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