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se 0.372905 -0.254066 0.466287 -0.329975 0.163037 -0.414140 -0.275146 0.241499 0.079083 -0.217296 0.442085 0.281065 0.057015 -0.304842 0.429797 0.205979 -0.157494 0.728257 -0.224052 0.993231 0.031310 -0.024450 0.477585 -0.620231 0.115212 -0.953851 0.375579 0.046713 0.652765 0.239282 0.458835 -0.917805 0.702363 0.262037 0.248194 0.822507 0.900384 1.142522 0.617748 0.155002 -0.368218 0.228732 0.330622 0.242858 0.252550 -0.399768 0.502582 0.662419 0.119731 0.779007 0.462735 -0.417892 0.046321 0.235241 -0.159850 0.069437 -0.605538 -0.241390 -0.351535 -0.525539 1.466552 -0.062916 0.295482 -0.120041 0.038792 -0.826800 -0.150497 0.239153 0.853614 -0.076565 -0.782148 -0.749976 -0.677527 0.201098 0.098525 0.911642 0.160162 -0.078085 0.442865 0.079189 -0.278777 -0.880515 0.589706 0.396059 -0.390936 -0.402788 0.536575 0.164632 -0.423546 -0.160032 -0.440755 -0.269539 0.340728 -0.148481 0.273876 0.307597 -0.399509 -0.174657 -1.022293 -0.247013 0.147377 -0.272787 0.649478 0.121952 -0.931092 -0.814717 0.056641 -0.234942 0.136662 -0.309147 -0.517496 0.062731 0.273812 -0.152057 -1.145339 0.550157 0.189769 0.359048 0.185547 -0.331876 -0.124074 -0.396704 0.840111 -0.988698 0.411198 -0.229747 -0.247751 0.238939 -0.215126 0.286035 0.209188 0.041701 -0.091131 -0.143031 0.375787 1.082196 -1.159695 0.324199 1.169951 -0.294693 -0.377230 0.583524 0.736195 -0.003272 -0.094001 0.565395 0.316077 -0.084965 -0.190780 -0.227923 0.640415 -0.039713 0.403527 0.226363 -1.217525 0.053199 -0.280179 -0.893980 0.068469 0.128716 0.086554 0.586456 -0.285951 2.099888 -0.251573 -0.195133 -0.207329 0.017349 -0.369136 -0.357951 0.266807 -2.787536 0.682567 -0.605345 0.417853 -0.649459 -0.368829 -0.628165 -0.166052 0.308223 0.962997 0.692238 0.255435 -0.181093 -0.142996 -0.881382 0.080231 -0.610285 0.424429 -1.023603 -0.039398 1.676307 0.315525 -0.365166 0.733207 -0.276144 0.126495 0.527864 -0.975886 0.326098 1.012695 0.149964 0.476959 0.268478 -0.006476 1.262120 0.640142 0.708184 -0.053671 0.088798 0.531199 -0.673983 -0.762500 -0.511095 -0.136482 0.921320 0.242210 -0.747157 -0.031572 0.077771 -0.548909 0.670266 -0.049320 0.457825 0.684757 0.743393 0.444836 -0.509200 0.222441 0.357767 0.312889 0.307847 -0.006155 0.129451 -0.463921 0.266551 0.317176 -0.038752 0.243798 0.299415 0.795827 -0.461203 0.022899 -0.250814 -0.839061 0.273913 0.131354 0.368959 -0.498913 0.077910 0.815555 -0.277852 0.036776 -0.697687 -0.625312 0.235275 0.299427 0.123032 0.928374 -0.449237 0.092009 -0.242876 0.507094 -0.223361 -0.060429 -0.231656 -0.084832 0.058870 0.880697 0.613146 -0.338370 -1.763186 -0.459453 -0.199254 -0.301210 0.478329 -0.423316 -0.207721 -0.909874 -0.012591 0.000159 0.362251 -0.373912 -0.163699 -0.158991 0.281264 -1.204742 -0.611408 0.484403 -0.314443 -0.228787 0.735409 -0.343591 -0.157914 0.948273 -0.447171 -0.194347 0.230596 0.158587 -0.947504 |
