Source code for autorag.nodes.passagereranker.sentence_transformer
from typing import List, Tuple
import pandas as pd
import torch
from sentence_transformers import CrossEncoder
from tqdm import tqdm
from autorag.nodes.passagereranker.base import passage_reranker_node
from autorag.utils.util import flatten_apply, make_batch, select_top_k, sort_by_scores
[docs]
@passage_reranker_node
def sentence_transformer_reranker(
queries: List[str],
contents_list: List[List[str]],
scores_list: List[List[float]],
ids_list: List[List[str]],
top_k: int,
batch: int = 64,
sentence_transformer_max_length: int = 512,
model_name: str = "cross-encoder/ms-marco-MiniLM-L-2-v2",
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents based on their relevance to a query using Sentence Transformer model.
:param queries: The list of queries to use for reranking
:param contents_list: The list of lists of contents to rerank
:param scores_list: The list of lists of scores retrieved from the initial ranking
:param ids_list: The list of lists of ids retrieved from the initial ranking
:param top_k: The number of passages to be retrieved
:param batch: The number of queries to be processed in a batch
:param sentence_transformer_max_length: The maximum length of the input text for the Sentence Transformer model
:param model_name: The name of the Sentence Transformer model to use for reranking
Default is "cross-encoder/ms-marco-MiniLM-L-2-v2"
:return: tuple of lists containing the reranked contents, ids, and scores
"""
device = "cuda" if torch.cuda.is_available() else "cpu"
model = CrossEncoder(
model_name, max_length=sentence_transformer_max_length, device=device
)
nested_list = [
list(map(lambda x: [query, x], content_list))
for query, content_list in zip(queries, contents_list)
]
rerank_scores = flatten_apply(
sentence_transformer_run_model, nested_list, model=model, batch_size=batch
)
df = pd.DataFrame(
{
"contents": contents_list,
"ids": ids_list,
"scores": rerank_scores,
}
)
df[["contents", "ids", "scores"]] = df.apply(
sort_by_scores, axis=1, result_type="expand"
)
results = select_top_k(df, ["contents", "ids", "scores"], top_k)
del model
if torch.cuda.is_available():
torch.cuda.empty_cache()
return (
results["contents"].tolist(),
results["ids"].tolist(),
results["scores"].tolist(),
)
[docs]
def sentence_transformer_run_model(input_texts, model, batch_size: int):
batch_input_texts = make_batch(input_texts, batch_size)
results = []
for batch_texts in tqdm(batch_input_texts):
with torch.no_grad():
pred_scores = model.predict(sentences=batch_texts, apply_softmax=True)
results.extend(pred_scores.tolist())
return results