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---
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)
```

	---
	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)
	```