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prot_t5_xl_uniref50

Text2Text Generation
Transformers
PyTorch
t5
protein language model
Inference Endpoints
text-generation-inference
Model card Files Community
2
Ahmed Elnaggar commited on
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+
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+ >
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+ <button
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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"
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+ >
1140
+ <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"
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+ xmlns:xlink="http://www.w3.org/1999/xlink"
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+ focusable="false"
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+ width="1em"
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+ height="1em"
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+ viewBox="0 0 32 32"
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+ style="transform: rotate(360deg);"
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+ >
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+ <path
1155
+ 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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+ d="M12.419 25.484L17.639 6l1.932.518L14.35 26z"
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+ fill="currentColor"
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+ ></path>
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+ </svg>
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+ 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=""
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+ class="mr-1"
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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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+ focusable="false"
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+ width="1em"
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+ height="1em"
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+ viewBox="0 0 32 32"
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+ >
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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"
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+ fill="currentColor"
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+ ></path>
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+ </svg>
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+ 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=""
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+ class="mr-1 text-gray-700 dark: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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+ width="1em"
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+ height="1em"
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+ preserveAspectRatio="xMidYMid meet"
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+ >
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+ <path
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+ fill="currentColor"
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+ ></path>
1275
+ </svg>
1276
+ main
1277
+ <svg
1278
+ alt=""
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+ class="text-gray-500"
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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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1286
+ height="1em"
1287
+ preserveAspectRatio="xMidYMid meet"
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+ style="transform: rotate(360deg);"
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+ >
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+ <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
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1343
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1344
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1346
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1347
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1348
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1349
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1351
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1352
+ <a
1353
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1354
+ href="/Rostlab/prot_t5_xl_bfd/commit/7ae1d5c1d148d6c65c7e294cc72807e5b454fdb7"
1355
+ >
1356
+ Migrate model card from transformers-repo
1357
+ </a>
1358
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+ </td>
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+ <td class="align-top w-full">
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+ <pre class="p-3 text-sm overflow-x-auto">---
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+ language: protein
1595
+ tags:
1596
+ <span class="hljs-bullet">-</span> protein language model
1597
+ datasets:
1598
+ <span class="hljs-section">- BFD
1599
+ ---</span>
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+
1601
+ <span class="hljs-section"># ProtT5-XL-BFD model</span>
1602
+
1603
+ Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in
1604
+ [<span class="hljs-string">this paper</span>](<span class="hljs-link">https://doi.org/10.1101/2020.07.12.199554</span>) and first released in
1605
+ [<span class="hljs-string">this repository</span>](<span class="hljs-link">https://github.com/agemagician/ProtTrans</span>). This model is trained on uppercase amino acids: it only works with capital letter amino acids.
1606
+
1607
+
1608
+ <span class="hljs-section">## Model description</span>
1609
+
1610
+ ProtT5-XL-BFD is based on the <span class="hljs-code">`t5-3b`</span> model and was pretrained on a large corpus of protein sequences in a self-supervised fashion.
1611
+ 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
1612
+ publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
1613
+
1614
+ One important difference between this T5 model and the original T5 version is the denosing objective.
1615
+ The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective.
1616
+ The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input.
1617
+
1618
+ It has been shown that the features extracted from this self-supervised model (LM-embeddings) captured important biophysical properties governing protein shape.
1619
+ shape.
1620
+ This implied learning some of the grammar of the language of life realized in protein sequences.
1621
+
1622
+ <span class="hljs-section">## Intended uses &amp; limitations</span>
1623
+
1624
+ The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
1625
+ We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor.
1626
+ We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder.
