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'.
Source du modèle
- Type
- Embedding
- Taille du fichier
- 293 MB
- Modèle de base
- FacebookAI/roberta-base
- Version
- vRevision 8d07806
- Mis à jour
- 4 oct. 2026
- Éditeur
- @keiwoo
- Téléchargements
- 385
- Source
- Hugging Face
Description de la source
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
Sources
1 source
Sources
1 sourceVérifié 4 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- 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
