http://www.dailyscript.com/scripts/Hustler,+The.txt
+m95 +++$+++ i am legend +++$+++ http://www.horrorlair.com/scripts/legend.txt
+m96 +++$+++ invaders from mars +++$+++ http://leonscripts.tripod.com/scripts/INVADERSold.htm
+m97 +++$+++ independence day +++$+++ http://www.dailyscript.com/scripts/Independence+Day.txt
+m98 +++$+++ indiana jones and the last crusade +++$+++ http://www.dailyscript.com/scripts/Indiana%20Jones%20And%20The%20Last%20Crusade.txt
+m99 +++$+++ indiana jones and the temple of doom +++$+++ http://www.dailyscript.com/scripts/Indiana%20Jones%20And%20The%20Temple%20Of%20Doom.txt
+m100 +++$+++ innerspace +++$+++ http://www.scifiscripts.com/scripts/InnerSpace.txt
+m101 +++$+++ the insider +++$+++ http://www.dailyscript.com/scripts/Insider,+The.txt
+m102 +++$+++ intolerable cruelty +++$+++ http://www.dailyscript.com/scripts/Intolerable%20Cruelty.txt
+m103 +++$+++ it happened one night +++$+++ http://www.dailyscript.com/scripts/It+Happened+One+Night.txt
+m104 +++$+++ jfk +++$+++ http://www.dailyscript.com/scripts/JFK.txt
+m105 +++$+++ jackie brown +++$+++ http://www.dailyscript.com/scripts/Jackie+Brown.txt
+m106 +++$+++ jacob's ladder +++$+++ http://www.dailyscript.com/scripts/Jacobs+Ladder.txt
+m107 +++$+++ jason x +++$+++ http://www.dailyscript.com/scripts/Jason+X.txt
+m108 +++$+++ jaws +++$+++ http://www.dailyscript.com/scripts/Jaws.txt
+m109 +++$+++ juno +++$+++ http://www.dailyscript.com/scripts/Juno.txt
+m110 +++$+++ kalifornia +++$+++ http://www.dailyscript.com/scripts/Kalifornia.txt
+m111 +++$+++ kids +++$+++ http://www.dailyscript.com/scripts/Kids.txt
+m112 +++$+++ knight moves +++$+++ http://www.scifiscripts.com/msol/Knight_m.txt
+m113 +++$+++ krull +++$+++ http://www.dailyscript.com/scripts/Krull.html
+m114 +++$+++ l�on +++$+++ http://www.scifiscripts.com/msol/LEON.txt
+m115 +++$+++ labor of love +++$+++ http://www.fortunecity.com/tattooine/clarke/38/scripts/LaborOfLove.txt
+m116 +++$+++ leaving las vegas +++$+++ http://www.dailyscript.com/scripts/Leaving+Las+Vegas.txt
+m117 +++$+++ legally blonde +++$+++ http://www.dailyscript.com/scripts/Legally%20Blonde.txt
+m118 +++$+++ legend +++$+++ http://www.angelfire.com/movies/ridleyscott/script/LegendOfDarkness.txt
+m119 +++$+++ life as a house +++$+++ http://www.dailyscript.com/scripts/LifeAsAHouse.html
+m120 +++$+++ the life of david gale +++$+++ http://www.dailyscript.com/scripts/Life.txt
+m121 +++$+++ little nicky +++$+++ http://www.dailyscript.com/scripts/Little+Nicky.txt
+m122 +++$+++ logan's run +++$+++ http://www.dailyscript.com/scripts/Logans+Run.txt
+m123 +++$+++ lost highway +++$+++ http://www.dailyscript.com/scripts/Lost+Highway.txt
+m124 +++$+++ lost horizon +++$+++ http://www.dailyscript.com/scripts/Lost_Horizon.html
+m125 +++$+++ men in black +++$+++ http://www.dailyscript.com/scripts/Men+In+Black.txt
+m126 +++$+++ minority report +++$+++ http://www.dailyscript.com/scripts/Minority%20Report.txt
+m127 +++$+++ made +++$+++ http://www.dailyscript.com/scripts/Made.txt
+m128 +++$+++ malcolm x +++$+++ http://www.dailyscript.com/scripts/Malcolm+X.txt
+m129 +++$+++ man on fire +++$+++ http://www.dailyscript.com/scripts/Man+On+Fire.txt
+m130 +++$+++ marty +++$+++ http://www.dailyscript.com/scripts/Marty.txt
