| from tqdm import tqdm | |
| from sentence_transformers import SentenceTransformer | |
| from cde_benchmark.embedders.base_embedder import Embedder | |
| class NaiveContextualEmbedder(Embedder): | |
| def __init__( | |
| self, | |
| model: SentenceTransformer = None, | |
| batch_size: int = 16, | |
| show_progress_bar: bool = True, | |
| ): | |
| super().__init__(is_contextual_model=True) | |
| self.model: SentenceTransformer = model | |
| self.show_progress_bar = show_progress_bar | |
| self.batch_size = batch_size | |
| def embed_queries(self, queries): | |
| return self.model.encode( | |
| queries, | |
| show_progress_bar=self.show_progress_bar, | |
| batch_size=self.batch_size, | |
| ) | |
| def embed_documents(self, documents): | |
| # documents is a list of list of documents | |
| # This is just for the demo, but here it's not contextual at all | |
| embeddings = [] | |
| for document in tqdm(documents): | |
| embeddings.append( | |
| self.model.encode( | |
| document, | |
| show_progress_bar=False, | |
| batch_size=self.batch_size, | |
| ) | |
| ) | |
| return embeddings | |