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Nru 1.051561 0.636769 0.206703 0.177413 -0.388475 0.489406 0.374307 -0.461489 0.977453 -0.274561 0.213077 -0.201952 0.146547 0.159342 0.527773 -0.079721 -0.630992 1.237121 0.514073 1.108164 0.043086 -0.347800 0.623000 0.593151 -0.008896 -0.875759 0.332967 -0.387894 -0.256343 0.307848 -0.566832 0.262199 -0.227502 -0.587380 0.161968 0.092115 -0.150979 0.306483 0.150587 -0.329164 -0.084598 0.183080 0.153396 -0.182474 0.028071 -0.819545 -0.458500 -0.156378 0.135221 0.218922 -0.395796 0.113408 0.353408 0.777334 -1.347158 -0.522151 -0.533342 -0.480810 0.495931 1.561943 0.907276 -1.121345 0.517214 -0.023861 0.447005 0.159064 -0.226245 -0.143003 0.609869 0.257051 -0.541878 -0.062911 0.185980 -0.165987 1.158236 -0.072091 0.200070 1.191589 0.057192 -0.866023 0.753044 -0.474763 -0.702393 -0.416428 0.889987 0.016849 -0.338559 0.217441 0.112861 -0.774724 -0.160477 -0.533278 0.697108 -0.574561 0.192690 -0.287905 -0.251687 0.703009 0.305839 0.101856 0.223779 -0.187968 -1.148773 -0.295276 -0.143731 -0.102625 0.287051 -0.315551 -0.274699 0.504487 0.080370 0.161998 -0.441845 -0.203569 -0.408919 0.053448 -0.059461 0.405390 -0.170246 -0.204720 -0.357473 -0.539919 0.688214 -0.183999 0.173161 0.586879 -0.155695 0.688013 1.190017 0.459427 0.120386 -1.363739 -0.105654 0.039635 0.226324 -0.347302 -0.108175 0.051323 -0.501006 0.943184 0.529222 0.309413 -0.724326 0.128299 0.074706 0.358127 -0.749574 -0.498542 -0.457399 0.014697 0.540497 -0.092225 0.945202 -0.752567 -1.553183 0.092516 -0.450397 0.461489 -0.271692 0.017011 0.447245 0.667373 0.685508 0.104534 -0.793010 -0.226027 -0.102810 0.031317 -1.128531 0.980135 -0.265819 -2.278039 -0.022276 0.412077 -0.430501 -0.019117 -0.556585 -0.083011 0.822482 -0.299091 0.585991 0.241359 0.780448 0.330011 0.404736 -0.630405 -0.431107 -0.203565 -0.079484 -1.260682 -0.144607 2.479369 0.403968 0.510415 0.329224 -0.159548 -1.048383 0.786677 0.507287 -0.271334 0.421817 -0.088598 0.140096 0.057516 0.799486 -0.161408 0.598658 0.503131 0.522458 0.396230 0.519613 0.912035 -0.352853 -1.555642 -0.293644 0.084419 1.099342 0.088690 0.058738 -1.457028 0.329935 -0.784233 -0.233930 -0.023919 -0.118754 -0.257321 0.016197 -0.011245 -0.122474 -0.111123 0.397677 -0.330193 0.210003 0.037138 0.413871 0.181039 -0.165210 0.315743 -0.164458 0.679220 0.598498 0.071006 -0.627328 0.232964 0.020264 0.579157 0.247046 0.077602 0.334887 -0.028886 -0.183551 -0.239041 0.101097 -0.987768 0.180527 -1.091500 0.075451 0.072463 1.156548 -0.462721 -0.958851 0.590090 0.069445 -0.438798 0.165491 -0.505708 -0.000182 0.480748 0.407600 -0.814697 -0.847723 -0.614741 0.676099 0.379093 -0.023929 0.481602 -0.397605 0.748362 -0.077138 -0.005172 0.577003 -0.696964 0.145799 0.025276 0.400229 -0.053513 -0.210770 -0.328203 -0.276005 -0.451634 0.866343 0.303432 0.058788 0.792002 -0.399571 -0.085570 0.482483 0.347653 -0.318489 -0.382741 |
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Dataset Summary
This repository contains three distinct datasets focusing on Maltese word embeddings:
GloVe Maltese Word Embeddings Embeddings generated using GloVe on the "korpus_malti" dataset, the largest Maltese corpus available.
Word2Vec Maltese Word Embeddings Word embeddings for Maltese obtained using Word2Vec trained on the "korpus_malti" dataset.
PPMI Maltese Word Embeddings Pointwise Mutual Information (PPMI) based word embeddings generated from ConceptNet data via SVD on the co-occurrence matrix.
Languages
- Maltese
Dataset Creation
- GloVe and Word2Vec embeddings were trained using the largest Maltese dataset, "korpus_malti".
- Details of the training parameters for both GloVe and Word2Vec models can be found in the provided scripts.
- PPMI embeddings were trained using ConceptNet data and applying SVD on the co-occurrence matrix.
Contributors
- Daniil Gurgurov
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