Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:504
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use njw9722/tech-tuned-mxbai-embed-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use njw9722/tech-tuned-mxbai-embed-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("njw9722/tech-tuned-mxbai-embed-large") sentences = [ "rust", "OpenShift", "React Native", "Rust" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from njw9722/tech-tuned-mxbai-embed-large: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/njw9722/tech-tuned-mxbai-embed-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://njw9722/tech-tuned-mxbai-embed-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/njw9722/tech-tuned-mxbai-embed-large/resolve/main/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.