| --- |
| library_name: sentence-transformers |
| 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 1024 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): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
| (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
| |
| ## Citing & Authors |
| |
| <!--- Describe where people can find more information --> |
