update readme
Browse filesSigned-off-by: Anupam Kumar <kyteinsky@gmail.com>
README.md
CHANGED
@@ -1,3 +1,647 @@
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---
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license: apache-2.0
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1 |
+
---
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2 |
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license: apache-2.0
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3 |
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language:
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4 |
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- multilingual
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5 |
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- en
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6 |
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- ru
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7 |
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- es
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8 |
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- fr
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9 |
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- de
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- it
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11 |
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- pt
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- pl
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- nl
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- vi
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- tr
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- sv
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- id
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- ro
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- cs
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- zh
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21 |
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- hu
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22 |
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- ja
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23 |
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- th
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24 |
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- fi
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25 |
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- fa
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26 |
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- uk
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27 |
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- da
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28 |
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- el
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29 |
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- "no"
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30 |
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- bg
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31 |
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- sk
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32 |
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- ko
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33 |
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- ar
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34 |
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- lt
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35 |
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- ca
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36 |
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- sl
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37 |
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- he
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- et
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39 |
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- lv
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40 |
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- hi
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41 |
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- sq
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42 |
+
- ms
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43 |
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- az
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44 |
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- sr
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45 |
+
- ta
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46 |
+
- hr
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47 |
+
- kk
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48 |
+
- is
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49 |
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- ml
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50 |
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- mr
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51 |
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- te
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- af
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- gl
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- fil
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55 |
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- be
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- mk
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- eu
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58 |
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- bn
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59 |
+
- ka
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60 |
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- mn
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61 |
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- bs
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62 |
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- uz
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63 |
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- ur
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- sw
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65 |
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- yue
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66 |
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- ne
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67 |
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- kn
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- kaa
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69 |
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- gu
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70 |
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- si
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71 |
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- cy
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72 |
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- eo
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73 |
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- la
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74 |
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- hy
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75 |
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- ky
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76 |
+
- tg
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77 |
+
- ga
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78 |
+
- mt
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79 |
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- my
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80 |
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- km
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81 |
+
- tt
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82 |
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- so
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83 |
+
- ku
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84 |
+
- ps
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+
- pa
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- rw
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- lo
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- ha
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- dv
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- fy
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- lb
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- ckb
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- mg
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- gd
