Sentence Similarity
sentence-transformers
Safetensors
English
llama
feature-extraction
Generated from Trainer
dataset_size:65749
loss:MultipleNegativesRankingLoss
loss:SoftmaxLoss
loss:CoSENTLoss
text-embeddings-inference
Instructions to use keeeeenw/MicroLlama-text-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use keeeeenw/MicroLlama-text-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("keeeeenw/MicroLlama-text-embedding") sentences = [ "A construction worker is standing on a crane placing a large arm on top of a stature in progress.", "The man is wearing black.", "A person standing", "Nobody is standing" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from keeeeenw/MicroLlama-text-embedding: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/keeeeenw/MicroLlama-text-embedding/resolve/main/tokenizer.model
- Command line
-
hf download hf://keeeeenw/MicroLlama-text-embedding/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/keeeeenw/MicroLlama-text-embedding/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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