Sentence Similarity
sentence-transformers
Safetensors
Japanese
luke
linktransformer
tabular-classification
Instructions to use dell-research-harvard/lt-wikidata-comp-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dell-research-harvard/lt-wikidata-comp-ja with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dell-research-harvard/lt-wikidata-comp-ja") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Updated model with better training and evaluation. Test and val data included as pickle files. Older Legacy files were removed to avoid confusion.
034082e | epoch,steps,cos_sim-Accuracy@1,cos_sim-Accuracy@3,cos_sim-Accuracy@5,cos_sim-Accuracy@10,cos_sim-Precision@1,cos_sim-Recall@1,cos_sim-Precision@3,cos_sim-Recall@3,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-MRR@10,cos_sim-NDCG@10,cos_sim-MAP@100 | |
| 0,0,0.47911547911547914,0.5572481572481572,0.5886977886977887,0.62997542997543,0.47911547911547914,0.3180831480831481,0.2509418509418509,0.4286396786396786,0.16943488943488946,0.4653344253344253,0.0952825552825553,0.510026715026715,0.5265397215397216,0.47095023529894586,0.43073521049634894 | |