├── LICENSE ├── README.md ├── example.ipynb ├── extract.py ├── go.png └── look.png /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Bert Pretrained Token Embeddings 2 | 3 | BERT([BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)) yields pretrained token (=subword) embeddings. Let's extract and save them in the [word2vec](https://code.google.com/archive/p/word2vec/) format so that they can be used for downstream tasks. 4 | 5 | ## Requirements 6 | * pytorch_pretrained_bert 7 | * NumPy 8 | * tqdm 9 | 10 | ## Extraction 11 | * Check `extract.py`. 12 | 13 | ## Bert (Pretrained) Token Embeddings in word2vec format 14 | | Models | # Vocab | # Dim | Notes | 15 | |--|--|--|--| 16 | |[bert-base-uncased](https://dl.dropboxusercontent.com/s/hcj1oh8ltqhqrk4/bert-base-uncased.30522.768d.vec.tar.gz) | 30,522 | 768 || 17 | |[bert-large-uncased](https://dl.dropboxusercontent.com/s/kdf932v3taa22zp/bert-large-uncased.30522.1024d.vec.tar.gz) | 30,522 | 1024 || 18 | |[bert-base-cased](https://dl.dropboxusercontent.com/s/7wb92gvc40jxvz9/bert-base-cased.28996.768d.vec.tar.gz) | 28,996 | 768 || 19 | | [bert-large-cased](https://dl.dropboxusercontent.com/s/ke68kjw7yui39s7/bert-large-cased.28996.1024d.vec.tar.gz) | 28,996 | 1024 || 20 | | [bert-base-multilingual-cased](https://dl.dropboxusercontent.com/s/kmyh9cxmu6hb1jx/bert-base-multilingual-cased.119547.768d.vec.tar.gz)| 119,547 | 768 | Recommended | 21 | |[bert-base-multilingual-uncased](https://dl.dropboxusercontent.com/s/j2vz6hue7pze0ae/bert-base-multilingual-uncased.105879.768d.vec.tar.gz)| 30,522 | 768| Not recommended| 22 | |[bert-base-chinese](https://dl.dropboxusercontent.com/s/gzb6kfjipw591vz/bert-base-chinese.21128.768d.vec.tar.gz)|21,128 | 768 || 23 | 24 | ## Example 25 | * Check `example.ipynb` to see how to load (sub-)word vectors with [gensim](https://github.com/RaRe-Technologies/gensim) and plot them in 2d space using [tSNE](https://lvdmaaten.github.io/tsne/). 26 | 27 | * Related tokens to look
28 | 29 | * Related tokens to ##go
30 | -------------------------------------------------------------------------------- /example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 137, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import gensim\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "from sklearn.manifold import TSNE\n", 13 | "%matplotlib inline" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 150, 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "'1.15.4'" 25 | ] 26 | }, 27 | "execution_count": 150, 28 | "metadata": {}, 29 | "output_type": "execute_result" 30 | } 31 | ], 32 | "source": [ 33 | "np.__version__" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 151, 39 | "metadata": {}, 40 | "outputs": [ 41 | { 42 | "data": { 43 | "text/plain": [ 44 | "'3.7.0'" 45 | ] 46 | }, 47 | "execution_count": 151, 48 | "metadata": {}, 49 | "output_type": "execute_result" 50 | } 51 | ], 52 | "source": [ 53 | "gensim.__version__" 54 | ] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "metadata": {}, 59 | "source": [ 60 | "Let's load pretrained bert token vectors and project them to 2d space using tSNE." 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | "# Load data" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 181, 73 | "metadata": {}, 74 | "outputs": [], 75 | "source": [ 76 | "f = \"bert-base-uncased.30522.768d.vec\"\n", 77 | "# f = 'bert-base-cased.28996.768d.vec'\n", 78 | "# f = 'bert-base-multilingual-uncased.105879.768d.vec'\n", 79 | "# f = 'bert-base-chinese.21128.768d.vec'\n", 80 | "# f = 'bert-base-multilingual-cased.119547.768d.vec'\n", 81 | "# f = 'bert-base-uncased.30522.768d.vec'\n", 82 | "# f = 'bert-large-cased.28996.1024d.vec'" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 135, 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "model = gensim.models.KeyedVectors.load_word2vec_format(f, binary=False)" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "metadata": {}, 97 | "source": [ 98 | "# Find most related tokens" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 191, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "data": { 108 | "text/plain": [ 109 | "[('feel', 0.4074964225292206),\n", 110 | " ('style', 0.3370037078857422),\n", 111 | " ('get', 0.32385802268981934),\n", 112 | " ('think', 0.3120831251144409),\n", 113 | " ('work', 0.3055831491947174),\n", 114 | " ('pose', 0.2996072769165039),\n", 115 | " ('point', 0.2994779348373413),\n", 116 | " ('follow', 0.29945236444473267),\n", 117 | " ('fashion', 0.29616934061050415),\n", 118 | " ('eyes', 0.2930969297885895)]" 119 | ] 120 | }, 121 | "execution_count": 191, 122 | "metadata": {}, 123 | "output_type": "execute_result" 124 | } 125 | ], 126 | "source": [ 127 | "model.most_similar(\"look\")" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "# Plot" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 183, 140 | "metadata": {}, 141 | "outputs": [], 142 | "source": [ 143 | "\n", 144 | "def tsne(query, topn=10):\n", 145 | " results = model.wv.similar_by_word(query, topn=topn)\n", 146 | " words = [query]+[r[0] for r in results]\n", 147 | " wordvectors = np.array(model[query]+[model[w] for w in words], np.float32)\n", 148 | " reduced = TSNE(n_components=2).fit_transform(wordvectors)\n", 149 | " plt.figure(figsize=(20, 20), dpi=100)\n", 150 | " max_x = np.amax(reduced, axis=0)[0]\n", 151 | " max_y = np.amax(reduced, axis=0)[1]\n", 152 | " plt.xlim((-max_x, max_x))\n", 153 | " plt.ylim((-max_y, max_y))\n", 154 | " plt.scatter(reduced[:, 0], reduced[:, 1], s=20, c=[\"r\"] + [\"b\"]*(len(reduced)-1))\n", 155 | " \n", 156 | " for i in range(len(words)):\n", 157 | " target_word = words[i]\n", 158 | " # print(target_word)\n", 159 | " x = reduced[i, 0]\n", 160 | " y = reduced[i, 1]\n", 161 | " plt.annotate(target_word, (x, y))\n", 162 | " plt.axis('off')\n", 163 | "\n", 164 | " plt.show()" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": 182, 170 | "metadata": {}, 171 | "outputs": [ 172 | { 173 | "name": "stderr", 174 | "output_type": "stream", 175 | "text": [ 176 | "/Users/ryan/pytorch1.0/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n", 177 | " This is separate from the ipykernel package so we can avoid doing imports until\n" 178 | ] 179 | }, 180 | { 181 | "data": { 182 | "image/png": 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" 185 | ] 186 | }, 187 | "metadata": { 188 | "needs_background": "light" 189 | }, 190 | "output_type": "display_data" 191 | } 192 | ], 193 | "source": [ 194 | "tsne(\"look\", 30)" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 176, 200 | "metadata": { 201 | "scrolled": false 202 | }, 203 | "outputs": [ 204 | { 205 | "name": "stderr", 206 | "output_type": "stream", 207 | "text": [ 208 | "/Users/ryan/pytorch1.0/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n", 209 | " This is separate from the ipykernel package so we can avoid doing imports until\n" 210 | ] 211 | }, 212 | { 213 | "data": { 214 | "image/png": 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215 | "text/plain": [ 216 | "
" 217 | ] 218 | }, 219 | "metadata": { 220 | "needs_background": "light" 221 | }, 222 | "output_type": "display_data" 223 | } 224 | ], 225 | "source": [ 226 | "tsne(\"##go\", 30)" 227 | ] 228 | }, 229 | { 230 | "cell_type": "code", 231 | "execution_count": null, 232 | "metadata": {}, 233 | "outputs": [], 234 | "source": [] 235 | } 236 | ], 237 | "metadata": { 238 | "kernelspec": { 239 | "display_name": "Python 3", 240 | "language": "python", 241 | "name": "python3" 242 | }, 243 | "language_info": { 244 | "codemirror_mode": { 245 | "name": "ipython", 246 | "version": 3 247 | }, 248 | "file_extension": ".py", 249 | "mimetype": "text/x-python", 250 | "name": "python", 251 | "nbconvert_exporter": "python", 252 | "pygments_lexer": "ipython3", 253 | "version": "3.6.4" 254 | } 255 | }, 256 | "nbformat": 4, 257 | "nbformat_minor": 2 258 | } 259 | -------------------------------------------------------------------------------- /extract.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | np.set_printoptions(threshold=np.nan) 4 | from multiprocessing import Pool 5 | import re 6 | from tqdm import tqdm 7 | 8 | import os 9 | os.system("pip install pytorch_pretrained_bert") 10 | from pytorch_pretrained_bert import BertTokenizer, BertModel 11 | 12 | def get_embeddings(mname): 13 | '''Gets pretrained embeddings of Bert-tokenized tokens or subwords 14 | mname: string. model name. 15 | ''' 16 | print("# Model name:", mname) 17 | 18 | print("# Load pre-trained model tokenizer (vocabulary)") 19 | tokenizer = BertTokenizer.from_pretrained(mname) 20 | 21 | print("# Construct vocab") 22 | vocab = [token for token in tokenizer.vocab] 23 | 24 | print("# Load pre-trained model") 25 | model = BertModel.from_pretrained(mname) 26 | 27 | print("# Load word embeddings") 28 | emb = model.embeddings.word_embeddings.weight.data 29 | emb = emb.numpy() 30 | 31 | print("# Write") 32 | with open("{}.{}.{}d.vec".format(mname, len(vocab), emb.shape[-1]), "w") as fout: 33 | fout.write("{} {}\n".format(len(vocab), emb.shape[-1])) 34 | assert len(vocab)==len(emb), "The number of vocab and embeddings MUST be identical." 35 | for token, e in zip(vocab, emb): 36 | e = np.array2string(e, max_line_width=np.inf)[1:-1] 37 | e = re.sub("[ ]+", " ", e) 38 | fout.write("{} {}\n".format(token, e)) 39 | 40 | if __name__ == "__main__": 41 | mnames = ( 42 | "bert-base-uncased", 43 | "bert-large-uncased", 44 | "bert-base-cased", 45 | "bert-large-cased", 46 | "bert-base-multilingual-cased", 47 | "bert-base-multilingual-uncased", 48 | "bert-base-chinese" 49 | ) 50 | 51 | p = Pool(16) 52 | with tqdm(total=len(mnames)) as pbar: 53 | for _ in tqdm(p.imap(get_embeddings, mnames)): 54 | pbar.update() -------------------------------------------------------------------------------- /go.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kyubyong/bert-token-embeddings/73d68466be290551b4a9a31d4a02a7330cac4ce3/go.png -------------------------------------------------------------------------------- /look.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kyubyong/bert-token-embeddings/73d68466be290551b4a9a31d4a02a7330cac4ce3/look.png --------------------------------------------------------------------------------