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 | |
| 0,0,0.5841294191478976,0.6356599958161914,0.6537549682727843,0.6768356460497873,0.5841294191478976,0.21864753515077315,0.3924412523533924,0.3292335126018899,0.3050624084791856,0.3703159789792596,0.20323199219022384,0.4136028883099734,0.6142583894771726,0.4801559908551449,0.39577325822245696 | |