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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
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