| --- |
| license: mit |
| tags: |
| - protein |
| - thermostability |
| --- |
| |
| __Purpose__: classifies protein sequence into Thermophilic (>= 60C) or Mesophilic (<30C) by host organism growth temperature. |
|
|
| __Usage__: |
| Prepare sequences identically to using the original pretrained model: |
|
|
| ``` |
| from transformers import BertModelForSequenceClassification, BertTokenizer |
| import torch |
| import re |
| tokenizer = BertTokenizer.from_pretrained("evankomp/learn2therm", do_lower_case=False ) |
| model = BertModelForSequenceClassification.from_pretrained("evankomp/learn2therm") |
| sequence_Example = "A E T C Z A O" |
| sequence_Example = re.sub(r"[UZOB]", "X", sequence_Example) |
| encoded_input = tokenizer(sequence_Example, return_tensors='pt') |
| output = torch.argmax(model(**encoded_input), dim=1) |
| ``` |
|
|
| 1 indicates thermophilic, 0 mesophilic. |
|
|
| __Training__: |
| ProteinBERT (Rostlab/prot_bert) was fine tuned on a class balanced version of learn2therm (see [here]()), about 250k protein amino acid sequences. |
| |
| Training parameters below: |
| ``` |
| TrainingArguments( |
| _n_gpu=1, |
| adafactor=False, |
| adam_beta1=0.9, |
| adam_beta2=0.999, |
| adam_epsilon=1e-08, |
| auto_find_batch_size=False, |
| bf16=False, |
| bf16_full_eval=False, |
| data_seed=None, |
| dataloader_drop_last=False, |
| dataloader_num_workers=0, |
| dataloader_pin_memory=True, |
| ddp_bucket_cap_mb=None, |
| ddp_find_unused_parameters=None, |
| ddp_timeout=1800, |
| debug=[], |
| deepspeed=None, |
| disable_tqdm=False, |
| do_eval=True, |
| do_predict=False, |
| do_train=True, |
| eval_accumulation_steps=25, |
| eval_delay=0, |
| eval_steps=6, |
| evaluation_strategy=steps, |
| fp16=True, |
| fp16_backend=auto, |
| fp16_full_eval=False, |
| fp16_opt_level=O1, |
| fsdp=[], |
| fsdp_min_num_params=0, |
| fsdp_transformer_layer_cls_to_wrap=None, |
| full_determinism=False, |
| gradient_accumulation_steps=25, |
| gradient_checkpointing=True, |
| greater_is_better=False, |
| group_by_length=False, |
| half_precision_backend=cuda_amp, |
| hub_model_id=None, |
| hub_private_repo=False, |
| hub_strategy=every_save, |
| hub_token=<HUB_TOKEN>, |
| ignore_data_skip=False, |
| include_inputs_for_metrics=False, |
| jit_mode_eval=False, |
| label_names=None, |
| label_smoothing_factor=0.0, |
| learning_rate=5e-05, |
| length_column_name=length, |
| load_best_model_at_end=True, |
| local_rank=0, |
| log_level=info, |
| log_level_replica=passive, |
| log_on_each_node=True, |
| logging_dir=./data/ogt_protein_classifier/model/runs/Jun19_12-16-35_g3070, |
| logging_first_step=False, |
| logging_nan_inf_filter=True, |
| logging_steps=1, |
| logging_strategy=steps, |
| lr_scheduler_type=linear, |
| max_grad_norm=1.0, |
| max_steps=-1, |
| metric_for_best_model=loss, |
| mp_parameters=, |
| no_cuda=False, |
| num_train_epochs=2, |
| optim=adamw_hf, |
| optim_args=None, |
| output_dir=./data/ogt_protein_classifier/model, |
| overwrite_output_dir=False, |
| past_index=-1, |
| per_device_eval_batch_size=32, |
| per_device_train_batch_size=32, |
| prediction_loss_only=False, |
| push_to_hub=False, |
| push_to_hub_model_id=None, |
| push_to_hub_organization=None, |
| push_to_hub_token=<PUSH_TO_HUB_TOKEN>, |
| ray_scope=last, |
| remove_unused_columns=True, |
| report_to=['tensorboard', 'codecarbon'], |
| resume_from_checkpoint=None, |
| run_name=./data/ogt_protein_classifier/model, |
| save_on_each_node=False, |
| save_steps=6, |
| save_strategy=steps, |
| save_total_limit=None, |
| seed=42, |
| sharded_ddp=[], |
| skip_memory_metrics=True, |
| tf32=None, |
| torch_compile=False, |
| torch_compile_backend=None, |
| torch_compile_mode=None, |
| torchdynamo=None, |
| tpu_metrics_debug=False, |
| tpu_num_cores=None, |
| use_ipex=False, |
| use_legacy_prediction_loop=False, |
| use_mps_device=False, |
| warmup_ratio=0.0, |
| warmup_steps=0, |
| weight_decay=0.0, |
| xpu_backend=None, |
| ) |
| ``` |
| |
| See the [training repository](https://github.com/BeckResearchLab/learn2thermML) for code. |
