Instructions to use relbert/relbert-roberta-base-nce-conceptnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use relbert/relbert-roberta-base-nce-conceptnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="relbert/relbert-roberta-base-nce-conceptnet")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("relbert/relbert-roberta-base-nce-conceptnet") model = AutoModel.from_pretrained("relbert/relbert-roberta-base-nce-conceptnet") - Notebooks
- Google Colab
- Kaggle
| {"scan/test": 0.21225247524752475, "sat_full/test": 0.45187165775401067, "sat/test": 0.456973293768546, "u2/test": 0.44298245614035087, "u4/test": 0.42824074074074076, "google/test": 0.774, "bats/test": 0.5892162312395776, "t_rex_relational_similarity/test": 0.4808743169398907, "conceptnet_relational_similarity/test": 0.18036912751677853, "nell_relational_similarity/test": 0.6633333333333333, "scan/validation": 0.19101123595505617, "sat/validation": 0.40540540540540543, "u2/validation": 0.3333333333333333, "u4/validation": 0.5833333333333334, "google/validation": 0.86, "bats/validation": 0.6331658291457286, "semeval2012_relational_similarity/validation": 0.4936708860759494, "t_rex_relational_similarity/validation": 0.20766129032258066, "conceptnet_relational_similarity/validation": 0.1447841726618705, "nell_relational_similarity/validation": 0.545} |