Instructions to use STRIDE-lab/biobert-data-type-20250221 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STRIDE-lab/biobert-data-type-20250221 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="STRIDE-lab/biobert-data-type-20250221")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("STRIDE-lab/biobert-data-type-20250221") model = AutoModelForSequenceClassification.from_pretrained("STRIDE-lab/biobert-data-type-20250221") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| base_model: dmis-lab/biobert-v1.1 | |
| tags: | |
| - transformers | |
| - pytorch | |
| - biomedical | |
| - text-classification | |
| license: apache-2.0 | |
| language: | |
| - en | |
| pipeline_tag: token-classification | |
| # biobert_data_type_20250221 | |
| Fine-tuned model from the PsyNamic project. | |
| ## Base Model | |
| `dmis-lab/biobert-v1.1` | |
| ## Task | |
| Data Type | |
| ## Training Parameters | |
| ```json | |
| { | |
| "mode": "train", | |
| "data": "<private_dataset>", | |
| "task": "Data Type", | |
| "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": false | |
| } | |
| ``` |