Ahmed Elnaggar commited on
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99b17de
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Update prot-t5 from elnaggar

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  2. special_tokens_map.json +1 -1485
  3. spiece.model +2 -2
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- TensorFlow
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- <span>
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- BFD
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- </span>
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-
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- <a class="
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- tag
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- "
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- href="/models?filter=protein"
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- protein
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-
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- <span>
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- protein language model
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-
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- </a>
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- <div class="border-b border-gray-100">
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- <div class="flex items-center justify-between">
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- href="/Rostlab/prot_t5_xl_bfd"
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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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- >
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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"
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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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- height="1em"
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- preserveAspectRatio="xMidYMid meet"
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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
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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"
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- fill="currentColor"
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- ></path>
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- </svg>
1167
- Use in transformers
1168
- </button><div
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- 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 "
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- >
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- <ul class="min-w-full w-auto">
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- <div class="v2-to-clipboard" style="width: 700px;">
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- <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">
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- How to use from the
1176
- <a target="_blank" href="https://github.com/huggingface/transformers">🤗/transformers</a>
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- library:
1178
- </h3>
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- <button
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- class="v2-to-clipboard-button text-sm underline flex items-center cursor-pointer text-gray-500"
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- >
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- <svg
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- 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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- role="img"
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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
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-
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">
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- <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>
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-
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- </div>
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- </ul>
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- </div></div>
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- </div>
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- </div>
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- </div>
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- </div>
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- </header>
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- <div
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- class="container relative space-y-4 flex flex-col md:grid md:space-y-0 overflow-hidden
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- md:grid-cols-12
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-
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- md:gap-6
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-
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- "
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- >
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-
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- <header class="pb-4 flex items-center justify-between">
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- <div class="flex items-center">
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- <div
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- class="v2-dropdown relative mr-6"
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-
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- >
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- class="v2-dropdown-button cursor-pointer btn"
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- slot="trigger"
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- type="button"
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- <a class="hover:underline text-gray-800 dark:text-gray-300" href="/Rostlab/prot_t5_xl_bfd/tree/main">
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- </a>
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- <span class="text-gray-300 mx-1 font-light">/</span>
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- />
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1352
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1354
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1355
- >
1356
- Migrate model card from transformers-repo
1357
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1358
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1359
- class="text-sm border dark:border-gray-800 px-1.5 rounded bg-gray-50 dark:bg-gray-900 hover:underline"
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1364
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1366
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- >
1368
- 2 months ago
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1590
- 126</pre>
1591
- </td>
1592
- <td class="align-top w-full">
1593
- <pre class="p-3 text-sm overflow-x-auto">---
1594
  language: protein
1595
  tags:
1596
- <span class="hljs-bullet">-</span> protein language model
1597
  datasets:
1598
- <span class="hljs-section">- BFD
1599
- ---</span>
1600
 
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
 
@@ -1619,33 +27,33 @@ It has been shown that the features extracted from this self-supervised model (L
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)
@@ -1653,38 +61,38 @@ with torch.no_grad():
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
 
@@ -1697,9 +105,9 @@ Test results :
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},
@@ -1707,36 +115,11 @@ Test results :
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
- </td>
1720
- </tr>
1721
- </table>
1722
- </div>
1723
- </section>
1724
- </div>
1725
- </main>
1726
-
1727
- <script defer src="/front/build/bundle.04f8ca9.js"></script>
1728
- <script>
1729
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1730
- ['localhost', 'huggingface.test'].includes(window.location.hostname)
1731
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1732
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1733
- (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
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1735
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1736
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1737
- ganalytics('create', 'UA-83738774-2', 'auto');
1738
- ganalytics('send', 'pageview');
1739
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1740
- </script>
1741
- </body>
1742
- </html>
 
