Ahmed Elnaggar
commited on
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99b17de
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871cef0
Update prot-t5 from elnaggar
Browse files- README.md +36 -1653
- special_tokens_map.json +1 -1485
- spiece.model +2 -2
README.md
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README.md · Rostlab/prot_t5_xl_bfd at main
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Organizations
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Community
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Forum
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Blog
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href="https://github.com/huggingface"
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Model Hub doc
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Transformers doc
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"
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Tokenizers doc
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>
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Datasets doc
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Log In
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Sign Up
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Log In
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| 1051 |
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>
|
| 1052 |
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<span>
|
| 1053 |
-
protein language model
|
| 1054 |
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</span>
|
| 1055 |
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|
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</a>
|
| 1057 |
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</div>
|
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<div class="border-b border-gray-100">
|
| 1059 |
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<div class="flex items-center justify-between">
|
| 1060 |
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<div class="flex items-center h-12 -mb-px overflow-x-auto overflow-y-hidden">
|
| 1061 |
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<a
|
| 1062 |
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class="tab-alternate "
|
| 1063 |
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href="/Rostlab/prot_t5_xl_bfd"
|
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-
>
|
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<svg
|
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|
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xmlns:xlink="http://www.w3.org/1999/xlink"
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viewBox="0 0 24 24"
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class="uim-quaternary"
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d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z"
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fill="currentColor"
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></path>
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<path
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class="uim-tertiary"
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d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z"
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fill="currentColor"
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></path>
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d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z"
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fill="currentColor"
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| 1094 |
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></path>
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</svg>
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Model card
|
| 1097 |
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</a>
|
| 1098 |
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<a
|
| 1099 |
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class="tab-alternate active"
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| 1100 |
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href="/Rostlab/prot_t5_xl_bfd/tree/main"
|
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>
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<svg
|
| 1103 |
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alt=""
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class="mr-1.5 text-gray-400"
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xmlns="http://www.w3.org/2000/svg"
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xmlns:xlink="http://www.w3.org/1999/xlink"
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aria-hidden="true"
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focusable="false"
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role="img"
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width="1em"
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d="M21 19h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0-4h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0-8h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0 4h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2z"
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fill="currentColor"
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></path>
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<path
|
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class="uim-primary"
|
| 1123 |
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d="M9 19a1 1 0 0 1-1-1V6a1 1 0 0 1 2 0v12a1 1 0 0 1-1 1zm-6-4.333a1 1 0 0 1-.64-1.769L3.438 12l-1.078-.898a1 1 0 0 1 1.28-1.538l2 1.667a1 1 0 0 1 0 1.538l-2 1.667a.999.999 0 0 1-.64.231z"
|
| 1124 |
-
fill="currentColor"
|
| 1125 |
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></path>
|
| 1126 |
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</svg>
|
| 1127 |
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Files and versions
|
| 1128 |
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</a>
|
| 1129 |
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</div>
|
| 1130 |
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<div class="relative hidden space-x-1.5 md:flex">
|
| 1131 |
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<div
|
| 1132 |
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class="v2-dropdown relative "
|
| 1133 |
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>
|
| 1135 |
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<button
|
| 1136 |
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class="v2-dropdown-button cursor-pointer btn text-sm"
|
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slot="trigger"
|
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type="button"
|
| 1139 |
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>
|
| 1140 |
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<svg
|
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alt=""
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class="mr-1.5"
|
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xmlns="http://www.w3.org/2000/svg"
|
| 1144 |
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xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1145 |
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aria-hidden="true"
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focusable="false"
|
| 1147 |
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role="img"
|
| 1148 |
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width="1em"
|
| 1149 |
