Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
linktransformer
tabular-classification
text-embeddings-inference
Instructions to use dell-research-harvard/lt-wikidata-comp-multi 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-multi with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dell-research-harvard/lt-wikidata-comp-multi") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - 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.
129b41d | 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 | |
| 0,0,0.8269356740469016,0.8672867810821433,0.8830079916153544,0.8997772828507795,0.8269356740469016,0.33790638352711344,0.5961286519061967,0.514297215838513,0.48165858771125375,0.580945733817281,0.3425979300406131,0.6543484503814214,0.8507003450972069,0.7596109393644823,0.6960622509001786 | |