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- am
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- ug
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- ht
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- grc
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- hmn
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- sd
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- jv
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- mi
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- tk
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- ceb
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- yi
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- ba
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- fo
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- or
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- xh
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- su
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- kl
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- ny
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- sm
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114 |
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- sn
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115 |
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- co
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116 |
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- zu
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117 |
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- ig
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118 |
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- yo
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119 |
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- pap
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120 |
+
- st
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121 |
+
- haw
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122 |
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- as
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123 |
+
- oc
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124 |
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- cv
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125 |
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- lus
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126 |
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- tet
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127 |
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- gsw
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128 |
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- sah
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129 |
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- br
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130 |
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- rm
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131 |
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- sa
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132 |
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- bo
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133 |
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- om
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134 |
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- se
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135 |
+
- ce
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136 |
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- cnh
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137 |
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- ilo
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138 |
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- hil
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139 |
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- udm
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140 |
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- os
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141 |
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- lg
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142 |
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- ti
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143 |
+
- vec
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144 |
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- ts
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145 |
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- tyv
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146 |
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- kbd
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147 |
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- ee
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148 |
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- iba
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149 |
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- av
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150 |
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- kha
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151 |
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- to
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152 |
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- tn
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153 |
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- nso
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154 |
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- fj
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155 |
+
- zza
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156 |
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- ak
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157 |
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- ada
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158 |
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- otq
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159 |
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- dz
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160 |
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- bua
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161 |
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- cfm
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162 |
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- ln
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163 |
+
- chm
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164 |
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- gn
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165 |
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- krc
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166 |
+
- wa
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167 |
+
- hif
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168 |
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- yua
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169 |
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- srn
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170 |
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- war
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171 |
+
- rom
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172 |
+
- bik
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173 |
+
- pam
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174 |
+
- sg
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175 |
+
- lu
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176 |
+
- ady
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177 |
+
- kbp
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178 |
+
- syr
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179 |
+
- ltg
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180 |
+
- myv
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181 |
+
- iso
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182 |
+
- kac
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183 |
+
- bho
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184 |
+
- ay
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185 |
+
- kum
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186 |
+
- qu
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187 |
+
- za
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188 |
+
- pag
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189 |
+
- ngu
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190 |
+
- ve
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191 |
+
- pck
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192 |
+
- zap
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193 |
