LocalWisdom/PurpleCBoW
08
1{2 "cells": [3 {4 "cell_type": "code",5 "metadata": {6 "collapsed": false,7 "scrolled": true8 },9 "source": [10 "%uv pip install datasets huggingface_hub"11 ],12 "execution_count": 1,13 "outputs": [14 {15 "output_type": "stream",16 "name": "stdout",17 "text": [18 "\u001b[2mUsing Python 3.12.6 environment at: /usr/local\u001b[0m\r\n",19 "\u001b[37m\u280b\u001b[0m \u001b[2mResolving dependencies... \u001b[0m\r\u001b[2K\u001b[37m\u280b\u001b[0m \u001b[2mResolving dependencies... \u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mResolving dependencies... \u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mdatasets==5.0.0 \u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mhuggingface-hub==0.34.4 \u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mfilelock==3.13.1 \u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mnumpy==2.1.2 \u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mpyarrow==25.0.0 \u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mdill==0.4.1 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\u001b[0m\r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mfrozenlist==1.4.1 \u001b[0m\r\u001b[2K\u001b[2mResolved \u001b[1m36 packages\u001b[0m \u001b[2min 159ms\u001b[0m\u001b[0m\r\n",20 "\u001b[37m\u280b\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/0) \r\u001b[2K\u001b[37m\u280b\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/5) \r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/5) \r\u001b[2K\u001b[37m\u2819\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/5)\r\n",21 "\u001b[2mxxhash \u001b[0m \u001b[32m\u001b[2m------------------------------\u001b[0m\u001b[0m 0 B/215.30 KiB \u001b[1A\r\u001b[2K\u001b[1B\r\u001b[2K\u001b[1A\u001b[37m\u2819\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/5)\r\n",22 "\u001b[2mxxhash \u001b[0m \u001b[32m---\u001b[2m---------------------------\u001b[0m\u001b[0m 16.00 KiB/215.30 KiB \u001b[1A\r\u001b[2K\u001b[1B\r\u001b[2K\u001b[1A\u001b[37m\u2819\u001b[0m \u001b[2mPreparing packages...\u001b[0m (0/5)\r\n",23 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\u001b[1mdatasets\u001b[0m\u001b[2m==5.0.0\u001b[0m\r\n",113 " \u001b[32m+\u001b[39m \u001b[1mdill\u001b[0m\u001b[2m==0.4.1\u001b[0m\r\n",114 " \u001b[32m+\u001b[39m \u001b[1mmultiprocess\u001b[0m\u001b[2m==0.70.19\u001b[0m\r\n",115 " \u001b[32m+\u001b[39m \u001b[1mpyarrow\u001b[0m\u001b[2m==25.0.0\u001b[0m\r\n",116 " \u001b[32m+\u001b[39m \u001b[1mxxhash\u001b[0m\u001b[2m==3.8.1\u001b[0m\r\n",117 "Note: you may need to restart the kernel to use updated packages.