1
+ ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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: \&lt;a href="https://github.com/agemagician/ProtTrans"\&gt;https://github.com/agemagician/ProtTrans\&lt;/a\&gt;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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-
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- </a>
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- <a class="
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- tag
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-
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- tag-purple
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- "
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- href="/models?filter=t5"
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- tag-id="t5"
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- >
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- <span>
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- t5
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- </span>
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-
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- </a>
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- <a class="
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- tag
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-
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- tag-purple
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- "
1023
- href="/models?filter=lm-head"
1024
- tag-id="lm-head"
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- >
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- <span>
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- lm-head
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- </span>
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-
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- </a>
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- <a class="
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- tag
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-
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- tag-purple
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- "
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- href="/models?filter=seq2seq"
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- tag-id="seq2seq"
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- >
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- <span>
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- seq2seq
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- </span>
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-
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- </a>
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- <a class="
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- tag
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-
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- tag-purple
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- "
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- href="/models?filter=protein language model"
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- tag-id="protein language model"
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- >
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- <span>
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- protein language model
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- </span>
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-
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- </a>
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- </div>
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- <div class="border-b border-gray-100">
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- <div class="flex items-center justify-between">
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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"
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- >
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- <svg
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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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- height="1em"
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- preserveAspectRatio="xMidYMid meet"
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- viewBox="0 0 24 24"
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- >
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- <path
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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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- opacity=".25"
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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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- opacity=".5"
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- fill="currentColor"
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- ></path>
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- class="uim-primary"
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- fill="currentColor"
1094
- ></path>
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- </svg>
1096
- Model card
1097
- </a>
1098
- <a
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- class="tab-alternate active"
1100
- href="/Rostlab/prot_t5_xl_bfd/tree/main"
1101
- >
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- <svg
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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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- 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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- height="1em"
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- preserveAspectRatio="xMidYMid meet"
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- viewBox="0 0 24 24"
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- >
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- <path
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- class="uim-tertiary"
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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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- opacity=".5"
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- fill="currentColor"
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- ></path>
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- <path
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- class="uim-primary"
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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"
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- fill="currentColor"
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- ></path>
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- </svg>
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- Files and versions
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- </a>
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- </div>
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- <div class="relative hidden space-x-1.5 md:flex">
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- <div
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- class="v2-dropdown relative "
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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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- >
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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"
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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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- height="1em"
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- preserveAspectRatio="xMidYMid meet"
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- viewBox="0 0 32 32"
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- style="transform: rotate(360deg);"
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- >
1154
- <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>
1158
- <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"
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=""
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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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- role="img"
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- 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>
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- </div>
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- </div>
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- </div>
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- </div>
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- </header>
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- <div
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- class="container relative space-y-4 flex flex-col md:grid md:space-y-0 overflow-hidden
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- md:grid-cols-12
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-
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- md:gap-6
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-
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- "
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-
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-
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- >
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- <section class="pt-8 border-gray-100 col-span-full">
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-
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- <header class="pb-4 flex items-center justify-between">
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- <div class="flex items-center">
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- <div
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- class="v2-dropdown relative mr-6"
1250
-
1251
- >
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- <button
