Text Classification
Transformers
Safetensors
PyTorch
English
bert
biomedical
text-embeddings-inference
Instructions to use STRIDE-lab/biobert-study-purpose-20250221 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STRIDE-lab/biobert-study-purpose-20250221 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="STRIDE-lab/biobert-study-purpose-20250221")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("STRIDE-lab/biobert-study-purpose-20250221") model = AutoModelForSequenceClassification.from_pretrained("STRIDE-lab/biobert-study-purpose-20250221") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
base_model: dmis-lab/biobert-v1.1
tags:
- transformers
- pytorch
- biomedical
- text-classification
license: apache-2.0
language:
- en
pipeline_tag: text-classification
biobert_study_purpose_20250221
Fine-tuned model from the PsyNamic project.
Base Model
dmis-lab/biobert-v1.1
Task
Study Purpose
Training Parameters
{
"mode": "train",
"data": "<private_dataset>",
"task": "Study Purpose",
"model": "biobert",
"cross_val": false,
"batch_size": 8,
"learning_rate": 5e-05,
"weight_decay": 0.01,
"lr_scheduler": "linear",
"warmup_ratio": 0.1,
"epochs": 30,
"dropout": 0.1,
"early_stopping_patience": 5,
"gradient_clipping": 0.1,
"device": "cuda",
"load": null,
"max_length": 512,
"is_multilabel": true
}