1627
+
1628
+ <span class="hljs-section">### How to use</span>
1629
+
1630
+ Here is how to use this model to extract the features of a given protein sequence in PyTorch:
1631
+
1632
+ <span class="hljs-code">```python
1633
+ from transformers import T5Tokenizer, T5Model
1634
+ import re
1635
+ import torch
1636
+
1637
+ tokenizer = T5Tokenizer.from_pretrained(&#x27;Rostlab/prot_t5_xl_bfd&#x27;, do_lower_case=False)
1638
+
1639
+ model = T5Model.from_pretrained(&quot;Rostlab/prot_t5_xl_bfd&quot;)
1640
+
1641
+ sequences_Example = [&quot;A E T C Z A O&quot;,&quot;S K T Z P&quot;]
1642
+
1643
+ sequences_Example = [re.sub(r&quot;[UZOB]&quot;, &quot;X&quot;, sequence) for sequence in sequences_Example]
1644
+
1645
+ ids = tokenizer.batch_encode_plus(sequences_Example, add_special_tokens=True, padding=True)
1646
+
1647
+ input_ids = torch.tensor(ids[&#x27;input_ids&#x27;])
1648
+ attention_mask = torch.tensor(ids[&#x27;attention_mask&#x27;])
1649
+
1650
+ with torch.no_grad():
1651
+ embedding = model(input_ids=input_ids,attention_mask=attention_mask,decoder_input_ids=None)
1652
+
1653
+ # For feature extraction we recommend to use the encoder embedding
1654
+ encoder_embedding = embedding[2].cpu().numpy()
1655
+ decoder_embedding = embedding[0].cpu().numpy()
1656
+ ```</span>
1657
+
1658
+ <span class="hljs-section">## Training data</span>
1659
+
1660
+ The ProtT5-XL-BFD model was pretrained on [<span class="hljs-string">BFD</span>](<span class="hljs-link">https://bfd.mmseqs.com/</span>), a dataset consisting of 2.1 billion protein sequences.
1661
+
1662
+ <span class="hljs-section">## Training procedure</span>
1663
+
1664
+ <span class="hljs-section">### Preprocessing</span>
1665
+
1666
+ The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids &quot;U,Z,O,B&quot; were mapped to &quot;X&quot;.
1667
+ The inputs of the model are then of the form:
1668
+
1669
+ <span class="hljs-code">```
1670
+ Protein Sequence [EOS]
1671
+ ```</span>
1672
+
1673
+ The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens.
1674
+
1675
+ The details of the masking procedure for each sequence are as follows:
1676
+ <span class="hljs-bullet">-</span> 15% of the amino acids are masked.
1677
+ <span class="hljs-bullet">-</span> In 90% of the cases, the masked amino acids are replaced by <span class="hljs-code">`[MASK]`</span> token.
1678
+ <span class="hljs-bullet">-</span> In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
1679
+
1680
+ <span class="hljs-section">### Pretraining</span>
1681
+
1682
+ 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).
1683
+ It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture.
1684
+ The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.
1685
+
1686
+
1687
+ <span class="hljs-section">## Evaluation results</span>
1688
+
1689
+ When the model is used for feature etraction, this model achieves the following results:
1690
+
1691
+ Test results :
1692
+
1693
+ | Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane |
1694
+ |:-----:|:-----:|:-----:|:-----:|:-----:|
1695
+ | CASP12 | 77 | 66 | | |
1696
+ | TS115 | 85 | 74 | | |
1697
+ | CB513 | 84 | 71 | | |
1698
+ | DeepLoc | | | 77 | 91 |
1699
+
1700
+ <span class="hljs-section">### BibTeX entry and citation info</span>
1701
+
1702
+ <span class="hljs-code">```bibtex
1703
+ @article {Elnaggar2020.07.12.199554,
1704
+ 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},
1705
+ title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
1706
+ elocation-id = {2020.07.12.199554},
1707
+ year = {2020},
1708
+ doi = {10.1101/2020.07.12.199554},
1709
+ publisher = {Cold Spring Harbor Laboratory},
1710
+ 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: \&amp;lt;a href=&quot;https://github.com/agemagician/ProtTrans&quot;\&amp;gt;https://github.com/agemagician/ProtTrans\&amp;lt;/a\&amp;gt;Competing Interest StatementThe authors have declared no competing interest.},
1711
+ URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
1712
+ eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
1713
+ journal = {bioRxiv}
1714
+ }
1715
+ ```</span>
1716
+
1717
+ <span class="hljs-quote">&gt; Created by [<span class="hljs-string">Ahmed Elnaggar/@Elnaggar_AI</span>](<span class="hljs-link">https://twitter.com/Elnaggar_AI</span>) | [<span class="hljs-string">LinkedIn</span>](<span class="hljs-link">https://www.linkedin.com/in/prof-ahmed-elnaggar/</span>)</span>
1718
+ </pre>
1719
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+ </tr>
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+ ></path>
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+ fill="#F8BF3C"
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+
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+ href="/models?filter=protein"
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+
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+ </a>
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+ tag-purple
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+ seq2seq
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+ "
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+ protein language model
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+ </div>
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+ <div class="flex items-center h-12 -mb-px overflow-x-auto overflow-y-hidden">
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+ <a
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+ class="tab-alternate "
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+ href="/Rostlab/prot_t5_xl_bfd"
1064
+ >
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+ alt=""
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+ </a>
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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"
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+ xmlns:xlink="http://www.w3.org/1999/xlink"
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+ 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"
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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
1163
+ d="M12.419 25.484L17.639 6l1.932.518L14.35 26z"
1164
+ fill="currentColor"
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+ ></path>
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+ </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=""
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+ class="mr-1"
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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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1190
+ width="1em"
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+ height="1em"
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+ preserveAspectRatio="xMidYMid meet"
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+ viewBox="0 0 32 32"
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+ >
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+ <path
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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"
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+ fill="currentColor"
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+ ></path>
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+ </svg>
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+ 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"
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+ xmlns:xlink="http://www.w3.org/1999/xlink"