\n"118 ]119 }120 ]121 },122 {123 "cell_type": "code",124 "metadata": {125 "collapsed": false,126 "scrolled": true127 },128 "source": [129 "import torch\n",130 "import random\n",131 "import torch.nn as nn\n",132 "import torch.optim as optim\n",133 "from torch.utils.data import DataLoader\n",134 "from datasets import load_dataset\n",135 "from huggingface_hub import PyTorchModelHubMixin, notebook_login"136 ],137 "execution_count": 7,138 "outputs": []139 },140 {141 "cell_type": "code",142 "metadata": {143 "collapsed": false,144 "scrolled": true145 },146 "source": [147 "ds_splits = load_dataset(\"jakartaresearch/cerpen-corpus\", split=\"train\", revision=\"refs/convert/parquet\").train_test_split(test_size=0.1)\n",148 "ds_train = ds_splits[\"train\"]\n",149 "ds_test = ds_splits[\"test\"]\n",150 "\n",151 "def gen(rows):\n",152 " sentences = []\n",153 " for content in rows[\"content\"]:\n",154 " stripped = ''.join(c.lower() for c in content if c.isalpha() or c == ' ')\n",155 " sentences.append(stripped)\n",156 " return dict(sentence1=sentences)\n",157 "\n",158 "ds_train = ds_train.map(gen, batched=True, remove_columns=ds_train.column_names)\n",159 "ds_test = ds_test.map(gen, batched=True, remove_columns=ds_test.column_names)"160 ],161 "execution_count": 8,162 "outputs": [163 {164 "output_type": "display_data",165 "data": {166 "application/vnd.jupyter.widget-view+json": {167 "model_id": "044dc56786ea411381769c8664d30915",168 "version_minor": 0.0,169 "version_major": 2.0170 },171 "text/plain": "0000.parquet: 0%| | 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"code",215 "metadata": {216 "collapsed": false,217 "scrolled": true218 },219 "source": [220 "class BOW:\n",221 " def __init__(self):\n",222 " self.vocab = dict()\n",223 "\n",224 " @property\n",225 " def features(self):\n",226 " return list(self.vocab.keys())\n",227 "\n",228 " def train(self, ds):\n",229 " for row in ds:\n",230 " sentence = row[\"sentence1\"]\n",231 " words = list(filter(lambda x: x, sentence.split()))\n",232 "\n",233 " for word in words:\n",234 " if word not in self.vocab:\n",235 " self.vocab[word] = 0\n",236 " print(f\"Vocab size: {len(self.vocab)}\", end='\\r')\n",237 " print()\n",238 "\n",239 " def bag(self, sentence):\n",240 " counter = self.vocab.copy()\n",241 "\n",242 " # sanitation\n",243 " sanitized = ''.join(c for c in sentence if c.isalpha() or c == ' ')\n",244 " words = list(filter(lambda x: x, sanitized.split()))\n",245 "\n",246 " for word in words:\n",247 " if word not in counter:\n",248 " continue\n",249 " counter[word] = counter[word] + 1\n",250 "\n",251 " return list(counter.values())"252 ],253 "execution_count": 9,254 "outputs": []255 },256 {257 "cell_type": "code",258 "metadata": {259 "collapsed": false,260 "scrolled": true261 },262 "source": [263 "class CBOW(nn.Module, BOW, PyTorchModelHubMixin):\n",264 " def __init__(self, config):\n",265 " BOW.__init__(self)\n",266 " nn.Module.__init__(self)\n",267 "\n",268 " self.embedding_size = config[\"embedding_size\"]\n",269 " self.context_window = config[\"context_window\"]\n",270 " self.epoch_num = config[\"epoch\"]\n",271 "\n",272 " self.embeddings = None\n",273 " self.linear = None\n",274 "\n",275 " self.tokens = None\n",276 "\n",277 " def train(self, ds):\n",278 " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",279 " BOW.train(self, ds)\n",280 "\n",281 " ds = self._normalize_ds(ds)\n",282 "\n",283 " vocab_size = len(self.vocab)\n",284 "\n",285 " self.embeddings = nn.Embedding(vocab_size, self.embedding_size)\n",286 " self.linear = nn.Linear(self.embedding_size, vocab_size)\n",287 " self.to(device)\n",288 "\n",289 " loss_f = nn.CrossEntropyLoss()\n",290 " optimizer = optim.Adam(self.parameters(), lr=1e-4)\n",291 "\n",292 " dl = DataLoader(\n",293 " ds.with_format(\"torch\"),\n",294 " batch_size=32, shuffle=True,\n",295 " pin_memory=True if device.type == \"cuda\" else False\n",296 " )\n",297 "\n",298 " for epoch in range(self.epoch_num):\n",299 " for batch in dl:\n",300 " x = batch[\"context\"].to(device, non_blocking=True)\n",301 " y = batch[\"label\"].to(device, non_blocking=True)\n",302 "\n",303 " output = self(x)\n",304 " loss = loss_f(output, y)\n",305 " optimizer.zero_grad()\n",306 " loss.backward()\n",307 " optimizer.step()\n",308 "\n",309 " loss = loss.item()\n",310 " print(f\"{epoch = } | {loss = }\")\n",311 "\n",312 " def forward(self, inputs):\n",313 " embeddings = self.embeddings(inputs).mean(1).squeeze(1)\n",314 " return self.linear(embeddings)\n",315 "\n",316 " def embed(self, word):\n",317 " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",318 " return self.embeddings(torch.tensor([self.tokens[word]], device=device))\n",319 "\n",320 " def _normalize_ds(self, ds):\n",321 " self.tokens = {w: i for i, w in enumerate(self.features)}\n",322 "\n",323 " def normalizer(rows):\n",324 " contexts = []\n",325 " labels = []\n",326 "\n",327 " for sentence in rows[\"sentence1\"]:\n",328 " words = sentence.split()\n",329 " for i in range(self.context_window, len(words) - self.context_window):\n",330 " context = []\n",331 " for j in range(i - self.context_window, i):\n",332 " context.append(self.tokens[words[j]])\n",333 " for j in range(i + 1, i - self.context_window + 1):\n",334 " context.append(self.tokens[words[j]])\n",335 "\n",336 " contexts.append(context)\n",337 " labels.append(self.tokens[words[i]])\n",338 "\n",339 " return dict(context=contexts, label=labels)\n",340 " \n",341 " return ds.map(normalizer, batched=True, remove_columns=ds_train.column_names)"342 ],343 "execution_count": 18,344 "outputs": []345 },346 {347 "cell_type": "code",348 "metadata": {349 "collapsed": false,350 "scrolled": false351 },352 "source": [353 "PurpleCBoW = CBOW(dict(embedding_size=100, context_window=2, epoch=200))\n",354 "PurpleCBoW.train(ds_train)"355 ],356 "execution_count": 13,357 "outputs": [358 {359 "output_type": "stream",360 "name": "stdout",361 "text": [362 "Vocab size: 1\rVocab size: 2\rVocab size: 3\rVocab size: 4\rVocab size: 5\rVocab size: 6\rVocab size: 7\rVocab size: 8\rVocab size: 9\rVocab size: 10\rVocab size: 11\rVocab size: 12\rVocab size: 13\rVocab size: 14\rVocab size: 15\rVocab size: 16\rVocab size: 17\rVocab size: 18\rVocab size: 19\rVocab size: 20\rVocab size: 21\rVocab size: 22\rVocab size: 23\rVocab size: 24\rVocab size: 25\rVocab size: 26\rVocab size: 27\rVocab size: 28\rVocab size: 29\rVocab size: 30\rVocab size: 31\rVocab size: 32\rVocab size: 33\rVocab size: 34\rVocab size: 