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height="1em"
|
| 1150 |
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preserveAspectRatio="xMidYMid meet"
|
| 1151 |
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viewBox="0 0 32 32"
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style="transform: rotate(360deg);"
|
| 1153 |
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>
|
| 1154 |
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<path
|
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d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z"
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fill="currentColor"
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></path>
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<path
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d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z"
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fill="currentColor"
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></path>
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<path
|
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d="M12.419 25.484L17.639 6l1.932.518L14.35 26z"
|
| 1164 |
-
fill="currentColor"
|
| 1165 |
-
></path>
|
| 1166 |
-
</svg>
|
| 1167 |
-
Use in transformers
|
| 1168 |
-
</button><div
|
| 1169 |
-
class="v2-dropdown-menu hidden absolute mt-1 min-w-full bg-white rounded-xl overflow-hidden shadow-lg z-10 border border-gray-100 right-0 top-full "
|
| 1170 |
-
>
|
| 1171 |
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<ul class="min-w-full w-auto">
|
| 1172 |
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<div class="v2-to-clipboard" style="width: 700px;">
|
| 1173 |
-
<div class="border-b px-4 py-2 bg-gradient-to-r from-gray-50 to-white border-gray-200 flex justify-between items-center h-11">
|
| 1174 |
-
<h3 class="font-semibold">
|
| 1175 |
-
How to use from the
|
| 1176 |
-
<a target="_blank" href="https://github.com/huggingface/transformers">🤗/transformers</a>
|
| 1177 |
-
library:
|
| 1178 |
-
</h3>
|
| 1179 |
-
<button
|
| 1180 |
-
class="v2-to-clipboard-button text-sm underline flex items-center cursor-pointer text-gray-500"
|
| 1181 |
-
>
|
| 1182 |
-
<svg
|
| 1183 |
-
alt=""
|
| 1184 |
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class="mr-1"
|
| 1185 |
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xmlns="http://www.w3.org/2000/svg"
|
| 1186 |
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xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1187 |
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aria-hidden="true"
|
| 1188 |
-
focusable="false"
|
| 1189 |
-
role="img"
|
| 1190 |
-
width="1em"
|
| 1191 |
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height="1em"
|
| 1192 |
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preserveAspectRatio="xMidYMid meet"
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viewBox="0 0 32 32"
|
| 1194 |
-
>
|
| 1195 |
-
<path
|
| 1196 |
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d="M25.7 9.3l-7-7A.908.908 0 0 0 18 2H8a2.006 2.006 0 0 0-2 2v24a2.006 2.006 0 0 0 2 2h16a2.006 2.006 0 0 0 2-2V10a.908.908 0 0 0-.3-.7zM18 4.4l5.6 5.6H18zM24 28H8V4h8v6a2.006 2.006 0 0 0 2 2h6z"
|
| 1197 |
-
fill="currentColor"
|
| 1198 |
-
></path>
|
| 1199 |
-
</svg>
|
| 1200 |
-
Copy to clipboard
|
| 1201 |
-
</button>
|
| 1202 |
-
</div>
|
| 1203 |
-
<pre class="v2-to-clipboard-content whitespace-pre-line text-sm p-4 text-gray-700">
|
| 1204 |
-
from transformers import AutoTokenizer, AutoModelWithLMHead
|
| 1205 |
-
|
| 1206 |
-
tokenizer = AutoTokenizer.from_pretrained("Rostlab/prot_t5_xl_bfd")
|
| 1207 |
-
|
| 1208 |
-
model = AutoModelWithLMHead.from_pretrained("Rostlab/prot_t5_xl_bfd")
|
| 1209 |
-
</pre>
|
| 1210 |
-
|
| 1211 |
-
<div class="border-b px-4 py-2 bg-gradient-to-r from-gray-50 to-white border-gray-200 flex justify-between items-center h-11">
|
| 1212 |
-
<h3 class="font-semibold">
|
| 1213 |
-
Or just clone the model repo
|
| 1214 |
-
</h3>
|
| 1215 |
-
</div>
|
| 1216 |
-
<pre class="whitespace-pre-line text-sm p-4 text-gray-700">
|
| 1217 |
-
git lfs install
|
| 1218 |
-
git clone https://huggingface.co/Rostlab/prot_t5_xl_bfd
|
| 1219 |
-
<div class="text-gray-500 text-xs">
|
| 1220 |
-
# if you want to clone without large files – just their pointers
|
| 1221 |
-
# prepend your git clone with the following env var:
|
| 1222 |
-
</div>
|
| 1223 |
-
GIT_LFS_SKIP_SMUDGE=1
|
| 1224 |
-
</pre>
|
| 1225 |
-
|
| 1226 |
-
</div>
|
| 1227 |
-
</ul>
|
| 1228 |
-
</div></div>
|
| 1229 |
-
</div>
|
| 1230 |
-
</div>
|
| 1231 |
-
</div>
|
| 1232 |
-
</div>
|
| 1233 |
-
</header>
|
| 1234 |
-
<div
|
| 1235 |
-
class="container relative space-y-4 flex flex-col md:grid md:space-y-0 overflow-hidden
|
| 1236 |
-
md:grid-cols-12
|
| 1237 |
-
|
| 1238 |
-
md:gap-6
|
| 1239 |
-
|
| 1240 |
-
"
|
| 1241 |
-
|
| 1242 |
-
|
| 1243 |
-
>
|
| 1244 |
-
<section class="pt-8 border-gray-100 col-span-full">
|
| 1245 |
-
|
| 1246 |
-
<header class="pb-4 flex items-center justify-between">
|
| 1247 |
-
<div class="flex items-center">
|
| 1248 |
-
<div
|
| 1249 |
-
class="v2-dropdown relative mr-6"
|
| 1250 |
-
|
| 1251 |
-
>
|
| 1252 |
-
<button
|
| 1253 |
-
class="v2-dropdown-button cursor-pointer btn"
|
| 1254 |
-
slot="trigger"
|
| 1255 |
-
type="button"
|
| 1256 |
-
>
|
| 1257 |
-
<svg
|
| 1258 |
-
alt=""
|
| 1259 |
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class="mr-1 text-gray-700 dark:text-gray-400"
|
| 1260 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1261 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1262 |
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aria-hidden="true"
|
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-
focusable="false"
|
| 1264 |
-
role="img"
|
| 1265 |
-
width="1em"
|
| 1266 |
-
height="1em"
|
| 1267 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1268 |
-
viewBox="0 0 24 24"
|
| 1269 |
-
style="transform: rotate(360deg);"
|
| 1270 |
-
>
|
| 1271 |
-
<path
|
| 1272 |
-
d="M13 14c-3.36 0-4.46 1.35-4.82 2.24C9.25 16.7 10 17.76 10 19a3 3 0 0 1-3 3a3 3 0 0 1-3-3c0-1.31.83-2.42 2-2.83V7.83A2.99 2.99 0 0 1 4 5a3 3 0 0 1 3-3a3 3 0 0 1 3 3c0 1.31-.83 2.42-2 2.83v5.29c.88-.65 2.16-1.12 4-1.12c2.67 0 3.56-1.34 3.85-2.23A3.006 3.006 0 0 1 14 7a3 3 0 0 1 3-3a3 3 0 0 1 3 3c0 1.34-.88 2.5-2.09 2.86C17.65 11.29 16.68 14 13 14m-6 4a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1M7 4a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1m10 2a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1z"
|
| 1273 |
-
fill="currentColor"
|
| 1274 |
-
></path>
|
| 1275 |
-
</svg>
|
| 1276 |
-
main
|
| 1277 |
-
<svg
|
| 1278 |
-
alt=""
|
| 1279 |
-
class="text-gray-500"
|
| 1280 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1281 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1282 |
-
aria-hidden="true"
|
| 1283 |
-
focusable="false"
|
| 1284 |
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role="img"
|
| 1285 |
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width="1em"
|
| 1286 |
-
height="1em"
|
| 1287 |
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preserveAspectRatio="xMidYMid meet"
|
| 1288 |
-
viewBox="0 0 24 24"
|
| 1289 |
-
style="transform: rotate(360deg);"
|
| 1290 |
-
>
|
| 1291 |
-
<path d="M7 10l5 5l5-5z" fill="currentColor"></path>
|
| 1292 |
-
</svg>
|
| 1293 |
-
</button><div
|
| 1294 |
-
class="v2-dropdown-menu hidden absolute mt-1 min-w-full bg-white rounded-xl overflow-hidden shadow-lg z-10 border border-gray-100 right-0 top-full "
|
| 1295 |
-
>
|
| 1296 |
-
<ul class="min-w-full w-auto">
