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_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srikarvar/fine_tuned_model_13 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srikarvar/fine_tuned_model_13") sentences = [ "The next move is to acquire the dataset and delineate the divisions.", "The next step is to download the dataset and define the splits.", "The `batch_id` parameter is used to specify a batch specific to the recipe code. It is used to update the storage directory when the recipe instructions are modified.", "The Instructions guide is divided into sections such as Overview, Tutorials, How-to guides, Settings, Interface, Hardware, System repository, Conceptual guides, and Reference." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle