Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
bert
feature-extraction
test
text-embeddings-inference
Instructions to use 0neisall/tiny-embedding-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 0neisall/tiny-embedding-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("0neisall/tiny-embedding-test") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 749 Bytes
a225491 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ---
language:
- en
- zh
license: apache-2.0
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- test
---
# Tiny embedding test model
Randomly initialized 1-layer BERT with mean pooling and L2 normalization.
Outputs 16-dimensional embeddings. Maximum sequence length: 128 tokens.
**For upload/loading tests only. Not trained; no useful semantic quality.**
The tiny vocabulary maps most words to `[UNK]`.
```python
from sentence_transformers import SentenceTransformer
# Replace with your uploaded repository ID.
model = SentenceTransformer("YOUR_USERNAME/tiny-embedding-test")
embeddings = model.encode(["hello world", "你好世界"])
print(embeddings.shape) # (2, 16)
```
|