--- 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]]) ```