Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:101762
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use palusi/sentest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use palusi/sentest with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("palusi/sentest") sentences = [ "Why am I still afraid of the dark?", "What one single change can change a life?", "Why do we have a dark side?", "Why are humans afraid of the dark?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 296 Bytes
a24e8ee | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 768,
"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
} |