| # Run Model this way: | |
| ```python | |
| from sentence_transformers import SentenceTransformer | |
| model = SentenceTransformer("ClovenDoug/small_128_all-MiniLM-L6-v2") | |
| sentence_one = "I like cats" | |
| embedding = model.encode(sentence_one) | |
| print(embedding) | |
| ``` | |
| # small_128_all-MiniLM-L6-v2 | |
| This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search. | |
| This gives it faster similarity comparison time although inference time will remain about the same. | |
| This model was made using knowledge distillation techniques on the original 384 dimensional all-MiniLM-L6-v2 model. | |
| The script for distilling this model into various sizes can be found here: | |
| https://github.com/dorenwick/sentence_encoder_distillation | |
| ## Usage (Sentence-Transformers) | |
| Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: | |