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", "ไฝ ๅฅฝไธ็"])
print(embeddings.shape) # (2, 16)
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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]