Text Classification
Transformers
Safetensors
PyTorch
English
bert
biomedical
text-embeddings-inference
Instructions to use STRIDE-lab/pubmedbert-setting-20250221 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STRIDE-lab/pubmedbert-setting-20250221 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="STRIDE-lab/pubmedbert-setting-20250221")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("STRIDE-lab/pubmedbert-setting-20250221") model = AutoModelForSequenceClassification.from_pretrained("STRIDE-lab/pubmedbert-setting-20250221", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download params.json from STRIDE-lab/pubmedbert-setting-20250221: direct link, hf CLI and curl.
- Browser
- Download file 416 Bytes
-
https://huggingface.co/STRIDE-lab/pubmedbert-setting-20250221/resolve/main/params.json
- Command line
-
hf download hf://STRIDE-lab/pubmedbert-setting-20250221/params.json
-
curl -L -o params.json https://huggingface.co/STRIDE-lab/pubmedbert-setting-20250221/resolve/main/params.json
416 Bytes
| { | |
| "mode": "train", | |
| "data": "<private_dataset>", | |
| "task": "Setting", | |
| "model": "pubmedbert", | |
| "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 | |
| } |