288 lines
11 KiB
Python
288 lines
11 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
|
|
#
|
|
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software
|
|
# and associated documentation files (the “Software”), to deal in the Software without
|
|
# restriction, including without limitation the rights to use, copy, modify, merge, publish,
|
|
# distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the
|
|
# Software is furnished to do so, subject to the following conditions:
|
|
#
|
|
# The above copyright notice and this permission notice shall be included in all copies or
|
|
# substantial portions of the Software.
|
|
#
|
|
# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
# 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
|
|
# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
|
|
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
|
|
# OTHER DEALINGS IN THE SOFTWARE.
|
|
|
|
import logging
|
|
import numpy as np
|
|
from mteb import RerankingEvaluator, AbsTaskReranking
|
|
from tqdm import tqdm
|
|
|
|
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):
|
|
batch_size = 4
|
|
|
|
all_ap_scores = []
|
|
all_mrr_1_scores = []
|
|
all_mrr_5_scores = []
|
|
all_mrr_10_scores = []
|
|
|
|
all_scores = []
|
|
tmp_pairs = []
|
|
for sample in tqdm(self.samples, desc="Evaluating"):
|
|
b_pairs = [sample['query']]
|
|
for p in sample['positive']:
|
|
b_pairs.append(p)
|
|
for n in sample['negative']:
|
|
b_pairs.append(n)
|
|
tmp_pairs.append(b_pairs)
|
|
if len(tmp_pairs) == batch_size:
|
|
sample_scores = model.compute_score(tmp_pairs)
|
|
sample_scores = sum(sample_scores, [])
|
|
all_scores += sample_scores
|
|
tmp_pairs = []
|
|
if len(tmp_pairs) > 0:
|
|
sample_scores = model.compute_score(tmp_pairs)
|
|
sample_scores = sum(sample_scores, [])
|
|
all_scores += sample_scores
|
|
all_scores = np.array(all_scores)
|
|
|
|
start_inx = 0
|
|
for sample in tqdm(self.samples, desc="Evaluating"):
|
|
is_relevant = [True] * len(sample['positive']) + [False] * len(sample['negative'])
|
|
pred_scores = all_scores[start_inx:start_inx + len(is_relevant)]
|
|
start_inx += len(is_relevant)
|
|
pred_scores_argsort = np.argsort(-pred_scores) # Sort in decreasing order
|
|
|
|
ap = self.ap_score(is_relevant, pred_scores)
|
|
|
|
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_mrr_1_scores.append(mrr_1)
|
|
all_mrr_5_scores.append(mrr_5)
|
|
all_mrr_10_scores.append(mrr_10)
|
|
all_ap_scores.append(ap)
|
|
|
|
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',
|
|
}
|