35\rVocab size: 36\rVocab size: 37\rVocab size: 38\rVocab 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"epoch = 8 | loss = 7.360550880432129\n",390 "epoch = 9 | loss = 7.328634262084961\n",391 "epoch = 10 | loss = 7.310323715209961\n",392 "epoch = 11 | loss = 7.074193954467773\n",393 "epoch = 12 | loss = 7.2928338050842285\n",394 "epoch = 13 | loss = 6.252838611602783\n",395 "epoch = 14 | loss = 6.382636547088623\n",396 "epoch = 15 | loss = 6.322940349578857\n",397 "epoch = 16 | loss = 5.688790798187256\n",398 "epoch = 17 | loss = 5.764755725860596\n",399 "epoch = 18 | loss = 6.397175312042236\n",400 "epoch = 19 | loss = 5.829770088195801\n",401 "epoch = 20 | loss = 6.837596893310547\n",402 "epoch = 21 | loss = 6.44610071182251\n",403 "epoch = 22 | loss = 5.788278579711914\n",404 "epoch = 23 | loss = 5.76959228515625\n",405 "epoch = 24 | loss = 5.928990364074707\n",406 "epoch = 25 | loss = 5.474532127380371\n",407 "epoch = 26 | loss = 5.720479965209961\n",408 "epoch = 27 | loss = 5.741850852966309\n",409 "epoch = 28 | loss = 5.363188743591309\n",410 "epoch = 29 | loss = 5.4685587882995605\n",411 "epoch = 30 | loss = 5.121748447418213\n",412 "epoch = 31 | loss = 4.968938827514648\n",413 "epoch = 32 | loss = 5.124796390533447\n",414 "epoch = 33 | loss = 4.905245780944824\n",415 "epoch = 34 | loss = 4.998361110687256\n",416 "epoch = 35 | loss = 5.102531909942627\n",417 "epoch = 36 | loss = 5.444268703460693\n",418 "epoch = 37 | loss = 4.72947359085083\n",419 "epoch = 38 | loss = 5.334191799163818\n",420 "epoch = 39 | loss = 4.757353782653809\n",421 "epoch = 40 | loss = 4.50326681137085\n",422 "epoch = 41 | loss = 3.817579984664917\n",423 "epoch = 42 | loss = 4.811537265777588\n",424 "epoch = 43 | loss = 4.623417854309082\n",425 "epoch = 44 | loss = 4.0428338050842285\n",426 "epoch = 45 | loss = 4.453884601593018\n",427 "epoch = 46 | loss = 4.5467071533203125\n",428 "epoch = 47 | loss = 4.249697208404541\n",429 "epoch = 48 | loss = 3.6158692836761475\n",430 "epoch = 49 | loss = 4.197793483734131\n",431 "epoch = 50 | loss = 4.6146650314331055\n",432 "epoch = 51 | loss = 3.4962539672851562\n",433 "epoch = 52 | loss = 4.3625993728637695\n",434 "epoch = 53 | loss = 3.6078691482543945\n",435 "epoch = 54 | loss = 4.121210098266602\n",436 "epoch = 55 | loss = 4.397915363311768\n",437 "epoch = 56 | loss = 4.233282566070557\n",438 "epoch = 57 | loss = 3.526451826095581\n",439 "epoch = 58 | loss = 3.314310312271118\n",440 "epoch = 59 | loss = 3.9249608516693115\n",441 "epoch = 60 | loss = 3.766606330871582\n",442 "epoch = 61 | loss = 3.3864753246307373\n",443 "epoch = 62 | loss = 3.6759586334228516\n",444 "epoch = 63 | loss = 3.5711231231689453\n",445 "epoch = 64 | loss = 3.478040933609009\n",446 "epoch = 65 | loss = 3.2694404125213623\n",447 "epoch = 66 | loss = 3.582531213760376\n",448 "epoch = 67 | loss = 3.7063746452331543\n",449 "epoch = 68 | loss = 3.376821517944336\n",450 "epoch = 69 | loss = 4.0082879066467285\n",451 "epoch = 70 | loss = 3.0569915771484375\n",452 "epoch = 71 | loss = 4.533447265625\n",453 "epoch = 72 | loss = 3.643012285232544\n",454 "epoch = 73 | loss = 2.688606023788452\n",455 "epoch = 74 | loss = 3.026968002319336\n",456 "epoch = 75 | loss = 3.6498138904571533\n",457 "epoch = 76 | loss = 3.3773820400238037\n",458 "epoch = 77 | loss = 3.3013365268707275\n",459 "epoch = 78 | loss = 3.1055846214294434\n",460 "epoch = 79 | loss = 3.0534751415252686\n",461 "epoch = 80 | loss = 4.061032772064209\n",462 "epoch = 81 | loss = 3.473909854888916\n",463 "epoch = 82 | loss = 4.190488815307617\n",464 "epoch = 83 | loss = 3.3922548294067383\n",465 "epoch = 84 | loss = 3.440290689468384\n",466 "epoch = 85 | loss = 3.3444559574127197\n",467 "epoch = 86 | loss = 4.087173938751221\n",468 "epoch = 87 | loss = 2.554356575012207\n",469 "epoch = 88 | loss = 3.064892053604126\n",470 "epoch = 89 | loss = 3.637958288192749\n",471 "epoch = 90 | loss = 3.141202211380005\n",472 "epoch = 91 | loss = 2.4781088829040527\n",473 "epoch = 92 | loss = 2.913055181503296\n",474 "epoch = 93 | loss = 3.5907890796661377\n",475 "epoch = 94 | loss = 3.187624931335449\n",476 "epoch = 95 | loss = 3.2728512287139893\n",477 "epoch = 96 | loss = 3.2858381271362305\n",478 "epoch = 97 | loss = 3.658301830291748\n",479 "epoch = 98 | loss = 2.7959542274475098\n",480 "epoch = 99 | loss = 2.3767852783203125\n",481 "epoch = 100 | loss = 2.3135616779327393\n",482 "epoch = 101 | loss = 2.648489475250244\n",483 "epoch = 102 | loss = 3.2753093242645264\n",484 "epoch = 103 | loss = 2.9161806106567383\n",485 "epoch = 104 | loss = 2.6242527961730957\n",486 "epoch = 105 | loss = 2.2493374347686768\n",487 "epoch = 106 | loss = 2.859218120574951\n",488 "epoch = 107 | loss = 2.883864641189575\n",489 "epoch = 108 | loss = 3.011939764022827\n",490 "epoch = 109 | loss = 2.6183881759643555\n",491 "epoch = 110 | loss = 2.4759480953216553\n",492 "epoch = 111 | loss = 3.1420958042144775\n",493 "epoch = 112 | loss = 1.9008381366729736\n",494 "epoch = 113 | loss = 2.6253502368927\n",495 "epoch = 114 | loss = 2.8677332401275635\n",496 "epoch = 115 | loss = 2.811018228530884\n",497 "epoch = 116 | loss = 2.1877284049987793\n",498 "epoch = 117 | loss = 2.361095666885376\n",499 "epoch = 118 | loss = 2.446974992752075\n",500 "epoch = 119 | loss = 3.357314109802246\n",501 "epoch = 120 | loss = 1.9822837114334106\n",502 "epoch = 121 | loss = 1.611732840538025\n",503 "epoch = 122 | loss = 2.604283571243286\n",504 "epoch = 123 | loss = 3.19911527633667\n",505 "epoch = 124 | loss = 2.6321847438812256\n",506 "epoch = 125 | loss = 1.0968663692474365\n",507 "epoch = 126 | loss = 2.114259719848633\n",508 "epoch = 127 | loss = 1.7106822729110718\n",509 "epoch = 128 | loss = 2.3607490062713623\n",510 "epoch = 129 | loss = 2.5654497146606445\n",511 "epoch = 130 | loss = 2.7272660732269287\n",512 "epoch = 131 | loss = 1.977072834968567\n",513 "epoch = 132 | loss = 2.1739447116851807\n",514 "epoch = 133 | loss = 2.1941123008728027\n",515 "epoch = 134 | loss = 2.351308822631836\n",516 "epoch = 135 | loss = 1.8951632976531982\n",517 "epoch = 136 | loss = 1.7176084518432617\n",518 "epoch = 137 | loss = 2.604135274887085\n",519 "epoch = 138 | loss = 2.806469202041626\n",520 "epoch = 139 | loss = 2.178217887878418\n",521 "epoch = 140 | loss = 3.651960611343384\n",522 "epoch = 141 | loss = 2.1654245853424072\n",523 "epoch = 142 | loss = 2.383249044418335\n",524 "epoch = 143 | loss = 1.8410239219665527\n",525 "epoch = 144 | loss = 2.8519270420074463\n",526 "epoch = 145 | loss = 2.453697681427002\n",527 "epoch = 146 | loss = 1.9662528038024902\n",528 "epoch = 147 | loss = 3.0915334224700928\n",529 "epoch = 148 | loss = 2.3326780796051025\n",530 "epoch = 149 | loss = 2.25602650642395\n",531 "epoch = 150 | loss = 1.487765908241272\n",532 "epoch = 151 | loss = 2.698782205581665\n",533 "epoch = 152 | loss = 1.8108799457550049\n",534 "epoch = 153 | loss = 1.8249317407608032\n",535 "epoch = 154 | loss = 1.5529757738113403\n",536 "epoch = 155 | loss = 2.368912935256958\n",537 "epoch = 156 | loss = 2.828925371170044\n",538 "epoch = 157 | loss = 2.2746071815490723\n",539 "epoch = 158 | loss = 2.233288049697876\n",540 "epoch = 159 | loss = 1.6225029230117798\n",541 "epoch = 160 | loss = 2.023333787918091\n",542 "epoch = 161 | loss = 2.282166004180908\n",543 "epoch = 162 | loss = 1.5873664617538452\n",544 "epoch = 163 | loss = 1.5451254844665527\n",545 "epoch = 164 | loss = 1.7463966608047485\n",546 "epoch = 165 | loss = 2.583752393722534\n",547 "epoch = 166 | loss = 1.8475940227508545\n",548 "epoch = 167 | loss = 2.449542284011841\n",549 "epoch = 168 | loss = 1.8342992067337036\n",550 "epoch = 169 | loss = 2.2107322216033936\n",551 "epoch = 170 | loss = 1.7666174173355103\n",552 "epoch = 171 | loss = 1.8336145877838135\n",553 "epoch = 172 | loss = 2.0172605514526367\n",554 "epoch = 173 | loss = 1.3319790363311768\n",555 "epoch = 174 | loss = 1.8589327335357666\n",556 "epoch = 175 | loss = 1.7441205978393555\n",557 "epoch = 176 | loss = 1.4105230569839478\n",558 "epoch = 177 | loss = 1.8907177448272705\n",559 "epoch = 178 | loss = 1.9583863019943237\n",560 "epoch = 179 | loss = 1.5076522827148438\n",561 "epoch = 180 | loss = 2.150567054748535\n",562 "epoch = 181 | loss = 1.5123329162597656\n",563 "epoch = 182 | loss = 2.1223795413970947\n",564 "epoch = 183 | loss = 1.9815623760223389\n",565 "epoch = 184 | loss = 2.0051608085632324\n",566 "epoch = 185 | loss = 2.1949825286865234\n",567 "epoch = 186 | loss = 2.0770041942596436\n",568 "epoch = 187 | loss = 1.4347529411315918\n",569 "epoch = 188 | loss = 2.2815659046173096\n",570 "epoch = 189 | loss = 1.4783754348754883\n",571 "epoch = 190 | loss = 1.5720981359481812\n",572 "epoch = 191 | loss = 1.400451898574829\n",573 "epoch = 192 | loss = 2.6552751064300537\n",574 "epoch = 193 | loss = 2.391258955001831\n",575 "epoch = 194 | loss = 1.52137291431427\n",576 "epoch = 195 | loss = 1.8637100458145142\n",577 "epoch = 196 | loss = 2.101088047027588\n",578 "epoch = 197 | loss = 1.4051120281219482\n",579 "epoch = 198 | loss = 1.265346884727478\n",580 "epoch = 199 | loss = 0.8504002094268799\n"581 ]582 }583 ]584 },585 {586 "cell_type": "code",587 "metadata": {588 "collapsed": false,589 "scrolled": true590 },591 "source": [592 "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",593 "\n",594 "t = random.choice(ds_test[0][\"sentence1\"].split())\n",595 "embedding = PurpleCBoW.embed(t)\n",596 "print(t, '->', embedding)"597 ],598 "execution_count": 17,599 "outputs": [600 {601 "output_type": "stream",602 "name": "stdout",603 "text": [604 "ada -> tensor([[-0.1620, 2.3667, -1.6400, 1.0723, 1.3573, 1.7939, -1.9762, -0.7847,\n",605 " -0.6792, 1.1668, 4.2722, -0.0947, 0.8006, -0.6954, 0.0548, 0.3087,\n",606 " 0.7016, -0.3470, 0.2811, 0.5155, 2.1355, 0.3513, -0.6381, 1.2816,\n",607 " -0.7497, -1.1857, -1.7049, -0.3956, 1.2827, -2.2924, -2.2530, -2.2833,\n",608 " -1.9617, 0.1064, 2.3472, 0.0685, -0.1007, 0.6174, 1.8690, 0.5491,\n",609 " 0.3912, -2.4253, -0.3393, 2.0718, 0.2526, 1.3298, 0.8708, 2.1571,\n",610 " -0.8963, -1.6683, 0.8791, -1.1831, 1.0959, -0.8845, -1.7375, 0.0917,\n",611 " 0.3306, 0.5309, 0.0951, 2.8126, 0.3754, 2.0260, -0.8577, 2.0378,\n",612 " 1.4960, 2.0437, -1.2764, 2.3857, 3.0320, 0.1518, -0.1080, -0.1423,\n",613 " -1.1395, -0.1090, 2.5674, 1.9636, -1.0849, 0.4864, 1.4419, 0.3830,\n",614 " -0.9491, -3.2826, 1.1652, 1.0227, 0.9529, 2.0130, 1.3376, 1.0838,\n",615 " -1.5363, 1.0757, -2.3646, 0.7869, 4.1987, -0.5543, 2.1535, -2.2712,\n",616 " 3.0948, -0.6080, 1.2531, -0.1201]], device='cuda:0',\n",617 " grad_fn=<EmbeddingBackward0>)\n"618 ]619 }620 ]621 },622 {623 "cell_type": "code",624 "metadata": {625 "collapsed": false,626 "scrolled": true627 },628 "source": [629 "PurpleCBoW"630 ],631 "execution_count": 19,632 "outputs": [633 {634 "output_type": "execute_result",635 "execution_count": 19,636 "data": {637 "text/plain": "CBOW(\n (embeddings): Embedding(8254, 100)\n (linear): Linear(in_features=100, out_features=8254, bias=True)\n)"638 },639 "metadata": {}640 }641 ]642 },643 {644 "cell_type": "code",645 "metadata": {646 "collapsed": false,647 "scrolled": true648 },649 "source": [650 "notebook_login()"651 ],652 "execution_count": 22,653 "outputs": [654 {655 "output_type": "display_data",656 "data": {657 "application/vnd.jupyter.widget-view+json": {658 "model_id": "630df8641a9c4094b70be193cd4ebfbb",659 "version_minor": 0.0,660 "version_major": 2.0661 },662 "text/plain": "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv\u2026"663 },664 "metadata": {}665 }666 ]667 },668 {669 "cell_type": "code",670 "metadata": {671 "collapsed": false,672 "scrolled": true673 },674 "source": [675 "PurpleCBoW.push_to_hub(\"LocalWisdom/PurpleCBoW\")"676 ],677 "execution_count": 23,678 "outputs": [679 {680 "output_type": "display_data",681 "data": {682 "application/vnd.jupyter.widget-view+json": {683 "model_id": "cd4c968d5b9e4fc59786263d535cb4f8",684 "version_minor": 0.0,685 "version_major": 2.0686 },687 "text/plain": "Processing Files 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