|
| 1297 |
-
<li>
|
| 1298 |
-
<ul>
|
| 1299 |
-
<li>
|
| 1300 |
-
<div
|
| 1301 |
-
class="v2-dropdown-entry flex items-center hover:bg-gray-50 cursor-pointer truncate
|
| 1302 |
-
hover:underline
|
| 1303 |
-
|
| 1304 |
-
|
| 1305 |
-
"
|
| 1306 |
-
|
| 1307 |
-
|
| 1308 |
-
|
| 1309 |
-
|
| 1310 |
-
>
|
| 1311 |
-
<a
|
| 1312 |
-
class="px-3 py-1.5"
|
| 1313 |
-
href="/Rostlab/prot_t5_xl_bfd/blob/main/README.md"
|
| 1314 |
-
>
|
| 1315 |
-
main
|
| 1316 |
-
</a>
|
| 1317 |
-
</div>
|
| 1318 |
-
</li>
|
| 1319 |
-
</ul>
|
| 1320 |
-
</li>
|
| 1321 |
-
<li>
|
| 1322 |
-
<ul class="bg-yellow-50">
|
| 1323 |
-
</ul>
|
| 1324 |
-
</li>
|
| 1325 |
-
</ul>
|
| 1326 |
-
</div></div>
|
| 1327 |
-
|
| 1328 |
-
<a class="hover:underline text-gray-800 dark:text-gray-300" href="/Rostlab/prot_t5_xl_bfd/tree/main">
|
| 1329 |
-
Rostlab/prot_t5_xl_bfd
|
| 1330 |
-
</a>
|
| 1331 |
-
<span class="text-gray-300 mx-1 font-light">/</span>
|
| 1332 |
-
|
| 1333 |
-
<span class="font-light dark:text-gray-300">README.md</span>
|
| 1334 |
-
|
| 1335 |
-
</div>
|
| 1336 |
-
|
| 1337 |
-
<div class="flex justify-end">
|
| 1338 |
-
</div>
|
| 1339 |
-
</header>
|
| 1340 |
-
|
| 1341 |
-
<div class="border border-b-0 dark:border-gray-800 px-3 py-2 flex items-baseline rounded-t-lg bg-gradient-to-b from-white to-gray-100 dark:from-gray-800 dark:to-gray-900">
|
| 1342 |
-
<img
|
| 1343 |
-
class="w-4 h-4 rounded-full mr-2.5 self-center"
|
| 1344 |
-
alt="julien-c's picture"
|
| 1345 |
-
src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1583856843119-5dd96eb166059660ed1ee413.jpeg?w=200&h=200&f=face"
|
| 1346 |
-
/>
|
| 1347 |
-
<div class="mr-5 truncate flex-none">
|
| 1348 |
-
<a class="hover:underline" href="/julien-c">
|
| 1349 |
-
julien-c
|
| 1350 |
-
</a>
|
| 1351 |
-
</div>
|
| 1352 |
-
<a
|
| 1353 |
-
class="mr-4 font-mono text-sm text-gray-500 dark:text-gray-300 truncate hover:underline"
|
| 1354 |
-
href="/Rostlab/prot_t5_xl_bfd/commit/7ae1d5c1d148d6c65c7e294cc72807e5b454fdb7"
|
| 1355 |
-
>
|
| 1356 |
-
Migrate model card from transformers-repo
|
| 1357 |
-
</a>
|
| 1358 |
-
<a
|
| 1359 |
-
class="text-sm border dark:border-gray-800 px-1.5 rounded bg-gray-50 dark:bg-gray-900 hover:underline"
|
| 1360 |
-
href="/Rostlab/prot_t5_xl_bfd/commit/7ae1d5c1d148d6c65c7e294cc72807e5b454fdb7"
|
| 1361 |
-
>
|
| 1362 |
-
7ae1d5c
|
| 1363 |
-
</a>
|
| 1364 |
-
<time
|
| 1365 |
-
datetime="2020-12-11T21:30:13"
|
| 1366 |
-
class="ml-auto hidden lg:block text-gray-500 dark:text-gray-400 truncate flex-none pl-2"
|
| 1367 |
-
>
|
| 1368 |
-
2 months ago
|
| 1369 |
-
</time>
|
| 1370 |
-
</div>
|
| 1371 |
-
|
| 1372 |
-
<div class="flex items-center p-3 border dark:border-gray-800 text-sm text-gray-700 dark:bg-gray-900">
|
| 1373 |
-
<a class="flex items-center hover:underline mr-4" href="/Rostlab/prot_t5_xl_bfd/raw/main/README.md">
|
| 1374 |
-
<svg
|
| 1375 |
-
alt=""
|
| 1376 |
-
class="mr-1.5 "
|
| 1377 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1378 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1379 |
-
aria-hidden="true"
|
| 1380 |
-
focusable="false"
|
| 1381 |
-
role="img"
|
| 1382 |
-
width="1em"
|
| 1383 |
-
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<pre class="p-3 text-sm overflow-x-auto">---
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language: protein
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tags:
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-
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datasets:
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Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
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[
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[
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ProtT5-XL-BFD is based on the
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This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of
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publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
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|
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@@ -1619,33 +27,33 @@ It has been shown that the features extracted from this self-supervised model (L
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shape.
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This implied learning some of the grammar of the language of life realized in protein sequences.
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-
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The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
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We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
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We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder.
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-
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Here is how to use this model to extract the features of a given protein sequence in PyTorch:
|
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|
| 1632 |
-
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from transformers import T5Tokenizer, T5Model
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import re
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import torch
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tokenizer = T5Tokenizer.from_pretrained(
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model = T5Model.from_pretrained(
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sequences_Example = [
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sequences_Example = [re.sub(r
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ids = tokenizer.batch_encode_plus(sequences_Example, add_special_tokens=True, padding=True)
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input_ids = torch.tensor(ids[
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attention_mask = torch.tensor(ids[
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with torch.no_grad():
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embedding = model(input_ids=input_ids,attention_mask=attention_mask,decoder_input_ids=None)
|
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@@ -1653,38 +61,38 @@ with torch.no_grad():
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# For feature extraction we recommend to use the encoder embedding
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encoder_embedding = embedding[2].cpu().numpy()
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decoder_embedding = embedding[0].cpu().numpy()
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-
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The ProtT5-XL-BFD model was pretrained on [
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The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids
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The inputs of the model are then of the form:
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Protein Sequence [EOS]
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The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens.
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The details of the masking procedure for each sequence are as follows:
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The model was trained on a single TPU Pod V3-1024 for 1.2 million steps in total, using sequence length 512 (batch size 4k).
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It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture.
