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
File size: 462 Bytes
f2ed788 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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
} |