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].
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)
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