Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use Revankumar/Ecommerce_assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Revankumar/Ecommerce_assistant with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Revankumar/Ecommerce_assistant") 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
| 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,dot_score-Accuracy@1,dot_score-Accuracy@3,dot_score-Accuracy@5,dot_score-Accuracy@10,dot_score-Precision@1,dot_score-Recall@1,dot_score-Precision@3,dot_score-Recall@3,dot_score-Precision@5,dot_score-Recall@5,dot_score-Precision@10,dot_score-Recall@10,dot_score-MRR@10,dot_score-NDCG@10,dot_score-MAP@100 | |
| 0,-1,0.75,1.0,1.0,1.0,0.75,0.75,0.3333333333333333,1.0,0.2,1.0,0.1,1.0,0.875,0.9077324383928644,0.875,0.75,1.0,1.0,1.0,0.75,0.75,0.3333333333333333,1.0,0.2,1.0,0.1,1.0,0.875,0.9077324383928644,0.875 | |
| 1,-1,0.75,1.0,1.0,1.0,0.75,0.75,0.3333333333333333,1.0,0.2,1.0,0.1,1.0,0.875,0.9077324383928644,0.875,0.75,1.0,1.0,1.0,0.75,0.75,0.3333333333333333,1.0,0.2,1.0,0.1,1.0,0.875,0.9077324383928644,0.875 | |