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
File size: 249 Bytes
075922d | 1 2 3 4 5 6 7 8 9 | {
"motivation": 0.6339556574821472,
"background": 0.45784226059913635,
"uses": 0.3992602229118347,
"extends": 0.8947582244873047,
"similarities": 0.7605156898498535,
"differences": 0.8103693723678589,
"future_work": 0.873489499092102
} |