Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1936
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use srikarvar/fine_tuned_model_11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srikarvar/fine_tuned_model_11 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srikarvar/fine_tuned_model_11") sentences = [ "What are the symptoms of COVID-19?", "How to identify COVID-19?", "What is the process for booking a dinner table?", "It is not necessary to include specific fields in a financial report; nevertheless, it is beneficial to add pertinent financial metrics to help investors gauge the company's condition." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from srikarvar/fine_tuned_model_11: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/srikarvar/fine_tuned_model_11/resolve/main/modules.json
- Command line
-
hf download hf://srikarvar/fine_tuned_model_11/modules.json
-
curl -L -o modules.json https://huggingface.co/srikarvar/fine_tuned_model_11/resolve/main/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |