Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
English
bert
feature-extraction
text-embeddings-inference
Instructions to use srsawant34/ProTopic-niter1-bs64-e32-ntopics10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srsawant34/ProTopic-niter1-bs64-e32-ntopics10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srsawant34/ProTopic-niter1-bs64-e32-ntopics10") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- 1_Pooling
- checkpoint-6027
- checkpoint-6314
- checkpoint-6601
- checkpoint-6888
- checkpoint-7175
- checkpoint-7462
- checkpoint-7749
- checkpoint-8036
- checkpoint-8323
- checkpoint-8610
- checkpoint-8897
- checkpoint-9184
- runs
- 1.52 kB
- 10.6 kB
- 720 Bytes
- 116 Bytes
- 90.9 MB xet
- 349 Bytes
- 53 Bytes
- 695 Bytes
- 712 kB
- 1.43 kB
- 232 kB