New reranker model: release cross-encoder models BAAI/bge-reranker-base and BAAI/bge-reranker-large, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. update embedding model: release bge-*-v1.5 embedding...
dynamicmodel_dir = modelscope.snapshot_download(modelName, revision:"master");dynamicflagReranker = flagEmbedding.FlagReranker(model_dir, use_fp16:true);model = flagReranker;returnmodel;}}catch(Exception ex){throwex;}}else{returnmodel;}}} publicstaticdoubleRerank(List<string> list){using(GIL...
feat: add Jina integration in Python for Embedding and Reranker 7797f5d JoanFM force-pushed the feat-jina-integration branch from 9e73c13 to 7797f5d Compare July 3, 2024 19:20 JoanFM marked this pull request as ready for review July 3, 2024 19:21 AyushExel approved these changes Ju...
def __init__( self, num_embeddings, embedding_dim, padding_idx, freeze_embed=False, normalize_embed=False, normalize_decay_rate=0.99, ): super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx) nn.init.uniform_(self.weight, -0.1, 0.1) nn.init.constant_(self.weight[pa...
dynamicmodel_dir = modelscope.snapshot_download(modelName, revision:"master");dynamicflagReranker = flagEmbedding.FlagReranker(model_dir, use_fp16:true);model = flagReranker;returnmodel;}}catch(Exception ex){throwex;}}else{returnmodel;}}} ...
retriever = DensePassageRetriever(document_store=document_store, query_embedding_model="facebook/dpr-question_encoder-single-nq-base", passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base", ) document_store.update_embeddings(retriever) # Init Reader: Powerful but slower neural model # ...
dynamicmodel_dir = modelscope.snapshot_download(modelName, revision:"master");dynamicflagReranker = flagEmbedding.FlagReranker(model_dir, use_fp16:true);model = flagReranker;returnmodel;}}catch(Exception ex){throwex;}}else{returnmodel;}}} ...
Ranker: Neural network (e.g., BERT or RoBERTA) that re-ranks top-k retrieved documents. The Ranker is an optional component and uses a TextPairClassification model under the hood. This model calculates semantic similarity of each of the top-k retrieved documents with the query. ...