[](https://discord.gg/qUtxnK2NMf)
# Progen
Implementation of Progen in Pytorch, from the paper "ProGen: Language Modeling for Protein Generation"
GPT for proteins sequences
[Paper Link](https://arxiv.org/pdf/2004.03497.pdf)
# Appreciation
* Lucidrains
* Agorians
# Install
`pip install progen-torch`
# Usage
```python
import torch
from progen.model import ProGen
x = torch.randint(0, 100, (1, 1024))
import torch
from progen.model import ProGen
x = torch.randint(0, 100, (1, 1024))
# Initialize the model with specific parameters
model = ProGen(
num_tokens=100, # The size of the vocabulary
dim=512, # The dimension of the embeddings
seq_len=1024, # The length of the sequences
depth=6, # The number of layers in the model
window_size=256, # The size of the window for local attention
global_mlp_depth=2, # The depth of the MLP in the global attention mechanism
heads=8, # The number of attention heads
dim_head=512, # The dimension of each attention head
ff_mult=4, # The multiplier for the feed-forward network's hidden layer size
ff_glu=True, # Whether to use a GLU activation in the feed-forward network
attn_dim=None, # The dimension of the attention mechanism (None means it defaults to `dim`)
clamp_gate=True, # Whether to clamp the gate values in the GLU activation
shift_tokens=True, # Whether to shift the tokens for the causal attention mechanism
dropout=0.1, # The dropout rate
)
# Forward pass through the model
logits = model(x)
# The output is the logits for each token in the vocabulary, for each position in the input sequences
# Shape: (batch_size, sequence_length, num_tokens)
print(logits.shape) # Should print: torch.Size([1, 1024, 100])
```
# Dataset Strategy
Here is a table of the datasets used in the paper with metadata and source links:
| Dataset | Description | Source |
|-|-|-|
| Uniparc | Contains protein sequences from various sources | https://www.uniprot.org/uniparc/ |
| UniprotKB | Contains protein sequences and annotations | https://www.uniprot.org/uniprot/ |
| SWISS-PROT | Curated protein sequence database | https://www.uniprot.org/swiss-prot/ |
| TrEMBL | Computer-annotated protein sequences | https://www.uniprot.org/trembl/ |
| Pfam | Database of protein families | https://pfam.xfam.org/ |
| NCBI taxonomy | Taxonomic classification of organisms | https://www.ncbi.nlm.nih.gov/taxonomy |
Here is a diagram showing the data preprocessing flow:
```mermaid
graph TD
A[Uniparc] --> B[Filter and merge]
C[UniprotKB] --> B
D[SWISS-PROT] --> B
E[TrEMBL] --> B
F[Pfam] --> B
G[NCBI taxonomy] --> B
B --> H[Train/test split]
H --> I[Train set]
H --> J[ID test set]
H --> K[OOD test set]
```
The Uniparc, UniprotKB, SWISS-PROT, TrEMBL, Pfam, and NCBI taxonomy datasets are filtered and merged in step B. The aggregated dataset is then split into training, in-distribution test, and out-of-distribution test sets in step H.
# Architecture
# Todo
# License
MIT
# Citations
Raw data
{
"_id": null,
"home_page": "https://github.com/kyegomez/Progen",
"name": "progen-torch",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6,<4.0",
"maintainer_email": "",
"keywords": "artificial intelligence,deep learning,optimizers,Prompt Engineering",
"author": "Kye Gomez",
"author_email": "kye@apac.ai",
"download_url": "https://files.pythonhosted.org/packages/ef/39/22208336bddfb277e15fec662aa554254e04cf6955d7e834530e32d5ee00/progen_torch-0.0.4.tar.gz",
"platform": null,
"description": "[](https://discord.gg/qUtxnK2NMf)\n\n# Progen\nImplementation of Progen in Pytorch, from the paper \"ProGen: Language Modeling for Protein Generation\"\n\nGPT for proteins sequences\n\n[Paper Link](https://arxiv.org/pdf/2004.03497.pdf)\n\n# Appreciation\n* Lucidrains\n* Agorians\n\n# Install\n`pip install progen-torch`\n\n# Usage\n```python\nimport torch\nfrom progen.model import ProGen\n\nx = torch.randint(0, 100, (1, 1024))\nimport torch\nfrom progen.model import ProGen\n\nx = torch.randint(0, 100, (1, 1024))\n\n# Initialize the model with specific parameters\nmodel = ProGen(\n num_tokens=100, # The size of the vocabulary\n dim=512, # The dimension of the embeddings\n seq_len=1024, # The length of the sequences\n depth=6, # The number of layers in the