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- class="v2-dropdown-button cursor-pointer btn"
1254
- slot="trigger"
1255
- type="button"
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- >
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- <svg
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- 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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- focusable="false"
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- role="img"
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- width="1em"
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- height="1em"
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- preserveAspectRatio="xMidYMid meet"
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- viewBox="0 0 24 24"
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- style="transform: rotate(360deg);"
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- >
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- <path
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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"
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- ></path>
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- </svg>
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- main
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- <svg
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- 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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- 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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- height="1em"
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- preserveAspectRatio="xMidYMid meet"
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- viewBox="0 0 24 24"
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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>
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- </svg>
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- </button><div
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- 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 "
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- >
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- <ul class="min-w-full w-auto">
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- <li>
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- <ul>
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- <li>
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- <div
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- class="v2-dropdown-entry flex items-center hover:bg-gray-50 cursor-pointer truncate
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- hover:underline
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-
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-
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- "
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-
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-
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-
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-
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- >
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- <a
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- class="px-3 py-1.5"
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- href="/Rostlab/prot_t5_xl_bfd/blob/main/special_tokens_map.json"
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- >
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- main
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- </a>
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- </div>
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- </li>
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- </ul>
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- </li>
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- <li>
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- <ul class="bg-yellow-50">
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- </ul>
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- </li>
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- </ul>
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- </div></div>
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-
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- <a class="hover:underline text-gray-800 dark:text-gray-300" href="/Rostlab/prot_t5_xl_bfd/tree/main">
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- Rostlab/prot_t5_xl_bfd
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- </a>
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- <span class="text-gray-300 mx-1 font-light">/</span>
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-
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- <span class="font-light dark:text-gray-300">special_tokens_map.json</span>
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-
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-
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- <div class="flex justify-end">
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-
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- <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">
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- <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
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- </a>
1357
- <time
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- datetime="2020-10-01T17:17:06"
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- 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>
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- </div>
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-
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- <div class="flex items-center p-3 border dark:border-gray-800 text-sm text-gray-700 dark:bg-gray-900">
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- <a class="flex items-center hover:underline mr-4" href="/Rostlab/prot_t5_xl_bfd/raw/main/special_tokens_map.json">
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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"
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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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- height="1em"
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- viewBox="0 0 32 32"
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- style="transform: rotate(360deg);"
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- >
1381
- <path
1382
- d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z"
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- fill="currentColor"
1384
- ></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"
1388
- ></path>
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- <path
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- d="M12.419 25.484L17.639 6l1.932.518L14.35 26z"
1391
- fill="currentColor"
1392
- ></path>
1393
- </svg>
1394
- raw
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- </a>
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- <a class="flex items-center hover:underline mr-4" href="/Rostlab/prot_t5_xl_bfd/commits/main/special_tokens_map.json">
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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"
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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"
1404
- role="img"
1405
- width="1em"
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- height="1em"
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- preserveAspectRatio="xMidYMid meet"
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- viewBox="0 0 32 32"
1409
- style="transform: rotate(360deg);"
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- >
1411
- <path
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- d="M16 4C9.383 4 4 9.383 4 16s5.383 12 12 12s12-5.383 12-12S22.617 4 16 4zm0 2c5.535 0 10 4.465 10 10s-4.465 10-10 10S6 21.535 6 16S10.465 6 16 6zm-1 2v9h7v-2h-5V8z"
1413
- fill="currentColor"
1414
- ></path>
1415
- </svg>
1416
- history
1417
- </a>
1418
- <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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- <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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- aria-hidden="true"
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- focusable="false"
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- role="img"
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- class=""
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- width="1em"
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- preserveAspectRatio="xMidYMid meet"
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- viewBox="0 0 32 32"
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- style="transform: rotate(360deg);"
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- >
1434
- <path
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- d="M16 2a14 14 0 1 0 14 14A14 14 0 0 0 16 2zm0 26a12 12 0 1 1 12-12a12 12 0 0 1-12 12z"
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- fill="currentColor"
1437
- ></path>
1438
- <path
1439
- d="M11.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z"
1440
- fill="currentColor"
1441
- ></path>
1442
- <path
1443
- d="M20.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z"
1444
- fill="currentColor"
1445
- ></path>
1446
- </svg>
1447
- blame
1448
- </a>
1449
- <div class="ml-auto dark:text-gray-300">
1450
- 1,786 Bytes
1451
- </div>
1452
- </div>
1453
-
1454
- <div class="border border-t-0 rounded-b-lg dark:bg-gray-900 dark:border-gray-800 overflow-x-auto">
1455
- <table>
1456
- <tr>
1457
- <td class="align-top text-right select-none">
1458
- <pre class="p-3 text-sm overflow-x-auto">1</pre>
1459
- </td>
1460
- <td class="align-top w-full">
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 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1462
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