+m131 +++$+++ mash +++$+++ http://www.dailyscript.com/scripts/MASH.txt
+m132 +++$+++ meet john doe +++$+++ http://www.dailyscript.com/scripts/Meet_John_Doe.html
+m133 +++$+++ metro +++$+++ http://www.dailyscript.com/scripts/Metro.txt
+m134 +++$+++ metropolis +++$+++ http://www.angelfire.com/movies/ridleyscott/script/Metropolis.txt
+m135 +++$+++ mighty morphin power rangers +++$+++ http://www.hundland.org/scripts/MightyMorphinPowerRangers.txt
+m136 +++$+++ mobsters +++$+++ http://www.dailyscript.com/scripts/Mobsters.txt
+m137 +++$+++ monkeybone +++$+++ http://www.dailyscript.com/scripts/MonkeyBone_script.htm
+m138 +++$+++ my mother dreams the satan's disciples in new york +++$+++ http://www.moviemalls.com/papers/MotherDreamsFD.txt
+m139 +++$+++ mr. smith goes to washington +++$+++ http://www.dailyscript.com/scripts/Mr%20Smith%20Goes%20To%20Washington.txt
+m140 +++$+++ mr. deeds goes to town +++$+++ http://www.dailyscript.com/scripts/Mr._Deeds_Goes_to_Town.html
+m141 +++$+++ mumford +++$+++ http://www.dailyscript.com/scripts/Mumford.txt
+m142 +++$+++ the mummy +++$+++ http://www.dailyscript.com/scripts/Mummy,+The.txt
+m143 +++$+++ mystery men +++$+++ http://www.dailyscript.com/scripts/Mystery+Men.txt
+m144 +++$+++ napoleon +++$+++ http://web.archive.org/web/20010625040837/www.interlink.es/cookies/guion/Napoleon.txt
+m145 +++$+++ next friday +++$+++ http://www.dailyscript.com/scripts/Next_Friday.html
+m146 +++$+++ nick of time +++$+++ http://www.dailyscript.com/scripts/Nick-Of-Time-script.html
+m147 +++$+++ the night of the hunter +++$+++ http://www.dailyscript.com/scripts/Night+Of+The+Hunter,+The.txt
+m148 +++$+++ a nightmare on elm street +++$+++ http://www.dailyscript.com/scripts/Nightmare+On+Elm+Street,+A.txt
+m149 +++$+++ ninotchka +++$+++ http://www.dailyscript.com/scripts/Ninotchka.txt
+m150 +++$+++ nixon +++$+++ http://home.online.no/~bhundlan/scripts/Nixon.txt
+m151 +++$+++ no country for old men +++$+++ http://www.dailyscript.com/scripts/No%20Country%20For%20Old%20Men%20(Shooting).txt
+m152 +++$+++ nurse betty +++$+++ http://www.dailyscript.com/scripts/Nurse%20Betty.txt
+m153 +++$+++ o brother, where art thou? +++$+++ http://www.dailyscript.com/scripts/O%20Brother%20Where%20Art%20Thou.txt
+m154 +++$+++ an officer and a gentleman +++$+++ http://www.dailyscript.com/scripts/Officer+And+A+Gentleman,+An.txt
+m155 +++$+++ panic room +++$+++ http://www.dailyscript.com/scripts/Panic_Room_Koepp.html
+m156 +++$+++ panther +++$+++ http://www.dailyscript.com/scripts/Panther.txt
+m157 +++$+++ the patriot +++$+++ http://www.dailyscript.com/scripts/Patriot_1999.html
+m158 +++$+++ pet sematary +++$+++ http://www.dailyscript.com/scripts/Pet-Sematary.html
+m159 +++$+++ pirates of the caribbean +++$+++ http://www.wordplayer.com/archives/PIRATES.cover.html
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+m575 +++$+++ transatlantic merry-go-round +++$+++ http://www.aellea.com/emruf3/tm.html
+m576 +++$+++ tombstone +++$+++ http://www.dailyscript.com/scripts/tomb_stone.html
+m577 +++$+++ tomorrow never dies +++$+++ http://www.awesomefilm.com/script/tomorrowneverdies.txt
+m578 +++$+++ to sleep with anger +++$+++ http://www.dailyscript.com/scripts/tosleepwithanger.html
