Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:560
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use srikarvar/fine_tuned_model_16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srikarvar/fine_tuned_model_16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srikarvar/fine_tuned_model_16") sentences = [ "The main objective of the System Logs documentation is to demonstrate how to utilize the 📋 Logs system to access and manipulate logs of any format or type.", "The purpose of the System Logs documentation is to provide information on how to use the 📋 Logs system to store and work with logs of any format or type.", "The main difference between a ProductList and an InventoryList is that a ProductList provides random access to the items, while an InventoryList updates progressively as you browse the list.", "The most recommended way to clean kitchen surfaces is with a microfiber cloth." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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