Instructions to use nepp1d0/SingleBertSmilesTargetInteraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nepp1d0/SingleBertSmilesTargetInteraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nepp1d0/SingleBertSmilesTargetInteraction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nepp1d0/SingleBertSmilesTargetInteraction") model = AutoModelForSequenceClassification.from_pretrained("nepp1d0/SingleBertSmilesTargetInteraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 387 Bytes
42068d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | Prot_bert finetuned on GPCR_train dataset of Drug Target prediction
Trainig paramenters:
overwrite_output_dir=True,
evaluation_strategy="epoch",
learning_rate=1e-3,
weight_decay=0.001,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
push_to_hub=True,
fp16=True,
logging_steps=logging_steps,
save_strategy='epoch',
num_train_epochs=2 |