Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:100000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use SikioN/wikidata-e5-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SikioN/wikidata-e5-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SikioN/wikidata-e5-finetuned") sentences = [ "query: 1960: Jim Baxter member of sports team", "passage: Scotland national football team", "passage: Fulkerson Prize", "passage: 1922" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |