Text Classification
Transformers
Safetensors
deberta-v2
citation-function-classification
scholarly-positioning
related-work-generation
rwgbench
multicite
text-embeddings-inference
Instructions to use Anonymous2876/rwgbench-citation-frame-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anonymous2876/rwgbench-citation-frame-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Anonymous2876/rwgbench-citation-frame-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Anonymous2876/rwgbench-citation-frame-classifier") model = AutoModelForSequenceClassification.from_pretrained("Anonymous2876/rwgbench-citation-frame-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "labels": [ | |
| "motivation", | |
| "background", | |
| "uses", | |
| "extends", | |
| "similarities", | |
| "differences", | |
| "future_work" | |
| ], | |
| "id2label": { | |
| "0": "motivation", | |
| "1": "background", | |
| "2": "uses", | |
| "3": "extends", | |
| "4": "similarities", | |
| "5": "differences", | |
| "6": "future_work" | |
| }, | |
| "label2id": { | |
| "motivation": 0, | |
| "background": 1, | |
| "uses": 2, | |
| "extends": 3, | |
| "similarities": 4, | |
| "differences": 5, | |
| "future_work": 6 | |
| } | |
| } |