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 tokenizer.json from srikarvar/fine_tuned_model_11: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/srikarvar/fine_tuned_model_11/resolve/main/tokenizer.json
- Command line
-
hf download hf://srikarvar/fine_tuned_model_11/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/srikarvar/fine_tuned_model_11/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 6803d80dfd27b26211a967d9e4a230d7caffa8564ff4adb8f96610825c9b6c92
- Size of remote file:
- 17.1 MB
- SHA256:
- ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
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