README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- tiny
base_model: sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: pearson_cosine
      value: 0.6751697498221416
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7044137530273638
      name: Spearman Cosine
---

# Super small embedding model (only 4MB!)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tabularisai/Zip-1")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 32]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

print(similarities)
#tensor([[1.0000, 0.7272, 0.2864],
#        [0.7272, 1.0000, 0.2265],
#        [0.2864, 0.2265, 1.0000]])
```




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