Text Classification
Transformers
Safetensors
deberta-v2
citation-function-classification
scholarly-positioning
related-work-generation
rwgbench
multicite
text-embeddings-inference
Instructions to use BFTree/rwgbench-citation-frame-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BFTree/rwgbench-citation-frame-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BFTree/rwgbench-citation-frame-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BFTree/rwgbench-citation-frame-classifier") model = AutoModelForSequenceClassification.from_pretrained("BFTree/rwgbench-citation-frame-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "motivation": 0.6339556574821472, | |
| "background": 0.45784226059913635, | |
| "uses": 0.3992602229118347, | |
| "extends": 0.8947582244873047, | |
| "similarities": 0.7605156898498535, | |
| "differences": 0.8103693723678589, | |
| "future_work": 0.873489499092102 | |
| } |