from typing import List, Tuple
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from autorag.nodes.passagereranker.base import passage_reranker_node
from autorag.utils.util import make_batch, sort_by_scores, flatten_apply, select_top_k
[docs]
@passage_reranker_node
def koreranker(
queries: List[str],
contents_list: List[List[str]],
scores_list: List[List[float]],
ids_list: List[List[str]],
top_k: int,
batch: int = 64,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents based on their relevance to a query using ko-reranker.
ko-reranker is a reranker based on korean (https://huggingface.co/Dongjin-kr/ko-reranker).
: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
Default is 64.
:return: tuple of lists containing the reranked contents, ids, and scores
"""
model_path = "Dongjin-kr/ko-reranker"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model.eval()
# Determine the device to run the model on (GPU if available, otherwise CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
nested_list = [
list(map(lambda x: [query, x], content_list))
for query, content_list in zip(queries, contents_list)
]
scores_nps = flatten_apply(
koreranker_run_model,
nested_list,
model=model,
batch_size=batch,
tokenizer=tokenizer,
device=device,
)
rerank_scores = list(
map(lambda scores: exp_normalize(np.array(scores)).astype(float), scores_nps)
)
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 koreranker_run_model(input_texts, model, tokenizer, device, batch_size: int):
batch_input_texts = make_batch(input_texts, batch_size)
results = []
for batch_texts in tqdm(batch_input_texts):
inputs = tokenizer(
batch_texts,
padding=True,
truncation=True,
return_tensors="pt",
max_length=512,
)
inputs = inputs.to(device)
with torch.no_grad():
scores = (
model(**inputs, return_dict=True)
.logits.view(
-1,
)
.float()
)
scores_np = scores.cpu().numpy()
results.extend(scores_np)
return results
[docs]
def exp_normalize(x):
b = x.max()
y = np.exp(x - b)
return y / y.sum()