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The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
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When the model is used for feature etraction, this model achieves the following results:
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|
|
@@ -1697,9 +105,9 @@ Test results :
|
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| CB513 | 84 | 71 | | |
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| DeepLoc | | | 77 | 91 |
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-
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@article {Elnaggar2020.07.12.199554,
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author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
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title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
|
|
@@ -1707,36 +115,11 @@ Test results :
|
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| 1707 |
year = {2020},
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doi = {10.1101/2020.07.12.199554},
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publisher = {Cold Spring Harbor Laboratory},
|
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-
abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \&
|
| 1711 |
URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
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eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
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journal = {bioRxiv}
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}
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|
| 2 |
language: protein
|
| 3 |
tags:
|
| 4 |
+
- protein language model
|
| 5 |
datasets:
|
| 6 |
+
- BFD
|
| 7 |
+
---
|
| 8 |
|
| 9 |
+
# ProtT5-XL-BFD model
|
| 10 |
|
| 11 |
Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
|
| 12 |
+
[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
|
| 13 |
+
[this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
|
| 14 |
|
| 15 |
|
| 16 |
+
## Model description
|
| 17 |
|
| 18 |
+
ProtT5-XL-BFD is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion.
|
| 19 |
This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of
|
| 20 |
publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
|
| 21 |
|
|
|
|
| 27 |
shape.
|
| 28 |
This implied learning some of the grammar of the language of life realized in protein sequences.
|
| 29 |
|
| 30 |
+
## Intended uses & limitations
|
| 31 |
|
| 32 |
The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
|
| 33 |
We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
|
| 34 |
We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder.
|
| 35 |
|
| 36 |
+
### How to use
|
| 37 |
|
| 38 |
Here is how to use this model to extract the features of a given protein sequence in PyTorch:
|
| 39 |
|
| 40 |
+
```python
|
| 41 |
from transformers import T5Tokenizer, T5Model
|
| 42 |
import re
|
| 43 |
import torch
|
| 44 |
|
| 45 |
+
tokenizer = T5Tokenizer.from_pretrained('Rostlab/prot_t5_xl_bfd', do_lower_case=False)
|
| 46 |
|
| 47 |
+
model = T5Model.from_pretrained("Rostlab/prot_t5_xl_bfd")
|
| 48 |
|
| 49 |
+
sequences_Example = ["A E T C Z A O","S K T Z P"]
|
| 50 |
|
| 51 |
+
sequences_Example = [re.sub(r"[UZOB]", "X", sequence) for sequence in sequences_Example]
|
| 52 |
|
| 53 |
ids = tokenizer.batch_encode_plus(sequences_Example, add_special_tokens=True, padding=True)
|
| 54 |
|
| 55 |
+
input_ids = torch.tensor(ids['input_ids'])
|
| 56 |
+
attention_mask = torch.tensor(ids['attention_mask'])
|
| 57 |
|
| 58 |
with torch.no_grad():
|
| 59 |
embedding = model(input_ids=input_ids,attention_mask=attention_mask,decoder_input_ids=None)
|
|
|
|
| 61 |
# For feature extraction we recommend to use the encoder embedding
|
| 62 |
encoder_embedding = embedding[2].cpu().numpy()
|
| 63 |
decoder_embedding = embedding[0].cpu().numpy()
|
| 64 |
+
```
|
| 65 |
|
| 66 |
+
## Training data
|
| 67 |
|
| 68 |
+
The ProtT5-XL-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences.
|
| 69 |
|
| 70 |
+
## Training procedure
|
| 71 |
|
| 72 |
+
### Preprocessing
|
| 73 |
|
| 74 |
+
The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X".
|
| 75 |
The inputs of the model are then of the form:
|
| 76 |
|
| 77 |
+
```
|
| 78 |
Protein Sequence [EOS]
|
| 79 |
+
```
|
| 80 |
|
| 81 |
The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens.
|
| 82 |
|
| 83 |
The details of the masking procedure for each sequence are as follows:
|
| 84 |
+
- 15% of the amino acids are masked.
|
| 85 |
+
- In 90% of the cases, the masked amino acids are replaced by `[MASK]` token.
|
| 86 |
+
- In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
|
| 87 |
|
| 88 |
+
### Pretraining
|
| 89 |
|
| 90 |
The model was trained on a single TPU Pod V3-1024 for 1.2 million steps in total, using sequence length 512 (batch size 4k).
|
| 91 |
It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture.
|
| 92 |
The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
|
| 93 |
|
| 94 |
|
| 95 |
+
## Evaluation results
|
| 96 |
|
| 97 |
When the model is used for feature etraction, this model achieves the following results:
|
| 98 |
|
|
|
|
| 105 |
| CB513 | 84 | 71 | | |
|
| 106 |
| DeepLoc | | | 77 | 91 |
|
| 107 |
|
| 108 |
+
### BibTeX entry and citation info
|
| 109 |
|
| 110 |
+
```bibtex
|
| 111 |
@article {Elnaggar2020.07.12.199554,
|
| 112 |
author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
|
| 113 |
title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
|
|
|
|
| 115 |
year = {2020},
|
| 116 |
doi = {10.1101/2020.07.12.199554},
|
| 117 |
publisher = {Cold Spring Harbor Laboratory},
|
| 118 |
+
abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
|
| 119 |
URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
|
| 120 |
eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
|
| 121 |
journal = {bioRxiv}
|
| 122 |
}
|
| 123 |
+
```
|
| 124 |
|
| 125 |
+
> Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)
|
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|
|
|
|
special_tokens_map.json
CHANGED
|
@@ -1,1485 +1 @@
|
|
| 1 |
-
|
| 2 |
-
<html>
|
| 3 |
-
<head>
|
| 4 |
-
<meta charset="utf-8">
|
| 5 |
-
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
|
| 6 |
-
<meta name="description" content="We’re on a journey to solve and democratize artificial intelligence through natural language.">
|
| 7 |
-
<meta property="fb:app_id" content="1321688464574422">
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</svg>
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Organizations
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Community
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"
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href="https://discuss.huggingface.co/"
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target="_blank"
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Forum
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Blog
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| 442 |
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href="https://github.com/huggingface"
|
| 443 |
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Documentation
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"
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Model Hub doc
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hover:underline
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| 480 |
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Inference API doc
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Transformers doc
|
| 499 |
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"
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| 510 |
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href="/docs/tokenizers/python/latest/"
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| 511 |
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| 512 |
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>
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| 513 |
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Tokenizers doc
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</a>
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"
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href="/docs/datasets/"
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| 527 |
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>
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| 528 |
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Datasets doc
|
| 529 |
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</a>
|
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</ul>
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</li>
|
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</div></div>
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Log In
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|
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|
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href="/join"
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Sign Up
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Account
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Log In
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Website
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Models
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| 666 |
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</a>
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|
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</li>
|
| 704 |
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<li>
|
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<a href="/languages" class="btn w-full v2-dropdown-entry">
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<li>
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href="/Rostlab/prot_t5_xl_bfd"
|
| 1064 |
-
>
|
| 1065 |
-
<svg
|
| 1066 |
-
alt=""
|
| 1067 |
-
class="mr-1.5 text-gray-400"
|
| 1068 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1069 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1070 |
-
aria-hidden="true"
|
| 1071 |
-
focusable="false"
|
| 1072 |
-
role="img"
|
| 1073 |
-
width="1em"
|
| 1074 |
-
height="1em"
|
| 1075 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1076 |
-
viewBox="0 0 24 24"
|
| 1077 |
-
>
|
| 1078 |
-
<path
|
| 1079 |
-
class="uim-quaternary"
|
| 1080 |
-
d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z"
|
| 1081 |
-
opacity=".25"
|
| 1082 |
-
fill="currentColor"
|
| 1083 |
-
></path>
|
| 1084 |
-
<path
|
| 1085 |
-
class="uim-tertiary"
|
| 1086 |
-
d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z"
|
| 1087 |
-
opacity=".5"
|
| 1088 |
-
fill="currentColor"
|
| 1089 |
-
></path>
|
| 1090 |
-
<path
|
| 1091 |
-
class="uim-primary"
|
| 1092 |
-
d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z"
|
| 1093 |
-
fill="currentColor"
|
| 1094 |
-
></path>
|
| 1095 |
-
</svg>
|
| 1096 |
-
Model card
|
| 1097 |
-
</a>
|
| 1098 |
-
<a
|
| 1099 |
-
class="tab-alternate active"
|
| 1100 |
-
href="/Rostlab/prot_t5_xl_bfd/tree/main"
|
| 1101 |
-
>
|
| 1102 |
-
<svg
|
| 1103 |
-
alt=""
|
| 1104 |
-
class="mr-1.5 text-gray-400"
|
| 1105 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1106 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1107 |
-
aria-hidden="true"
|
| 1108 |
-
focusable="false"
|
| 1109 |
-
role="img"
|
| 1110 |
-
width="1em"
|
| 1111 |
-
height="1em"
|
| 1112 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1113 |
-
viewBox="0 0 24 24"
|
| 1114 |
-
>
|
| 1115 |
-
<path
|
| 1116 |
-
class="uim-tertiary"
|
| 1117 |
-
