RoBERTpep
This repository contains relevant information of our research article 'Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences'.
Fonte do modelo
- Tipo
- Embedding
- Tamanho do arquivo
- 293 MB
- Modelo base
- FacebookAI/roberta-base
- Versão
- vRevision 8d07806
- Atualizado
- 4 de out. de 2026
- Publicador
- @keiwoo
- Downloads
- 385
- Fonte
- Hugging Face
Descrição da fonte
This repository contains relevant information of our research article 'Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences'.
Embedding examples
Transformers
You can use RoBERTpep with Transformers. To get started, install the necessary dependencies to setup your environment:
pip install -U transformers torch
Once setup you can proceed to run the model by running the snippet below:
from transformers import BertTokenizer, RobertaModel
import torch
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
tokenizer = BertTokenizer.from_pretrained('keiwoo/RoBERTpep', do_lower_case = False)
model = RobertaModel.from_pretrained('keiwoo/RoBERTpep').to(device)
outputs = model(**tokenizer(' '.join('KLGGALQAK'), return_tensors="pt").to(device))
print(outputs.last_hidden_state[0].shape)
# torch.Size([11, 1024]) [CLS+N+SEP, 1024]
Fine-tuning
Please refer to https://github.com/keiwoo/RoBERTcr
Fontes
1 fonte
Fontes
1 fonteVerificado 4 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- license: mit datasets: - keiwoo/peptide base_model: - FacebookAI/roberta-base pipeline_tag: feature-extraction tags: - biology - medical metrics: - accuracy --- This repository contains relevant information of our research article *'Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences'*. ### Embedding examples #### Transformers You can use RoBERTpep with Transformers. To get started, install the necessary dependencies to setup your environment: ``` pip install -U transformers torch ``` Once setup you can proceed to run the model by running the snippet below: ``` from transformers import BertTokenizer, RobertaModel import torch device = 'cuda:0' if torch.cuda.is_available() else 'cpu' tokenizer = BertTokenizer.from_pretrained('keiwoo/RoBERTpep', do_lower_case = False) model = RobertaModel.from_pretrained('keiwoo/RoBERTpep').to(device) outputs = model(**tokenizer(' '.join('KLGGALQAK'), return_tensors="pt").to(device)) print(outputs.last_hidden_state[0].shape) # torch.Size([11, 1024]) [CLS+N+SEP, 1024] ``` ### Fine-tuning Please refer to [https://github.com/keiwoo/RoBERTcr](https://github.com/keiwoo/RoBERTcr)
Source context: 385 downloads · 0 likes · Pipeline feature-extraction · Repo keiwoo/RoBERTpep
Source context: 42 downloads · 0 likes · Pipeline feature-extraction · Repo keiwoo/RoBERTpep
