## ncRNABert: Deciphering the landscape of non-coding RNA using language model
[](https://pypi.org/project/ncRNABert/) [](https://pypi.org/project/ncRNABert/) [](./LICENSE)  [](https://pypi.org/project/ncRNABert/) 
### Model details
| **Model** | **# of parameters** | **# of hidden size** | **Pretraining dataset** | **# of ncRNAs** | **Model download** |
|:--------------:|:-------------------:|:----------------------:|:----------------------------------------------:|:-----------------:|:------------------------:|
| ncRNABert | 303M | 1024 | [RNAcentral](http://ftp.ebi.ac.uk/pub/databases/RNAcentral/current_release/sequences/rnacentral_active.fasta.gz) | 26M | [Download](https://zenodo.org/record/8263889/files/ncRNABert.pt) |
| ncRNABert | 303M | 1024 | [RNAcentral](http://ftp.ebi.ac.uk/pub/databases/RNAcentral/current_release/sequences/rnacentral_active.fasta.gz) + nt | - | [Download](https://zenodo.org/records/10421246/files/ncRNABert_nt_rnacentral_3kmer.pt) |
### Install
As a prerequisite, you must have PyTorch installed to use this repository.
You can use this one-liner for installation, using the latest release version
```
# latest version
pip install git+https://github.com/wangleiofficial/ncRNABert
# stable version
pip install ncRNABert
```
### Usage
#### ncRNA sequence embedding
```
from ncRNABert.pretrain import load_ncRNABert, load_ncRNABert_ex
from ncRNABert.utils import BatchConverter
import torch
data = [
("ncRNA1", "ACGGAGGATGCGAGCGTTATCCGGATTTACTGGGCG"),
("ncRNA2", "AGGTTTTTAATCTAATTAAGATAGTTGA"),
]
ids, batch_token, lengths = BatchConverter(data)
model = load_ncRNABert()
model_ex = load_ncRNABert_ex()
with torch.no_grad():
results = model(batch_token, lengths, repr_layers=[24])
results_ex = model_ex(batch_token, lengths, repr_layers=[24])
# Generate per-sequence representations via averaging
token_representations = results["representations"][24]
token_representations_ex = results_ex["representations"][24]
sequence_representations = []
sequence_representations_ex = []
batch_lens = [len(item[1]) for item in data]
for i, tokens_len in enumerate(batch_lens):
sequence_representations.append(token_representations[i, 1 : tokens_len - 1].mean(0))
sequence_representations_ex.append(token_representations_ex[i, 1 : tokens_len - 1].mean(0))
```
### License
This source code is licensed under the Apache-2.0 license found in the LICENSE file in the root directory of this source tree.
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"description": "## ncRNABert: Deciphering the landscape of non-coding RNA using language model\n\n[](https://pypi.org/project/ncRNABert/) [](https://pypi.org/project/ncRNABert/) [](./LICENSE)  [](https://pypi.org/project/ncRNABert/) \n\n### Model details\n| **Model** | **# of parameters** | **# of hidden size** | **Pretraining dataset** | **# of ncRNAs** | **Model download** |\n|:--------------:|:-------------------:|:----------------------:|:----------------------------------------------:|:-----------------:|:------------------------:|\n| ncRNABert | 303M | 1024 | [RNAcentral](http://ftp.ebi.ac.uk/pub/databases/RNAcentral/current_release/sequences/rnacentral_active.fasta.gz) | 26M | [Download](https://zenodo.org/record/8263889/files/ncRNABert.pt) |\n| ncRNABert | 303M | 1024 | [RNAcentral](http://ftp.ebi.ac.uk/pub/databases/RNAcentral/current_release/sequences/rnacentral_active.fasta.gz) + nt | - | [Download](https://zenodo.org/records/10421246/files/ncRNABert_nt_rnacentral_3kmer.pt) |\n\n### Install\nAs a prerequisite, you must have PyTorch installed to use this repository.\n\nYou can use this one-liner for installation, using the latest release version\n\n```\n# latest version\npip install git+https://github.com/wangleiofficial/ncRNABert\n\n# stable version\npip install ncRNABert\n\n```\n\n### Usage\n\n#### ncRNA sequence embedding\n\n```\nfrom ncRNABert.pretrain import load_ncRNABert, load_ncRNABert_ex\nfrom ncRNABert.utils import BatchConverter\nimport torch\n\ndata = [\n (\"ncRNA1\", \"ACGGAGGATGCGAGCGTTATCCGGATTTACTGGGCG\"),\n (\"ncRNA2\", \"AGGTTTTTAATCTAATTAAGATAGTTGA\"),\n]\n\nids, batch_token, lengths = BatchConverter(data)\nmodel = load_ncRNABert()\nmodel_ex = load_ncRNABert_ex()\nwith torch.no_grad():\n results = model(batch_token, lengths, repr_layers=[24])\n results_ex = model_ex(batch_token, lengths, repr_layers=[24])\n# Generate per-sequence representations via averaging\ntoken_representations = results[\"representations\"][24]\ntoken_representations_ex = results_ex[\"representations\"][24]\nsequence_representations = []\nsequence_representations_ex = []\nbatch_lens = [len(item[1]) for item in data]\nfor i, tokens_len in enumerate(batch_lens):\n sequence_representations.append(token_representations[i, 1 : tokens_len - 1].mean(0))\n sequence_representations_ex.append(token_representations_ex[i, 1 : tokens_len - 1].mean(0))\n```\n\n### License\nThis source code is licensed under the Apache-2.0 license found in the LICENSE file in the root directory of this source tree.\n",
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