+m579 +++$+++ toy story +++$+++ http://www.dailyscript.com/scripts/toy_story.html
+m580 +++$+++ the magic toyshop +++$+++ http://www.aellea.com/emruf3/toyshop.html
+m581 +++$+++ traffic +++$+++ http://www.dailyscript.com/scripts/Traffic.txt
+m582 +++$+++ trainspotting +++$+++ http://www.awesomefilm.com/script/trainspotting.html
+m583 +++$+++ star trek v: the final frontier +++$+++ http://www.scifiscripts.com/scripts/trek5.txt
+m584 +++$+++ tron +++$+++ http://www.scifiscripts.com/scripts/tron.txt
+m585 +++$+++ true lies +++$+++ http://www.awesomefilm.com/script/truelies.txt
+m586 +++$+++ true romance +++$+++ http://www.awesomefilm.com/script/trueromance.html
+m587 +++$+++ twelve monkeys +++$+++ http://www.horrorlair.com/scripts/twelvemonkeys.html
+m588 +++$+++ u-turn +++$+++ http://www.dailyscript.com/scripts/u-turn_early.html
+m589 +++$+++ u turn +++$+++ http://www.dailyscript.com/scripts/u-turn_shooting.html
+m590 +++$+++ unbreakable +++$+++ http://www.dailyscript.com/scripts/Unbreakable.txt
+m591 +++$+++ unforgiven +++$+++ http://www.dailyscript.com/scripts/Unforgiven.txt
+m592 +++$+++ the usual suspects +++$+++ http://www.dailyscript.com/scripts/Usual%20Suspects,%20The.txt
+m593 +++$+++ verdict +++$+++ http://www.scifiscripts.com/msol/verdict.txt
+m594 +++$+++ vertigo +++$+++ http://www.dailyscript.com/scripts/Vertigo.txt
+m595 +++$+++ very bad things +++$+++ http://www.dailyscript.com/scripts/Very+Bad+Things.txt
+m596 +++$+++ viridiana +++$+++ http://www.aellea.com/script/viridiana.txt
+m597 +++$+++ virtuosity +++$+++ http://www.dailyscript.com/scripts/virtuosity.html
+m598 +++$+++ wag the dog +++$+++ http://www.dailyscript.com/scripts/wag-the-dog_production.html
+m599 +++$+++ wall street +++$+++ http://www.dailyscript.com/scripts/wall_street.html
+m600 +++$+++ waxwork +++$+++ http://www.horrorlair.com/scripts/waxwork.txt
+m601 +++$+++ what lies beneath +++$+++ http://www.dailyscript.com/scripts/What+Lies+Beneath.txt
+m602 +++$+++ what women want +++$+++ http://www.awesomefilm.com/script/whatwomenwant.html
+m603 +++$+++ the witching hour +++$+++ http://www.scifiscripts.com/scripts/whitching_hour.txt
+m604 +++$+++ white angel +++$+++ http://www.horrorlair.com/scripts/whiteang.txt
+m605 +++$+++ who's your daddy? +++$+++ http://www.cvisual.com/film-techniques/whos-your-daddy-by-maria-veltre-and-jack-sekowski.txt
+m606 +++$+++ wild things +++$+++ http://www.dailyscript.com/scripts/Wild+Things.txt
+m607 +++$+++ wild wild west +++$+++ http://www.scifiscripts.com/scripts/wildwest.txt
+m608 +++$+++ willow +++$+++ http://www.scifiscripts.com/scripts/willow.txt
+m609 +++$+++ witness +++$+++ http://www.dailyscript.com/scripts/Witness.txt
+m610 +++$+++ the wizard of oz +++$+++ http://www.scifiscripts.com/scripts/wizoz.txt
+m611 +++$+++ the world is not enough +++$+++ http://www.awesomefilm.com/script/worldisnotenough.txt
+m612 +++$+++ watchmen +++$+++ http://www.scifiscripts.com/scripts/wtchmn.txt
+m613 +++$+++ xxx +++$+++ http://www.dailyscript.com/scripts/xXx.txt
+m614 +++$+++ x-men +++$+++ http://www.scifiscripts.com/scripts/xmenthing.txt
+m615 +++$+++ young frankenstein +++$+++ http://www.horrorlair.com/scripts/young.txt
+m616 +++$+++ zulu dawn +++$+++ http://www.aellea.com/script/zuludawn.txt