Instructions to use STRIDE-lab/scibert-study-conclusion-20251005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STRIDE-lab/scibert-study-conclusion-20251005 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="STRIDE-lab/scibert-study-conclusion-20251005")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("STRIDE-lab/scibert-study-conclusion-20251005") model = AutoModelForSequenceClassification.from_pretrained("STRIDE-lab/scibert-study-conclusion-20251005") - Notebooks
- Google Colab
- Kaggle
| { | |
| "mode": "train", | |
| "data": "<private_dataset>", | |
| "task": "Study Conclusion", | |
| "model": "scibert", | |
| "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", | |
| "max_length": 512, | |
| "load": null, | |
| "outfile": null, | |
| "threshold": 0.5, | |
| "is_multilabel": false | |
| } |