ModelVerificadoExterno

Pep2Tcr GRATCR

This repository contains relevant information of our research article 'Leveraging contrastive learning for conditional and robust TCR sequence generation'.

Fuentehuggingface.co/keiwoo/Pep2Tcr-GRATCR/tree/43e325036b944641df718fc7dac9dddee9f4a375

TipoOtherModelo basekeiwoo/RoBERTpepVersiónRevision 43e3250Publicador@keiwooCcalidad

Fuente del modelo

Tipo: Other
Tamaño del archivo: 682 MB
Modelo base: keiwoo/RoBERTpep
Versión: vRevision 43e3250
Actualizado: 4 oct 2026
Publicador: @keiwoo
Descargas: 10
Tareas: Generación de texto
Fuente: Hugging Face

Descripción de la fuente

This repository contains relevant information of our research article 'Leveraging contrastive learning for conditional and robust TCR sequence generation'.

Transformers

You can use Pep2Tcr trained on GRATCR dataset with Transformers. To get started, install the necessary dependencies to setup your environment:

pip install -U transformers torch

2. Run the generation

from transformers import EncoderDecoderModel, AutoTokenizer  
import torch

torch.cuda.manual_seed_all(42)
device = torch.device('cuda:1')

model_name = 'keiwoo/Pep2Tcr-GRATCR'
model = EncoderDecoderModel.from_pretrained(
    model_name, 
    dtype=torch.float16,    # If your device does not support `BF16` precision mode
                            # (GPU based on NVIDIA's Ampere and its subsequent architectures), you should comment this line.
    ).to(device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False)

sample_kwargs = {
    'do_sample': True,
    'top_k': 9,
    'top_p': 0.93,
    'temperature': 0.9,
    'num_return_sequences': 16,
    'repetition_penalty': 1.1,
    'no_repeat_ngram_size': 3,
    'max_new_tokens': 24,
}
beam_kwargs = {
    'num_beams': 16,
    'num_return_sequences': 16,
    'repetition_penalty': 1.1,
    'no_repeat_ngram_size': 3,
    'max_new_tokens': 24,
}
inputs = "AAGIGILTV"
tokenized = tokenizer(" ".join(inputs), return_tensors="pt", ).to(device)
outputs = model.generate(**tokenized, **sample_kwargs) # If you want different sequences every generation, uncomment this line. 
# outputs = model.generate(**tokenized, **beam_kwargs) # If you want same sequences every generation (most possible sequence the model think), uncomment this line.

tcr = tokenizer.batch_decode(outputs, skip_special_tokens=True)
tcr = [i.replace(' ', '') for i in tcr]
print(tcr)
# ['CASSQEGLAGGTDTQYF', 'CASSQEGLAGGYNEQFF', 'CASSQDPGLAGGYNEQFF', ...]

3. Reproduce the model

Please refer to https://github.com/keiwoo/Pep2Tcr

Fuentes

1 fuente

Hugging FacePrincipalVersión 43e325036b944641df718fc7dac9dddee9f4a375

huggingface.co/keiwoo/Pep2Tcr-GRATCR/tree/43e325036b944641df718fc7dac9dddee9f4a375

Verificado 4 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 682 MB · SHA-256 717bf1ec1232…efb6 · Hugging Face

Descargar

Extractos de fuentes

3 extractos

Readme11 julhuggingface.co/keiwoo/Pep2Tcr-GRATCR

--- license: apache-2.0 base_model: - keiwoo/RoBERTpep - keiwoo/RoBERTtcr pipeline_tag: text-generation --- This repository contains relevant information of our research article *'Leveraging contrastive learning for conditional and robust TCR sequence generation'*. #### Transformers You can use Pep2Tcr trained on [GRATCR dataset](https://doi.org/10.1109/JBHI.2024.3514089) with Transformers. To get started, install the necessary dependencies to setup your environment: ``` pip install -U transformers torch ``` ### 2. Run the generation ``` from transformers import EncoderDecoderModel, AutoTokenizer import torch torch.cuda.manual_seed_all(42) device = torch.device('cuda:1') model_name = 'keiwoo/Pep2Tcr-GRATCR' model = EncoderDecoderModel.from_pretrained( model_name, dtype=torch.float16, # If your device does not support `BF16` precision mode # (GPU based on NVIDIA's Ampere and its subsequent architectures), you should comment this line. ).to(device) model.eval() tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False) sample_kwargs = { 'do_sample': True, 'top_k': 9, 'top_p': 0.93, 'temperature': 0.9, 'num_return_sequences': 16, 'repetition_penalty': 1.1, 'no_repeat_ngram_size': 3, 'max_new_tokens': 24, } beam_kwargs = { 'num_beams': 16, 'num_return_sequences': 16, 'repetition_penalty': 1.1, 'no_repeat_ngram_size': 3, 'max_new_tokens': 24, } inputs = "AAGIGILTV" tokenized = tokenizer(" ".join(inputs), return_tensors="pt", ).to(device) outputs = model.generate(**tokenized, **sample_kwargs) # If you want different sequences every generation, uncomment this line. # outputs = model.generate(**tokenized, **beam_kwargs) # If you want same sequences every generation (most possible sequence the model think), uncomment this line. tcr = tokenizer.batch_decode(outputs, skip_special_tokens=True) tcr = [i.replace(' ', '') for i in tcr] print(tcr) # ['CASSQEGLAGGTDTQYF', 'CASSQEGLAGGYNEQFF', 'CASSQDPGLAGGYNEQFF', ...] ``` ### 3. Reproduce the model Please refer to [https://github.com/keiwoo/Pep2Tcr](https://github.com/keiwoo/Pep2Tcr)

Note4 octhuggingface.co/keiwoo/Pep2Tcr-GRATCR

Source context: 10 downloads · 0 likes · Pipeline text-generation · Repo keiwoo/Pep2Tcr-GRATCR

Note18 julhuggingface.co/keiwoo/Pep2Tcr-GRATCR

Source context: 105 downloads · 0 likes · Pipeline text-generation · Repo keiwoo/Pep2Tcr-GRATCR