+
- tyz
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194 |
+
- hui
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195 |
+
- bbc
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196 |
+
- tzo
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197 |
+
- tiv
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198 |
+
- ksd
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199 |
+
- gom
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200 |
+
- min
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201 |
+
- ang
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202 |
+
- nhe
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203 |
+
- bgp
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204 |
+
- nzi
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205 |
+
- nnb
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206 |
+
- nv
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207 |
+
- zxx
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208 |
+
- bci
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209 |
+
- kv
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210 |
+
- new
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211 |
+
- mps
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212 |
+
- alt
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213 |
+
- meu
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214 |
+
- bew
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215 |
+
- fon
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216 |
+
- iu
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217 |
+
- abt
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218 |
+
- mgh
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219 |
+
- mnw
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220 |
+
- tvl
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221 |
+
- dov
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222 |
+
- tlh
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223 |
+
- ho
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224 |
+
- kw
|
225 |
+
- mrj
|
226 |
+
- meo
|
227 |
+
- crh
|
228 |
+
- mbt
|
229 |
+
- emp
|
230 |
+
- ace
|
231 |
+
- ium
|
232 |
+
- mam
|
233 |
+
- gym
|
234 |
+
- mai
|
235 |
+
- crs
|
236 |
+
- pon
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237 |
+
- ubu
|
238 |
+
- fip
|
239 |
+
- quc
|
240 |
+
- gv
|
241 |
+
- kj
|
242 |
+
- btx
|
243 |
+
- ape
|
244 |
+
- chk
|
245 |
+
- rcf
|
246 |
+
- shn
|
247 |
+
- tzh
|
248 |
+
- mdf
|
249 |
+
- ppk
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250 |
+
- ss
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251 |
+
- gag
|
252 |
+
- cab
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253 |
+
- kri
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254 |
+
- seh
|
255 |
+
- ibb
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256 |
+
- tbz
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257 |
+
- bru
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258 |
+
- enq
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259 |
+
- ach
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260 |
+
- cuk
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261 |
+
- kmb
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262 |
+
- wo
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263 |
+
- kek
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264 |
+
- qub
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265 |
+
- tab
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266 |
+
- bts
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267 |
+
- kos
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268 |
+
- rwo
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269 |
+
- cak
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270 |
+
- tuc
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271 |
+
- bum
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272 |
+
- cjk
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273 |
+
- gil
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274 |
+
- stq
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275 |
+
- tsg
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276 |
+
- quh
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277 |
+
- mak
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278 |
+
- arn
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279 |
+
- ban
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280 |
+
- jiv
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281 |
+
- sja
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282 |
+
- yap
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283 |
+
- tcy
|
284 |
+
- toj
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285 |
+
- twu
|
286 |
+
- xal
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287 |
+
- amu
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288 |
+
- rmc
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289 |
+
- hus
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290 |
+
- nia
|
291 |
+
- kjh
|
292 |
+
- bm
|
293 |
+
- guh
|
294 |
+
- mas
|
295 |
+
- acf
|
296 |
+
- dtp
|
297 |
+
- ksw
|
298 |
+
- bzj
|
299 |
+
- din
|
300 |
+
- zne
|
301 |
+
- mad
|
302 |
+
- msi
|
303 |
+
- mag
|
304 |
+
- mkn
|
305 |
+
- kg
|
306 |
+
- lhu
|
307 |
+
- ch
|
308 |
+
- qvi
|
309 |
+
- mh
|
310 |
+
- djk
|
311 |
+
- sus
|
312 |
+
- mfe
|
313 |
+
- srm
|
314 |
+
- dyu
|
315 |
+
- ctu
|
316 |
+
- gui
|
317 |
+
- pau
|
318 |
+
- inb
|
319 |
+
- bi
|
320 |
+
- mni
|
321 |
+
- guc
|
322 |
+
- jam
|
323 |
+
- wal
|
324 |
+
- jac
|
325 |
+
- bas
|
326 |
+
- gor
|
327 |
+
- skr
|
328 |
+
- nyu
|
329 |
+
- noa
|
330 |
+
- sda
|
331 |
+
- gub
|
332 |
+
- nog
|
333 |
+
- cni
|
334 |
+
- teo
|
335 |
+
- tdx
|
336 |
+
- sxn
|
337 |
+
- rki
|
338 |
+
- nr
|
339 |
+
- frp
|
340 |
+
- alz
|
341 |
+
- taj
|
342 |
+
- lrc
|
343 |
+
- cce
|
344 |
+
- rn
|
345 |
+
- jvn
|
346 |
+
- hvn
|
347 |
+
- nij
|
348 |
+
- dwr
|
349 |
+
- izz
|
350 |
+
- msm
|
351 |
+
- bus
|
352 |
+
- ktu
|
353 |
+
- chr
|
354 |
+
- maz
|
355 |
+
- tzj
|
356 |
+
- suz
|
357 |
+
- knj
|
358 |
+
- bim
|
359 |
+
- gvl
|
360 |
+
- bqc
|
361 |
+
- tca
|
362 |
+
- pis
|
363 |
+
- prk
|
364 |
+
- laj
|
365 |
+
- mel
|
366 |
+
- qxr
|
367 |
+
- niq
|
368 |
+
- ahk
|
369 |
+
- shp
|
370 |
+
- hne
|
371 |
+
- spp
|
372 |
+
- koi
|
373 |
+
- krj
|
374 |
+
- quf
|
375 |
+
- luz
|
376 |
+
- agr
|
377 |
+
- tsc
|
378 |
+
- mqy
|
379 |
+
- gof
|
380 |
+
- gbm
|
381 |
+
- miq
|
382 |
+
- dje
|
383 |
+
- awa
|
384 |
+
- bjj
|
385 |
+
- qvz
|
386 |
+
- sjp
|
387 |
+
- tll
|
388 |
+
- raj
|
389 |
+
- kjg
|
390 |
+
- bgz
|
391 |
+
- quy
|
392 |
+
- cbk
|
393 |
+
- akb
|
394 |
+
- oj
|
395 |
+
- ify
|
396 |
+
- mey
|
397 |
+
- ks
|
398 |
+
- cac
|
399 |
+
- brx
|
400 |
+
- qup
|
401 |
+
- syl
|
402 |
+
- jax
|
403 |
+
- ff
|
404 |
+
- ber
|
405 |
+
- tks
|
406 |
+
- trp
|
407 |
+
- mrw
|
408 |
+
- adh
|
409 |
+
- smt
|
410 |
+
- srr
|
411 |
+
- ffm
|
412 |
+
- qvc
|
413 |
+
- mtr
|
414 |
+
- ann
|
415 |
+
- kaa
|
416 |
+
- aa
|
417 |
+
- noe
|
418 |
+
- nut
|
419 |
+
- gyn
|
420 |
+
- kwi
|
421 |
+
- xmm
|
422 |
+
- msb
|
423 |
+
library_name: ctranslate2
|
424 |
+
tags:
|
425 |
+
- text2text-generation
|
426 |
+
- text-generation-inference
|
427 |
+
datasets:
|
428 |
+
- allenai/MADLAD-400
|
429 |
+
pipeline_tag: translation
|
430 |
+
|
431 |
+
widget:
|
432 |
+
- text: "<2en> Como vai, amigo?"
|
433 |
+
example_title: "Translation to English"
|
434 |
+
- text: "<2de> Do you speak German?"
|
435 |
+
example_title: "Translation to German"
|
436 |
+
|
437 |
+
---
|
438 |
+
|
439 |
+
# MADLAD-400-3B-MT (int8_float32 quantized using CTranslate2)
|
440 |
+
|
441 |
+
```
|
442 |
+
ct2-transformers-converter --model ./madlad400-3b-mt --quantization int8_float32 --output_dir madlad400-3b-mt-ct2-int8_float32 --copy_files added_tokens.json generation_config.json special_tokens_map.json spiece.model tokenizer.json tokenizer_config.json
|
443 |
+
```
|
444 |
+
|
445 |
+
---
|
446 |
+
|
447 |
+
Original model card below
|
448 |
+
|
449 |
+
---
|
450 |
+
|
451 |
+
|
452 |
+
# Model Card for MADLAD-400-3B-MT
|
453 |
+
|
454 |
+
# Table of Contents
|
455 |
+
|
456 |
+
0. [TL;DR](#TL;DR)
|
457 |
+
1. [Model Details](#model-details)
|
458 |
+
2. [Usage](#usage)
|
459 |
+
3. [Uses](#uses)
|
460 |
+
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
|
461 |
+
5. [Training Details](#training-details)
|
462 |
+
6. [Evaluation](#evaluation)
|
463 |
+
7. [Environmental Impact](#environmental-impact)
|
464 |
+
8. [Citation](#citation)
|
465 |
+
|
466 |
+
|
467 |
+
# TL;DR
|
468 |
+
|
469 |
+
MADLAD-400-3B-MT is a multilingual machine translation model based on the T5 architecture that was
|
470 |
+
trained on 1 trillion tokens covering over 450 languages using publicly available data.