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+ aria-hidden="true"
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+ height="1em"
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1269
+ style="transform: rotate(360deg);"
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+ >
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+ 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"
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+ 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"
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+ xmlns:xlink="http://www.w3.org/1999/xlink"
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1286
+ height="1em"
1287
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1289
+ style="transform: rotate(360deg);"
1290
+ >
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+ <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"
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+ aria-hidden="true"
1373
+ focusable="false"
1374
+ role="img"
1375
+ width="1em"
1376
+ height="1em"
1377
+ preserveAspectRatio="xMidYMid meet"
1378
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+ <pre class="p-3 text-sm overflow-x-auto">1</pre>
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1461
+ <pre class="p-3 text-sm overflow-x-auto">{<span class="hljs-attr">&quot;eos_token&quot;</span>: <span class="hljs-string">&quot;&lt;/s&gt;&quot;</span>, <span class="hljs-attr">&quot;unk_token&quot;</span>: <span class="hljs-string">&quot;&lt;unk&gt;&quot;</span>, <span class="hljs-attr">&quot;pad_token&quot;</span>: <span class="hljs-string">&quot;&lt;pad&gt;&quot;</span>, <span class="hljs-attr">&quot;additional_special_tokens&quot;</span>: [<span class="hljs-string">&quot;&lt;extra_id_0&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_1&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_2&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_3&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_4&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_5&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_6&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_7&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_8&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_9&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_10&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_11&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_12&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_13&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_14&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_15&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_16&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_17&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_18&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_19&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_20&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_21&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_22&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_23&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_24&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_25&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_26&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_27&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_28&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_29&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_30&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_31&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_32&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_33&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_34&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_35&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_36&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_37&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_38&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_39&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_40&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_41&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_42&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_43&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_44&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_45&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_46&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_47&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_48&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_49&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_50&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_51&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_52&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_53&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_54&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_55&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_56&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_57&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_58&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_59&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_60&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_61&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_62&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_63&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_64&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_65&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_66&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_67&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_68&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_69&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_70&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_71&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_72&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_73&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_74&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_75&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_76&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_77&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_78&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_79&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_80&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_81&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_82&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_83&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_84&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_85&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_86&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_87&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_88&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_89&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_90&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_91&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_92&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_93&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_94&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_95&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_96&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_97&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_98&gt;&quot;</span>, <span class="hljs-string">&quot;&lt;extra_id_99&gt;&quot;</span>]}</pre>
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