model\n window_size=256, # The size of the window for local attention\n global_mlp_depth=2, # The depth of the MLP in the global attention mechanism\n heads=8, # The number of attention heads\n dim_head=512, # The dimension of each attention head\n ff_mult=4, # The multiplier for the feed-forward network's hidden layer size\n ff_glu=True, # Whether to use a GLU activation in the feed-forward network\n attn_dim=None, # The dimension of the attention mechanism (None means it defaults to `dim`)\n clamp_gate=True, # Whether to clamp the gate values in the GLU activation\n shift_tokens=True, # Whether to shift the tokens for the causal attention mechanism\n dropout=0.1, # The dropout rate\n)\n\n# Forward pass through the model\nlogits = model(x)\n\n# The output is the logits for each token in the vocabulary, for each position in the input sequences\n# Shape: (batch_size, sequence_length, num_tokens)\nprint(logits.shape) # Should print: torch.Size([1, 1024, 100])\n\n\n```\n\n# Dataset Strategy\nHere is a table of the datasets used in the paper with metadata and source links:\n\n| Dataset | Description | Source |\n|-|-|-| \n| Uniparc | Contains protein sequences from various sources | https://www.uniprot.org/uniparc/ |\n| UniprotKB | Contains protein sequences and annotations | https://www.uniprot.org/uniprot/ |\n| SWISS-PROT | Curated protein sequence database | https://www.uniprot.org/swiss-prot/ |\n| TrEMBL | Computer-annotated protein sequences | https://www.uniprot.org/trembl/ |\n| Pfam | Database of protein families | https://pfam.xfam.org/ |\n| NCBI taxonomy | Taxonomic classification of organisms | https://www.ncbi.nlm.nih.gov/taxonomy |\n\nHere is a diagram showing the data preprocessing flow:\n\n```mermaid\ngraph TD\n A[Uniparc] --> B[Filter and merge]\n C[UniprotKB] --> B\n D[SWISS-PROT] --> B \n E[TrEMBL] --> B\n F[Pfam] --> B\n G[NCBI taxonomy] --> B\n B --> H[Train/test split]\n H --> I[Train set]\n H --> J[ID test set] \n H --> K[OOD test set]\n```\n\nThe Uniparc, UniprotKB, SWISS-PROT, TrEMBL, Pfam, and NCBI taxonomy datasets are filtered and merged in step B. The aggregated dataset is then split into training, in-distribution test, and out-of-distribution test sets in step H.\n\n# Architecture\n\n# Todo\n\n\n# License\nMIT\n\n# Citations\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "Paper - Pytorch",
"version": "0.0.4",
"project_urls": {
"Homepage": "https://github.com/kyegomez/Progen",
"Repository": "https://github.com/kyegomez/Progen"
},
"split_keywords": [
"artificial intelligence",
"deep learning",
"optimizers",
"prompt engineering"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "8cb311053a05868bf7da1f633043f8e43a9563ca6d960639d838c7f763b325f8",
"md5": "85a96f4adbd263a9ebe94089fce8e959",
"sha256": "f789a64329f30e26b947606d73b31b5b95e73bad462e48e7dfa80980e372fe70"
},
"downloads": -1,
"filename": "progen_torch-0.0.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "85a96f4adbd263a9ebe94089fce8e959",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6,<4.0",
"size": 8629,
"upload_time": "2023-10-09T13:33:18",
"upload_time_iso_8601": "2023-10-09T13:33:18.936545Z",
"url": "https://files.pythonhosted.org/packages/8c/b3/11053a05868bf7da1f633043f8e43a9563ca6d960639d838c7f763b325f8/progen_torch-0.0.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ef3922208336bddfb277e15fec662aa554254e04cf6955d7e834530e32d5ee00",
"md5": "e32c533cebd4440d2e5213ffe9d2cf01",
"sha256": "96b7ee58a600ef61e48761ab5b712e7c8cba50ab2c1d2d3e6a37c5311d32c953"
},
"downloads": -1,
"filename": "progen_torch-0.0.4.tar.gz",
"has_sig": false,
"md5_digest": "e32c533cebd4440d2e5213ffe9d2cf01",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6,<4.0",
"size": 8195,
"upload_time": "2023-10-09T13:33:20",
"upload_time_iso_8601": "2023-10-09T13:33:20.540698Z",
"url": "https://files.pythonhosted.org/packages/ef/39/22208336bddfb277e15fec662aa554254e04cf6955d7e834530e32d5ee00/progen_torch-0.0.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-09 13:33:20",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "kyegomez",
"github_project": "Progen",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "progen-torch"
}
JSON