|
471 |
+
It is competitive with models that are significantly larger.
|
472 |
+
|
473 |
+
**Disclaimer**: [Juarez Bochi](https://huggingface.co/jbochi), who was not involved in this research, converted
|
474 |
+
the original weights and wrote the contents of this model card based on the original paper and Flan-T5.
|
475 |
+
|
476 |
+
# Model Details
|
477 |
+
|
478 |
+
## Model Description
|
479 |
+
|
480 |
+
- **Model type:** Language model
|
481 |
+
- **Language(s) (NLP):** Multilingual (400+ languages)
|
482 |
+
- **License:** Apache 2.0
|
483 |
+
- **Related Models:** [All MADLAD-400 Checkpoints](https://huggingface.co/models?search=madlad)
|
484 |
+
- **Original Checkpoints:** [All Original MADLAD-400 Checkpoints](https://github.com/google-research/google-research/tree/master/madlad_400)
|
485 |
+
- **Resources for more information:**
|
486 |
+
- [Research paper](https://arxiv.org/abs/2309.04662)
|
487 |
+
- [GitHub Repo](https://github.com/google-research/t5x)
|
488 |
+
- [Hugging Face MADLAD-400 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/MADLAD-400) - [Pending PR](https://github.com/huggingface/transformers/pull/27471)
|
489 |
+
|
490 |
+
# Usage
|
491 |
+
|
492 |
+
Find below some example scripts on how to use the model:
|
493 |
+
|
494 |
+
## Using the Pytorch model with `transformers`
|
495 |
+
|
496 |
+
### Running the model on a CPU or GPU
|
497 |
+
|
498 |
+
<details>
|
499 |
+
<summary> Click to expand </summary>
|
500 |
+
|
501 |
+
First, install the Python packages that are required:
|
502 |
+
|
503 |
+
`pip install transformers accelerate sentencepiece`
|
504 |
+
|
505 |
+
```python
|
506 |
+
from transformers import T5ForConditionalGeneration, T5Tokenizer
|
507 |
+
|
508 |
+
model_name = 'jbochi/madlad400-3b-mt'
|
509 |
+
model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto")
|
510 |
+
tokenizer = T5Tokenizer.from_pretrained(model_name)
|
511 |
+
|
512 |
+
text = "<2pt> I love pizza!"
|
513 |
+
input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device)
|
514 |
+
outputs = model.generate(input_ids=input_ids)
|
515 |
+
|
516 |
+
tokenizer.decode(outputs[0], skip_special_tokens=True)
|
517 |
+
# Eu adoro pizza!
|
518 |
+
```
|
519 |
+
|
520 |
+
</details>
|
521 |
+
|
522 |
+
## Running the model with Candle
|
523 |
+
|
524 |
+
<details>
|
525 |
+
<summary> Click to expand </summary>
|
526 |
+
|
527 |
+
Usage with [candle](https://github.com/huggingface/candle):
|
528 |
+
|
529 |
+
```bash
|
530 |
+
$ cargo run --example t5 --release -- \
|
531 |
+
--model-id "jbochi/madlad400-3b-mt" \
|
532 |
+
--prompt "<2de> How are you, my friend?" \
|
533 |
+
--decode --temperature 0
|
534 |
+
```
|
535 |
+
|
536 |
+
We also provide a quantized model (1.65 GB vs the original 11.8 GB file):
|
537 |
+
|
538 |
+
```
|
539 |
+
cargo run --example quantized-t5 --release -- \
|
540 |
+
--model-id "jbochi/madlad400-3b-mt" --weight-file "model-q4k.gguf" \
|
541 |
+
--prompt "<2de> How are you, my friend?" \
|
542 |
+
--temperature 0
|
543 |
+
...
|
544 |
+
Wie geht es dir, mein Freund?
|
545 |
+
```
|
546 |
+
|
547 |
+
</details>
|
548 |
+
|
549 |
+
|
550 |
+
# Uses
|
551 |
+
|
552 |
+
## Direct Use and Downstream Use
|
553 |
+
|
554 |
+
> Primary intended uses: Machine Translation and multilingual NLP tasks on over 400 languages.
|
555 |
+
> Primary intended users: Research community.
|
556 |
+
|
557 |
+
## Out-of-Scope Use
|
558 |
+
|
559 |
+
> These models are trained on general domain data and are therefore not meant to
|
560 |
+
> work on domain-specific models out-of-the box. Moreover, these research models have not been assessed
|
561 |
+
> for production usecases.