d="M21 19h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0-4h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0-8h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0 4h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2z"
|
| 1118 |
-
opacity=".5"
|
| 1119 |
-
fill="currentColor"
|
| 1120 |
-
></path>
|
| 1121 |
-
<path
|
| 1122 |
-
class="uim-primary"
|
| 1123 |
-
d="M9 19a1 1 0 0 1-1-1V6a1 1 0 0 1 2 0v12a1 1 0 0 1-1 1zm-6-4.333a1 1 0 0 1-.64-1.769L3.438 12l-1.078-.898a1 1 0 0 1 1.28-1.538l2 1.667a1 1 0 0 1 0 1.538l-2 1.667a.999.999 0 0 1-.64.231z"
|
| 1124 |
-
fill="currentColor"
|
| 1125 |
-
></path>
|
| 1126 |
-
</svg>
|
| 1127 |
-
Files and versions
|
| 1128 |
-
</a>
|
| 1129 |
-
</div>
|
| 1130 |
-
<div class="relative hidden space-x-1.5 md:flex">
|
| 1131 |
-
<div
|
| 1132 |
-
class="v2-dropdown relative "
|
| 1133 |
-
|
| 1134 |
-
>
|
| 1135 |
-
<button
|
| 1136 |
-
class="v2-dropdown-button cursor-pointer btn text-sm"
|
| 1137 |
-
slot="trigger"
|
| 1138 |
-
type="button"
|
| 1139 |
-
>
|
| 1140 |
-
<svg
|
| 1141 |
-
alt=""
|
| 1142 |
-
class="mr-1.5"
|
| 1143 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1144 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1145 |
-
aria-hidden="true"
|
| 1146 |
-
focusable="false"
|
| 1147 |
-
role="img"
|
| 1148 |
-
width="1em"
|
| 1149 |
-
height="1em"
|
| 1150 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1151 |
-
viewBox="0 0 32 32"
|
| 1152 |
-
style="transform: rotate(360deg);"
|
| 1153 |
-
>
|
| 1154 |
-
<path
|
| 1155 |
-
d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z"
|
| 1156 |
-
fill="currentColor"
|
| 1157 |
-
></path>
|
| 1158 |
-
<path
|
| 1159 |
-
d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z"
|
| 1160 |
-
fill="currentColor"
|
| 1161 |
-
></path>
|
| 1162 |
-
<path
|
| 1163 |
-
d="M12.419 25.484L17.639 6l1.932.518L14.35 26z"
|
| 1164 |
-
fill="currentColor"
|
| 1165 |
-
></path>
|
| 1166 |
-
</svg>
|
| 1167 |
-
Use in transformers
|
| 1168 |
-
</button><div
|
| 1169 |
-
class="v2-dropdown-menu hidden absolute mt-1 min-w-full bg-white rounded-xl overflow-hidden shadow-lg z-10 border border-gray-100 right-0 top-full "
|
| 1170 |
-
>
|
| 1171 |
-
<ul class="min-w-full w-auto">
|
| 1172 |
-
<div class="v2-to-clipboard" style="width: 700px;">
|
| 1173 |
-
<div class="border-b px-4 py-2 bg-gradient-to-r from-gray-50 to-white border-gray-200 flex justify-between items-center h-11">
|
| 1174 |
-
<h3 class="font-semibold">
|
| 1175 |
-
How to use from the
|
| 1176 |
-
<a target="_blank" href="https://github.com/huggingface/transformers">🤗/transformers</a>
|
| 1177 |
-
library:
|
| 1178 |
-
</h3>
|
| 1179 |
-
<button
|
| 1180 |
-
class="v2-to-clipboard-button text-sm underline flex items-center cursor-pointer text-gray-500"
|
| 1181 |
-
>
|
| 1182 |
-
<svg
|
| 1183 |
-
alt=""
|
| 1184 |
-
class="mr-1"
|
| 1185 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1186 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1187 |
-
aria-hidden="true"
|
| 1188 |
-
focusable="false"
|
| 1189 |
-
role="img"
|
| 1190 |
-
width="1em"
|
| 1191 |
-
height="1em"
|
| 1192 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1193 |
-
viewBox="0 0 32 32"
|
| 1194 |
-
>
|
| 1195 |
-
<path
|
| 1196 |
-
d="M25.7 9.3l-7-7A.908.908 0 0 0 18 2H8a2.006 2.006 0 0 0-2 2v24a2.006 2.006 0 0 0 2 2h16a2.006 2.006 0 0 0 2-2V10a.908.908 0 0 0-.3-.7zM18 4.4l5.6 5.6H18zM24 28H8V4h8v6a2.006 2.006 0 0 0 2 2h6z"
|
| 1197 |
-
fill="currentColor"
|
| 1198 |
-
></path>
|
| 1199 |
-
</svg>
|
| 1200 |
-
Copy to clipboard
|
| 1201 |
-
</button>
|
| 1202 |
-
</div>
|
| 1203 |
-
<pre class="v2-to-clipboard-content whitespace-pre-line text-sm p-4 text-gray-700">
|
| 1204 |
-
from transformers import AutoTokenizer, AutoModelWithLMHead
|
| 1205 |
-
|
| 1206 |
-
tokenizer = AutoTokenizer.from_pretrained("Rostlab/prot_t5_xl_bfd")
|
| 1207 |
-
|
| 1208 |
-
model = AutoModelWithLMHead.from_pretrained("Rostlab/prot_t5_xl_bfd")
|
| 1209 |
-
</pre>
|
| 1210 |
-
|
| 1211 |
-
<div class="border-b px-4 py-2 bg-gradient-to-r from-gray-50 to-white border-gray-200 flex justify-between items-center h-11">
|
| 1212 |
-
<h3 class="font-semibold">
|
| 1213 |
-
Or just clone the model repo
|
| 1214 |
-
</h3>
|
| 1215 |
-
</div>
|
| 1216 |
-
<pre class="whitespace-pre-line text-sm p-4 text-gray-700">
|
| 1217 |
-
git lfs install
|
| 1218 |
-
git clone https://huggingface.co/Rostlab/prot_t5_xl_bfd
|
| 1219 |
-
<div class="text-gray-500 text-xs">
|
| 1220 |
-
# if you want to clone without large files – just their pointers
|
| 1221 |
-
# prepend your git clone with the following env var:
|
| 1222 |
-
</div>
|
| 1223 |
-
GIT_LFS_SKIP_SMUDGE=1
|
| 1224 |
-
</pre>
|
| 1225 |
-
|
| 1226 |
-
</div>
|
| 1227 |
-
</ul>
|
| 1228 |
-
</div></div>
|
| 1229 |
-
</div>
|
| 1230 |
-
</div>
|
| 1231 |
-
</div>
|
| 1232 |
-
</div>
|
| 1233 |
-
</header>
|
| 1234 |
-
<div
|
| 1235 |
-
class="container relative space-y-4 flex flex-col md:grid md:space-y-0 overflow-hidden
|
| 1236 |
-
md:grid-cols-12
|
| 1237 |
-
|
| 1238 |
-
md:gap-6
|
| 1239 |
-
|
| 1240 |
-
"
|
| 1241 |
-
|
| 1242 |
-
|
| 1243 |
-
>
|
| 1244 |
-
<section class="pt-8 border-gray-100 col-span-full">
|
| 1245 |
-
|
| 1246 |
-
<header class="pb-4 flex items-center justify-between">
|
| 1247 |
-
<div class="flex items-center">
|
| 1248 |
-
<div
|
| 1249 |
-
class="v2-dropdown relative mr-6"
|
| 1250 |
-
|
| 1251 |
-
>
|
| 1252 |
-
<button
|
| 1253 |
-
class="v2-dropdown-button cursor-pointer btn"
|
| 1254 |
-
slot="trigger"
|
| 1255 |
-
type="button"
|
| 1256 |
-
>
|
| 1257 |
-
<svg
|
| 1258 |
-
alt=""
|
| 1259 |
-
class="mr-1 text-gray-700 dark:text-gray-400"
|
| 1260 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1261 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1262 |
-
aria-hidden="true"
|
| 1263 |
-
focusable="false"
|
| 1264 |
-
role="img"
