from itertools import chain
from typing import List, Tuple
import pandas as pd
import torch
import torch.nn.functional as F
from tqdm import tqdm
from autorag.nodes.passagereranker.base import passage_reranker_node
from autorag.nodes.passagereranker.tart.modeling_enc_t5 import (
EncT5ForSequenceClassification,
)
from autorag.nodes.passagereranker.tart.tokenization_enc_t5 import EncT5Tokenizer
from autorag.utils.util import make_batch, sort_by_scores, flatten_apply, select_top_k
[docs]
@passage_reranker_node
def tart(
queries: List[str],
contents_list: List[List[str]],
scores_list: List[List[float]],
ids_list: List[List[str]],
top_k: int,
instruction: str = "Find passage to answer given question",
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 Tart.
TART is a reranker based on TART (https://github.com/facebookresearch/tart).
You can rerank the passages with the instruction using TARTReranker.
The default model is facebook/tart-full-flan-t5-xl.
: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 instruction: The instruction for reranking.
Note: default instruction is "Find passage to answer given question"
The default instruction from the TART paper is being used.
If you want to use a different instruction, you can change the instruction through this parameter
:param batch: The number of queries to be processed in a batch
:return: tuple of lists containing the reranked contents, ids, and scores
"""
model_name = "facebook/tart-full-flan-t5-xl"
model = EncT5ForSequenceClassification.from_pretrained(model_name)
tokenizer = EncT5Tokenizer.from_pretrained(model_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
nested_list = [
[["{} [SEP] {}".format(instruction, query)] for _ in contents]
for query, contents in zip(queries, contents_list)
]
rerank_scores = flatten_apply(
tart_run_model,
nested_list,
model=model,
batch_size=batch,
tokenizer=tokenizer,
device=device,
contents_list=contents_list,
)
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
del tokenizer
if torch.cuda.is_available():
torch.cuda.empty_cache()
return (
results["contents"].tolist(),
results["ids"].tolist(),
results["scores"].tolist(),
)
[docs]
def tart_run_model(
input_texts, contents_list, model, batch_size: int, tokenizer, device
):
flattened_texts = list(chain.from_iterable(input_texts))
flattened_contents = list(chain.from_iterable(contents_list))
batch_input_texts = make_batch(flattened_texts, batch_size)
batch_contents_list = make_batch(flattened_contents, batch_size)
results = []
for batch_texts, batch_contents in tqdm(
zip(batch_input_texts, batch_contents_list)
):
feature = tokenizer(
batch_texts,
batch_contents,
padding=True,
truncation=True,
return_tensors="pt",
).to(device)
with torch.no_grad():
pred_scores = model(**feature).logits
normalized_scores = [
float(score[1]) for score in F.softmax(pred_scores, dim=1)
]
results.extend(normalized_scores)
return results