|
562 |
+
|
563 |
+
# Bias, Risks, and Limitations
|
564 |
+
|
565 |
+
> We note that we evaluate on only 204 of the languages supported by these models and on machine translation
|
566 |
+
> and few-shot machine translation tasks. Users must consider use of this model carefully for their own
|
567 |
+
> usecase.
|
568 |
+
|
569 |
+
## Ethical considerations and risks
|
570 |
+
|
571 |
+
> We trained these models with MADLAD-400 and publicly available data to create baseline models that
|
572 |
+
> support NLP for over 400 languages, with a focus on languages underrepresented in large-scale corpora.
|
573 |
+
> Given that these models were trained with web-crawled datasets that may contain sensitive, offensive or
|
574 |
+
> otherwise low-quality content despite extensive preprocessing, it is still possible that these issues to the
|
575 |
+
> underlying training data may cause differences in model performance and toxic (or otherwise problematic)
|
576 |
+
> output for certain domains. Moreover, large models are dual use technologies that have specific risks
|
577 |
+
> associated with their use and development. We point the reader to surveys such as those written by
|
578 |
+
> Weidinger et al. or Bommasani et al. for a more detailed discussion of these risks, and to Liebling
|
579 |
+
> et al. for a thorough discussion of the risks of machine translation systems.
|
580 |
+
|
581 |
+
## Known Limitations
|
582 |
+
|
583 |
+
More information needed
|
584 |
+
|
585 |
+
## Sensitive Use:
|
586 |
+
|
587 |
+
More information needed
|
588 |
+
|
589 |
+
# Training Details
|
590 |
+
|
591 |
+
> We train models of various sizes: a 3B, 32-layer parameter model,
|
592 |
+
> a 7.2B 48-layer parameter model and a 10.7B 32-layer parameter model.
|
593 |
+
> We share all parameters of the model across language pairs,
|
594 |
+
> and use a Sentence Piece Model with 256k tokens shared on both the encoder and decoder
|
595 |
+
> side. Each input sentence has a <2xx> token prepended to the source sentence to indicate the target
|
596 |
+
> language.
|
597 |
+
|
598 |
+
See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details.
|
599 |
+
|
600 |
+
## Training Data
|
601 |
+
|
602 |
+
> For both the machine translation and language model, MADLAD-400 is used. For the machine translation
|
603 |
+
> model, a combination of parallel datasources covering 157 languages is also used. Further details are
|
604 |
+
> described in the [paper](https://arxiv.org/pdf/2309.04662.pdf).
|
605 |
+
|
606 |
+
## Training Procedure
|
607 |
+
|
608 |
+
See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details.
|
609 |
+
|
610 |
+
# Evaluation
|
611 |
+
|
612 |
+
## Testing Data, Factors & Metrics
|
613 |
+
|
614 |
+
> For evaluation, we used WMT, NTREX, Flores-200 and Gatones datasets as described in Section 4.3 in the [paper](https://arxiv.org/pdf/2309.04662.pdf).
|
615 |
+
|
616 |
+
> The translation quality of this model varies based on language, as seen in the paper, and likely varies on
|
617 |
+
> domain, though we have not assessed this.
|
618 |
+
|
619 |
+
## Results
|
620 |
+
|
621 |
+
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b7f632037d6452a321fa15/EzsMD1AwCuFH0S0DeD-n8.png)
|
622 |
+
|
623 |
+
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b7f632037d6452a321fa15/CJ5zCUVy7vTU76Lc8NZcK.png)
|
624 |
+
|
625 |
+
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b7f632037d6452a321fa15/NK0S-yVeWuhKoidpLYh3m.png)
|
626 |
+
|
627 |
+
See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details.
|
628 |
+
|
629 |
+
# Environmental Impact
|
630 |
+
|
631 |
+
More information needed
|
632 |
+
|
633 |
+
# Citation
|
634 |
+
|
635 |
+
**BibTeX:**
|
636 |
+
|
637 |
+
```bibtex
|
638 |
+
@misc{kudugunta2023madlad400,
|
639 |
+
title={MADLAD-400: A Multilingual And Document-Level Large Audited Dataset},
|
640 |
+
author={Sneha Kudugunta and Isaac Caswell and Biao Zhang and Xavier Garcia and Christopher A. Choquette-Choo and Katherine Lee and Derrick Xin and Aditya Kusupati and Romi Stella and Ankur Bapna and Orhan Firat},
|
641 |
+
year={2023},
|
642 |
+
eprint={2309.04662},
|
643 |
+
archivePrefix={arXiv},
|
644 |
+
primaryClass={cs.CL}
|
645 |
+
}
|
646 |
+
```
|
647 |
+
|