|
| 1265 |
-
width="1em"
|
| 1266 |
-
height="1em"
|
| 1267 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1268 |
-
viewBox="0 0 24 24"
|
| 1269 |
-
style="transform: rotate(360deg);"
|
| 1270 |
-
>
|
| 1271 |
-
<path
|
| 1272 |
-
d="M13 14c-3.36 0-4.46 1.35-4.82 2.24C9.25 16.7 10 17.76 10 19a3 3 0 0 1-3 3a3 3 0 0 1-3-3c0-1.31.83-2.42 2-2.83V7.83A2.99 2.99 0 0 1 4 5a3 3 0 0 1 3-3a3 3 0 0 1 3 3c0 1.31-.83 2.42-2 2.83v5.29c.88-.65 2.16-1.12 4-1.12c2.67 0 3.56-1.34 3.85-2.23A3.006 3.006 0 0 1 14 7a3 3 0 0 1 3-3a3 3 0 0 1 3 3c0 1.34-.88 2.5-2.09 2.86C17.65 11.29 16.68 14 13 14m-6 4a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1M7 4a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1m10 2a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1z"
|
| 1273 |
-
fill="currentColor"
|
| 1274 |
-
></path>
|
| 1275 |
-
</svg>
|
| 1276 |
-
main
|
| 1277 |
-
<svg
|
| 1278 |
-
alt=""
|
| 1279 |
-
class="text-gray-500"
|
| 1280 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1281 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1282 |
-
aria-hidden="true"
|
| 1283 |
-
focusable="false"
|
| 1284 |
-
role="img"
|
| 1285 |
-
width="1em"
|
| 1286 |
-
height="1em"
|
| 1287 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1288 |
-
viewBox="0 0 24 24"
|
| 1289 |
-
style="transform: rotate(360deg);"
|
| 1290 |
-
>
|
| 1291 |
-
<path d="M7 10l5 5l5-5z" fill="currentColor"></path>
|
| 1292 |
-
</svg>
|
| 1293 |
-
</button><div
|
| 1294 |
-
class="v2-dropdown-menu hidden absolute mt-1 min-w-full bg-white rounded-xl overflow-hidden shadow-lg z-10 border border-gray-100 right-0 top-full "
|
| 1295 |
-
>
|
| 1296 |
-
<ul class="min-w-full w-auto">
|
| 1297 |
-
<li>
|
| 1298 |
-
<ul>
|
| 1299 |
-
<li>
|
| 1300 |
-
<div
|
| 1301 |
-
class="v2-dropdown-entry flex items-center hover:bg-gray-50 cursor-pointer truncate
|
| 1302 |
-
hover:underline
|
| 1303 |
-
|
| 1304 |
-
|
| 1305 |
-
"
|
| 1306 |
-
|
| 1307 |
-
|
| 1308 |
-
|
| 1309 |
-
|
| 1310 |
-
>
|
| 1311 |
-
<a
|
| 1312 |
-
class="px-3 py-1.5"
|
| 1313 |
-
href="/Rostlab/prot_t5_xl_bfd/blob/main/special_tokens_map.json"
|
| 1314 |
-
>
|
| 1315 |
-
main
|
| 1316 |
-
</a>
|
| 1317 |
-
</div>
|
| 1318 |
-
</li>
|
| 1319 |
-
</ul>
|
| 1320 |
-
</li>
|
| 1321 |
-
<li>
|
| 1322 |
-
<ul class="bg-yellow-50">
|
| 1323 |
-
</ul>
|
| 1324 |
-
</li>
|
| 1325 |
-
</ul>
|
| 1326 |
-
</div></div>
|
| 1327 |
-
|
| 1328 |
-
<a class="hover:underline text-gray-800 dark:text-gray-300" href="/Rostlab/prot_t5_xl_bfd/tree/main">
|
| 1329 |
-
Rostlab/prot_t5_xl_bfd
|
| 1330 |
-
</a>
|
| 1331 |
-
<span class="text-gray-300 mx-1 font-light">/</span>
|
| 1332 |
-
|
| 1333 |
-
<span class="font-light dark:text-gray-300">special_tokens_map.json</span>
|
| 1334 |
-
|
| 1335 |
-
</div>
|
| 1336 |
-
|
| 1337 |
-
<div class="flex justify-end">
|
| 1338 |
-
</div>
|
| 1339 |
-
</header>
|
| 1340 |
-
|
| 1341 |
-
<div class="border border-b-0 dark:border-gray-800 px-3 py-2 flex items-baseline rounded-t-lg bg-gradient-to-b from-white to-gray-100 dark:from-gray-800 dark:to-gray-900">
|
| 1342 |
-
<div class="mr-5 truncate flex-none">
|
| 1343 |
-
system
|
| 1344 |
-
</div>
|
| 1345 |
-
<a
|
| 1346 |
-
class="mr-4 font-mono text-sm text-gray-500 dark:text-gray-300 truncate hover:underline"
|
| 1347 |
-
href="/Rostlab/prot_t5_xl_bfd/commit/459da522a657f58fa70bdfc39afc7b0e3d247f3e"
|
| 1348 |
-
>
|
| 1349 |
-
Update special_tokens_map.json
|
| 1350 |
-
</a>
|
| 1351 |
-
<a
|
| 1352 |
-
class="text-sm border dark:border-gray-800 px-1.5 rounded bg-gray-50 dark:bg-gray-900 hover:underline"
|
| 1353 |
-
href="/Rostlab/prot_t5_xl_bfd/commit/459da522a657f58fa70bdfc39afc7b0e3d247f3e"
|
| 1354 |
-
>
|
| 1355 |
-
459da52
|
| 1356 |
-
</a>
|
| 1357 |
-
<time
|
| 1358 |
-
datetime="2020-10-01T17:17:06"
|
| 1359 |
-
class="ml-auto hidden lg:block text-gray-500 dark:text-gray-400 truncate flex-none pl-2"
|
| 1360 |
-
>
|
| 1361 |
-
4 months ago
|
| 1362 |
-
</time>
|
| 1363 |
-
</div>
|
| 1364 |
-
|
| 1365 |
-
<div class="flex items-center p-3 border dark:border-gray-800 text-sm text-gray-700 dark:bg-gray-900">
|
| 1366 |
-
<a class="flex items-center hover:underline mr-4" href="/Rostlab/prot_t5_xl_bfd/raw/main/special_tokens_map.json">
|
| 1367 |
-
<svg
|
| 1368 |
-
alt=""
|
| 1369 |
-
class="mr-1.5 "
|
| 1370 |
-
xmlns="http://www.w3.org/2000/svg"
|
| 1371 |
-
xmlns:xlink="http://www.w3.org/1999/xlink"
|
| 1372 |
-
aria-hidden="true"
|
| 1373 |
-
focusable="false"
|
| 1374 |
-
role="img"
|
| 1375 |
-
width="1em"
|
| 1376 |
-
height="1em"
|
| 1377 |
-
preserveAspectRatio="xMidYMid meet"
|
| 1378 |
-
viewBox="0 0 32 32"
|
| 1379 |
-
style="transform: rotate(360deg);"
|
| 1380 |
-
>
|
| 1381 |
-
<path
|
| 1382 |
-
d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z"
|
| 1383 |
-
fill="currentColor"
|
| 1384 |
-
></path>
|
| 1385 |
-
<path
|
| 1386 |
-
d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z"
|
| 1387 |
-
fill="currentColor"
|
| 1388 |
-
></path>
|
| 1389 |
-
<path
|
| 1390 |
-
d="M12.419 25.484L17.639 6l1.932.518L14.35 26z"
|
| 1391 |
-
fill="currentColor"
|
| 1392 |
-
></path>
|
| 1393 |
-
</svg>
|
| 1394 |
-
raw
|
| 1395 |
-
</a>
|
| 1396 |
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|
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<a class="flex items-center hover:underline mr-4" href="/Rostlab/prot_t5_xl_bfd/blame/main/special_tokens_map.json">
|
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blame
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</a>
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<div class="ml-auto dark:text-gray-300">
|
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1,786 Bytes
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<td class="align-top text-right select-none">
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<pre class="p-3 text-sm overflow-x-auto">1</pre>
