Labib11/PMC_bge_800

1---2library_name: sentence-transformers3pipeline_tag: sentence-similarity4tags:5- sentence-transformers6- feature-extraction7- sentence-similarity8 9---10 11# {MODEL_NAME}12 13This 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.14 15<!--- Describe your model here -->16 17## Usage (Sentence-Transformers)18 19Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:20 21```22pip install -U sentence-transformers23```24 25Then you can use the model like this:26 27```python28from sentence_transformers import SentenceTransformer29sentences = ["This is an example sentence", "Each sentence is converted"]30 31model = SentenceTransformer('{MODEL_NAME}')32embeddings = model.encode(sentences)33print(embeddings)34```35 36 37 38## Evaluation Results39 40<!--- Describe how your model was evaluated -->41 42For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})43 44 45 46## Full Model Architecture47```48SentenceTransformer(49  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 50  (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})51  (2): Normalize()52)53```54 55## Citing & Authors56 57<!--- Describe where people can find more information -->