| pipeline_tag: sentence-similarity | |
| tags: | |
| - sentence-transformers | |
| - feature-extraction | |
| - sentence-similarity | |
| # {MODEL_NAME} | |
| This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a None dimensional dense vector space and can be used for tasks like clustering or semantic search. | |
| <!--- Describe your model here --> | |
| ## Usage (Sentence-Transformers) | |
| Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: | |
| ``` | |
| pip install -U sentence-transformers | |
| ``` | |
| Then you can use the model like this: | |
| ```python | |
| from sentence_transformers import SentenceTransformer | |
| sentences = ["This is an example sentence", "Each sentence is converted"] | |
| model = SentenceTransformer('{MODEL_NAME}') | |
| embeddings = model.encode(sentences) | |
| print(embeddings) | |
| ``` | |
| ## Evaluation Results | |
| <!--- Describe how your model was evaluated --> | |
| For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) | |
| ## Full Model Architecture | |
| ``` | |
| SentenceTransformer( | |
| (0): CLIPModel() | |
| ) | |
| ``` | |
| ## Citing & Authors | |
| <!--- Describe where people can find more information --> |