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<td class="align-top w-full">
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<pre class="p-3 text-sm overflow-x-auto">{<span class="hljs-attr">"eos_token"</span>: <span class="hljs-string">"</s>"</span>, <span class="hljs-attr">"unk_token"</span>: <span class="hljs-string">"<unk>"</span>, <span class="hljs-attr">"pad_token"</span>: <span class="hljs-string">"<pad>"</span>, <span class="hljs-attr">"additional_special_tokens"</span>: [<span class="hljs-string">"<extra_id_0>"</span>, <span class="hljs-string">"<extra_id_1>"</span>, <span class="hljs-string">"<extra_id_2>"</span>, <span class="hljs-string">"<extra_id_3>"</span>, <span class="hljs-string">"<extra_id_4>"</span>, <span class="hljs-string">"<extra_id_5>"</span>, <span class="hljs-string">"<extra_id_6>"</span>, <span class="hljs-string">"<extra_id_7>"</span>, <span class="hljs-string">"<extra_id_8>"</span>, <span class="hljs-string">"<extra_id_9>"</span>, <span class="hljs-string">"<extra_id_10>"</span>, <span class="hljs-string">"<extra_id_11>"</span>, <span class="hljs-string">"<extra_id_12>"</span>, <span class="hljs-string">"<extra_id_13>"</span>, <span class="hljs-string">"<extra_id_14>"</span>, <span class="hljs-string">"<extra_id_15>"</span>, <span class="hljs-string">"<extra_id_16>"</span>, <span class="hljs-string">"<extra_id_17>"</span>, <span class="hljs-string">"<extra_id_18>"</span>, <span class="hljs-string">"<extra_id_19>"</span>, <span class="hljs-string">"<extra_id_20>"</span>, <span class="hljs-string">"<extra_id_21>"</span>, <span class="hljs-string">"<extra_id_22>"</span>, <span class="hljs-string">"<extra_id_23>"</span>, <span class="hljs-string">"<extra_id_24>"</span>, <span class="hljs-string">"<extra_id_25>"</span>, <span class="hljs-string">"<extra_id_26>"</span>, <span class="hljs-string">"<extra_id_27>"</span>, <span class="hljs-string">"<extra_id_28>"</span>, <span class="hljs-string">"<extra_id_29>"</span>, <span class="hljs-string">"<extra_id_30>"</span>, <span class="hljs-string">"<extra_id_31>"</span>, <span class="hljs-string">"<extra_id_32>"</span>, <span class="hljs-string">"<extra_id_33>"</span>, <span class="hljs-string">"<extra_id_34>"</span>, <span class="hljs-string">"<extra_id_35>"</span>, <span class="hljs-string">"<extra_id_36>"</span>, <span class="hljs-string">"<extra_id_37>"</span>, <span class="hljs-string">"<extra_id_38>"</span>, <span class="hljs-string">"<extra_id_39>"</span>, <span class="hljs-string">"<extra_id_40>"</span>, <span class="hljs-string">"<extra_id_41>"</span>, <span class="hljs-string">"<extra_id_42>"</span>, <span class="hljs-string">"<extra_id_43>"</span>, <span class="hljs-string">"<extra_id_44>"</span>, <span class="hljs-string">"<extra_id_45>"</span>, <span class="hljs-string">"<extra_id_46>"</span>, <span class="hljs-string">"<extra_id_47>"</span>, <span class="hljs-string">"<extra_id_48>"</span>, <span class="hljs-string">"<extra_id_49>"</span>, <span class="hljs-string">"<extra_id_50>"</span>, <span class="hljs-string">"<extra_id_51>"</span>, <span class="hljs-string">"<extra_id_52>"</span>, <span class="hljs-string">"<extra_id_53>"</span>, <span class="hljs-string">"<extra_id_54>"</span>, <span class="hljs-string">"<extra_id_55>"</span>, <span class="hljs-string">"<extra_id_56>"</span>, <span class="hljs-string">"<extra_id_57>"</span>, <span class="hljs-string">"<extra_id_58>"</span>, <span class="hljs-string">"<extra_id_59>"</span>, <span class="hljs-string">"<extra_id_60>"</span>, <span class="hljs-string">"<extra_id_61>"</span>, <span class="hljs-string">"<extra_id_62>"</span>, <span class="hljs-string">"<extra_id_63>"</span>, <span class="hljs-string">"<extra_id_64>"</span>, <span class="hljs-string">"<extra_id_65>"</span>, <span class="hljs-string">"<extra_id_66>"</span>, <span class="hljs-string">"<extra_id_67>"</span>, <span class="hljs-string">"<extra_id_68>"</span>, <span class="hljs-string">"<extra_id_69>"</span>, <span class="hljs-string">"<extra_id_70>"</span>, <span class="hljs-string">"<extra_id_71>"</span>, <span class="hljs-string">"<extra_id_72>"</span>, <span class="hljs-string">"<extra_id_73>"</span>, <span class="hljs-string">"<extra_id_74>"</span>, <span class="hljs-string">"<extra_id_75>"</span>, <span class="hljs-string">"<extra_id_76>"</span>, <span class="hljs-string">"<extra_id_77>"</span>, <span class="hljs-string">"<extra_id_78>"</span>, <span class="hljs-string">"<extra_id_79>"</span>, <span class="hljs-string">"<extra_id_80>"</span>, <span class="hljs-string">"<extra_id_81>"</span>, <span class="hljs-string">"<extra_id_82>"</span>, <span class="hljs-string">"<extra_id_83>"</span>, <span class="hljs-string">"<extra_id_84>"</span>, <span class="hljs-string">"<extra_id_85>"</span>, <span class="hljs-string">"<extra_id_86>"</span>, <span class="hljs-string">"<extra_id_87>"</span>, <span class="hljs-string">"<extra_id_88>"</span>, <span class="hljs-string">"<extra_id_89>"</span>, <span class="hljs-string">"<extra_id_90>"</span>, <span class="hljs-string">"<extra_id_91>"</span>, <span class="hljs-string">"<extra_id_92>"</span>, <span class="hljs-string">"<extra_id_93>"</span>, <span class="hljs-string">"<extra_id_94>"</span>, <span class="hljs-string">"<extra_id_95>"</span>, <span class="hljs-string">"<extra_id_96>"</span>, <span class="hljs-string">"<extra_id_97>"</span>, <span class="hljs-string">"<extra_id_98>"</span>, <span class="hljs-string">"<extra_id_99>"</span>]}</pre>
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+
{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}
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|
spiece.model
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:74da7b4afcde53faa570114b530c726135bdfcdb813dec3abfb27f9d44db7324
|
| 3 |
+
size 237990
|