Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2476
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use srikarvar/fine_tuned_model_15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srikarvar/fine_tuned_model_15 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srikarvar/fine_tuned_model_15") sentences = [ "Why do you want to be to president?", "Can you teach me how to cook?", "Recipe for baking cookies", "Would you want to be President?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from srikarvar/fine_tuned_model_15: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/srikarvar/fine_tuned_model_15/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://srikarvar/fine_tuned_model_15/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/srikarvar/fine_tuned_model_15/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |