ListConRanker / modules /Reranking_loop.py
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# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
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# substantial portions of the Software.
#
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
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# OTHER DEALINGS IN THE SOFTWARE.
import logging
import numpy as np
from mteb import RerankingEvaluator, AbsTaskReranking
from tqdm import tqdm
import math
logger = logging.getLogger(__name__)
class ChineseRerankingEvaluator(RerankingEvaluator):
"""
This class evaluates a SentenceTransformer model for the task of re-ranking.
Given a query and a list of documents, it computes the score [query, doc_i] for all possible
documents and sorts them in decreasing order. Then, MRR@10 and MAP is compute to measure the quality of the ranking.
:param samples: Must be a list and each element is of the form:
- {'query': '', 'positive': [], 'negative': []}. Query is the search query, positive is a list of positive
(relevant) documents, negative is a list of negative (irrelevant) documents.
- {'query': [], 'positive': [], 'negative': []}. Where query is a list of strings, which embeddings we average
to get the query embedding.
"""
def __call__(self, model):
scores = self.compute_metrics(model)
return scores
def compute_metrics(self, model):
return (
self.compute_metrics_batched(model)
if self.use_batched_encoding
else self.compute_metrics_individual(model)
)
def compute_metrics_batched(self, model):
"""
Computes the metrices in a batched way, by batching all queries and
all documents together
"""
if hasattr(model, 'compute_score'):
return self.compute_metrics_batched_from_crossencoder(model)
else:
return self.compute_metrics_batched_from_biencoder(model)
def compute_metrics_batched_from_crossencoder(self, model):
all_ap_scores = []
all_mrr_1_scores = []
all_mrr_5_scores = []
all_mrr_10_scores = []
for sample in tqdm(self.samples, desc="Evaluating"):
query = sample['query']
pos = sample['positive']
neg = sample['negative']
passage = pos + neg
passage2label = {}
for p in pos:
passage2label[p] = True
for p in neg:
passage2label[p] = False
filter_times = 0
passage2score = {}
while len(passage) > 20:
batch = [[query] + passage]
pred_scores = model.compute_score(batch)[0]
# Sort in increasing order
pred_scores_argsort = np.argsort(pred_scores).tolist()
passage_len = len(passage)
to_filter_num = math.ceil(passage_len * 0.2)
if to_filter_num < 10:
to_filter_num = 10
have_filter_num = 0
while have_filter_num < to_filter_num:
idx = pred_scores_argsort[have_filter_num]
if passage[idx] in passage2score:
passage2score[passage[idx]].append(pred_scores[idx] + filter_times)
else:
passage2score[passage[idx]] = [pred_scores[idx] + filter_times]
have_filter_num += 1
while pred_scores[pred_scores_argsort[have_filter_num - 1]] == pred_scores[pred_scores_argsort[have_filter_num]]:
idx = pred_scores_argsort[have_filter_num]
if passage[idx] in passage2score:
passage2score[passage[idx]].append(pred_scores[idx] + filter_times)
else:
passage2score[passage[idx]] = [pred_scores[idx] + filter_times]
have_filter_num += 1
next_passage = []
next_passage_idx = have_filter_num
while next_passage_idx < len(passage):
idx = pred_scores_argsort[next_passage_idx]
next_passage.append(passage[idx])
next_passage_idx += 1
passage = next_passage
filter_times += 1
batch = [[query] + passage]
pred_scores = model.compute_score(batch)[0]
cnt = 0
while cnt < len(passage):
if passage[cnt] in passage2score:
passage2score[passage[cnt]].append(pred_scores[cnt] + filter_times)
else:
passage2score[passage[cnt]] = [pred_scores[cnt] + filter_times]
cnt += 1
passage = list(set(pos + neg))
is_relevant = []
final_score = []
for i in range(len(passage)):
p = passage[i]
is_relevant += [passage2label[p]] * len(passage2score[p])
final_score += passage2score[p]
ap = self.ap_score(is_relevant, final_score)
pred_scores_argsort = np.argsort(-(np.array(final_score)))
mrr_1 = self.mrr_at_k_score(is_relevant, pred_scores_argsort, 1)
mrr_5 = self.mrr_at_k_score(is_relevant, pred_scores_argsort, 5)
mrr_10 = self.mrr_at_k_score(is_relevant, pred_scores_argsort, 10)
all_ap_scores.append(ap)
all_mrr_1_scores.append(mrr_1)
all_mrr_5_scores.append(mrr_5)
all_mrr_10_scores.append(mrr_10)
mean_ap = np.mean(all_ap_scores)
mean_mrr_1 = np.mean(all_mrr_1_scores)
mean_mrr_5 = np.mean(all_mrr_5_scores)
mean_mrr_10 = np.mean(all_mrr_10_scores)
return {"map": mean_ap, "mrr_1": mean_mrr_1, 'mrr_5': mean_mrr_5, 'mrr_10': mean_mrr_10}
def compute_metrics_batched_from_biencoder(self, model):
all_mrr_scores = []
all_ap_scores = []
logger.info("Encoding queries...")
if isinstance(self.samples[0]["query"], str):
if hasattr(model, 'encode_queries'):
all_query_embs = model.encode_queries(
[sample["query"] for sample in self.samples],
convert_to_tensor=True,
batch_size=self.batch_size,
)
else:
all_query_embs = model.encode(
[sample["query"] for sample in self.samples],
convert_to_tensor=True,
batch_size=self.batch_size,
)
elif isinstance(self.samples[0]["query"], list):
# In case the query is a list of strings, we get the most similar embedding to any of the queries
all_query_flattened = [q for sample in self.samples for q in sample["query"]]
if hasattr(model, 'encode_queries'):
all_query_embs = model.encode_queries(all_query_flattened, convert_to_tensor=True,
batch_size=self.batch_size)
else:
all_query_embs = model.encode(all_query_flattened, convert_to_tensor=True, batch_size=self.batch_size)
else:
raise ValueError(f"Query must be a string or a list of strings but is {type(self.samples[0]['query'])}")
logger.info("Encoding candidates...")
all_docs = []
for sample in self.samples:
all_docs.extend(sample["positive"])
all_docs.extend(sample["negative"])
all_docs_embs = model.encode(all_docs, convert_to_tensor=True, batch_size=self.batch_size)
# Compute scores
logger.info("Evaluating...")
query_idx, docs_idx = 0, 0
for instance in self.samples:
num_subqueries = len(instance["query"]) if isinstance(instance["query"], list) else 1
query_emb = all_query_embs[query_idx: query_idx + num_subqueries]
query_idx += num_subqueries
num_pos = len(instance["positive"])
num_neg = len(instance["negative"])
docs_emb = all_docs_embs[docs_idx: docs_idx + num_pos + num_neg]
docs_idx += num_pos + num_neg
if num_pos == 0 or num_neg == 0:
continue
is_relevant = [True] * num_pos + [False] * num_neg
scores = self._compute_metrics_instance(query_emb, docs_emb, is_relevant)
all_mrr_scores.append(scores["mrr"])
all_ap_scores.append(scores["ap"])
mean_ap = np.mean(all_ap_scores)
mean_mrr = np.mean(all_mrr_scores)
return {"map": mean_ap, "mrr": mean_mrr}
def evaluate(self, model, split="test", **kwargs):
if not self.data_loaded:
self.load_data()
data_split = self.dataset[split]
evaluator = ChineseRerankingEvaluator(data_split, **kwargs)
scores = evaluator(model)
return dict(scores)
AbsTaskReranking.evaluate = evaluate
class T2Reranking(AbsTaskReranking):
@property
def description(self):
return {
'name': 'T2Reranking',
'hf_hub_name': "C-MTEB/T2Reranking",
'description': 'T2Ranking: A large-scale Chinese Benchmark for Passage Ranking',
"reference": "https://arxiv.org/abs/2304.03679",
'type': 'Reranking',
'category': 's2p',
'eval_splits': ['dev'],
'eval_langs': ['zh'],
'main_score': 'map',
}
class T2RerankingZh2En(AbsTaskReranking):
@property
def description(self):
return {
'name': 'T2RerankingZh2En',
'hf_hub_name': "C-MTEB/T2Reranking_zh2en",
'description': 'T2Ranking: A large-scale Chinese Benchmark for Passage Ranking',
"reference": "https://arxiv.org/abs/2304.03679",
'type': 'Reranking',
'category': 's2p',
'eval_splits': ['dev'],
'eval_langs': ['zh2en'],
'main_score': 'map',
}
class T2RerankingEn2Zh(AbsTaskReranking):
@property
def description(self):
return {
'name': 'T2RerankingEn2Zh',
'hf_hub_name': "C-MTEB/T2Reranking_en2zh",
'description': 'T2Ranking: A large-scale Chinese Benchmark for Passage Ranking',
"reference": "https://arxiv.org/abs/2304.03679",
'type': 'Reranking',
'category': 's2p',
'eval_splits': ['dev'],
'eval_langs': ['en2zh'],
'main_score': 'map',
}
class MMarcoReranking(AbsTaskReranking):
@property
def description(self):
return {
'name': 'MMarcoReranking',
'hf_hub_name': "C-MTEB/Mmarco-reranking",
'description': 'mMARCO is a multilingual version of the MS MARCO passage ranking dataset',
"reference": "https://github.com/unicamp-dl/mMARCO",
'type': 'Reranking',
'category': 's2p',
'eval_splits': ['dev'],
'eval_langs': ['zh'],
'main_score': 'map',
}
class CMedQAv1(AbsTaskReranking):
@property
def description(self):
return {
'name': 'CMedQAv1',
"hf_hub_name": "C-MTEB/CMedQAv1-reranking",
'description': 'Chinese community medical question answering',
"reference": "https://github.com/zhangsheng93/cMedQA",
'type': 'Reranking',
'category': 's2p',
'eval_splits': ['test'],
'eval_langs': ['zh'],
'main_score': 'map',
}
class CMedQAv2(AbsTaskReranking):
@property
def description(self):
return {
'name': 'CMedQAv2',
"hf_hub_name": "C-MTEB/CMedQAv2-reranking",
'description': 'Chinese community medical question answering',
"reference": "https://github.com/zhangsheng93/cMedQA2",
'type': 'Reranking',
'category': 's2p',
'eval_splits': ['test'],
'eval_langs': ['zh'],
'main_score': 'map',
}