| Model | # of parameters | # of hidden size | Pretraining dataset | # of ncRNAs | Model download |
|---|---|---|---|---|---|
| ncRNABert | 303M | 1024 | RNAcentral | 26M | Download |
| ncRNABert | 303M | 1024 | RNAcentral + nt | - | Download |
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
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))
This source code is licensed under the Apache-2.0 license found in the LICENSE file in the root directory of this source tree.