初始化项目，由ModelHub XC社区提供模型
Model: ByteDance/ListConRanker Source: Original Platform
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ModelHub XC
325
modules/Reranking_loop.py
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325
modules/Reranking_loop.py
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# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy of this software
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# and associated documentation files (the “Software”), to deal in the Software without
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# restriction, including without limitation the rights to use, copy, modify, merge, publish,
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# distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all copies or
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# substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
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# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
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# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
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# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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# OTHER DEALINGS IN THE SOFTWARE.
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import logging
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import numpy as np
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from mteb import RerankingEvaluator, AbsTaskReranking
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from tqdm import tqdm
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import math
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logger = logging.getLogger(__name__)
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class ChineseRerankingEvaluator(RerankingEvaluator):
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"""
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This class evaluates a SentenceTransformer model for the task of re-ranking.
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Given a query and a list of documents, it computes the score [query, doc_i] for all possible
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documents and sorts them in decreasing order. Then, MRR@10 and MAP is compute to measure the quality of the ranking.
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:param samples: Must be a list and each element is of the form:
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- {'query': '', 'positive': [], 'negative': []}. Query is the search query, positive is a list of positive
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(relevant) documents, negative is a list of negative (irrelevant) documents.
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- {'query': [], 'positive': [], 'negative': []}. Where query is a list of strings, which embeddings we average
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to get the query embedding.
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"""
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def __call__(self, model):
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scores = self.compute_metrics(model)
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return scores
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def compute_metrics(self, model):
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return (
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self.compute_metrics_batched(model)
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if self.use_batched_encoding
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else self.compute_metrics_individual(model)
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)
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def compute_metrics_batched(self, model):
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"""
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Computes the metrices in a batched way, by batching all queries and
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all documents together
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"""
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if hasattr(model, 'compute_score'):
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return self.compute_metrics_batched_from_crossencoder(model)
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else:
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return self.compute_metrics_batched_from_biencoder(model)
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def compute_metrics_batched_from_crossencoder(self, model):
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all_ap_scores = []
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all_mrr_1_scores = []
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all_mrr_5_scores = []
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all_mrr_10_scores = []
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for sample in tqdm(self.samples, desc="Evaluating"):
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query = sample['query']
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pos = sample['positive']
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neg = sample['negative']
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passage = pos + neg
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passage2label = {}
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for p in pos:
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passage2label[p] = True
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for p in neg:
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passage2label[p] = False
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filter_times = 0
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passage2score = {}
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while len(passage) > 20:
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batch = [[query] + passage]
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pred_scores = model.compute_score(batch)[0]
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# Sort in increasing order
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pred_scores_argsort = np.argsort(pred_scores).tolist()
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passage_len = len(passage)
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to_filter_num = math.ceil(passage_len * 0.2)
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if to_filter_num < 10:
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to_filter_num = 10
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have_filter_num = 0
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while have_filter_num < to_filter_num:
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idx = pred_scores_argsort[have_filter_num]
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if passage[idx] in passage2score:
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passage2score[passage[idx]].append(pred_scores[idx] + filter_times)
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else:
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passage2score[passage[idx]] = [pred_scores[idx] + filter_times]
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have_filter_num += 1
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while pred_scores[pred_scores_argsort[have_filter_num - 1]] == pred_scores[pred_scores_argsort[have_filter_num]]:
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idx = pred_scores_argsort[have_filter_num]
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if passage[idx] in passage2score:
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passage2score[passage[idx]].append(pred_scores[idx] + filter_times)
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else:
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passage2score[passage[idx]] = [pred_scores[idx] + filter_times]
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have_filter_num += 1
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next_passage = []
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next_passage_idx = have_filter_num
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while next_passage_idx < len(passage):
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idx = pred_scores_argsort[next_passage_idx]
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next_passage.append(passage[idx])
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next_passage_idx += 1
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passage = next_passage
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filter_times += 1
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batch = [[query] + passage]
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pred_scores = model.compute_score(batch)[0]
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cnt = 0
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while cnt < len(passage):
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if passage[cnt] in passage2score:
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passage2score[passage[cnt]].append(pred_scores[cnt] + filter_times)
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else:
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passage2score[passage[cnt]] = [pred_scores[cnt] + filter_times]
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cnt += 1
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passage = list(set(pos + neg))
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is_relevant = []
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final_score = []
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for i in range(len(passage)):
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p = passage[i]
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is_relevant += [passage2label[p]] * len(passage2score[p])
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final_score += passage2score[p]
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ap = self.ap_score(is_relevant, final_score)
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pred_scores_argsort = np.argsort(-(np.array(final_score)))
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mrr_1 = self.mrr_at_k_score(is_relevant, pred_scores_argsort, 1)
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mrr_5 = self.mrr_at_k_score(is_relevant, pred_scores_argsort, 5)
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mrr_10 = self.mrr_at_k_score(is_relevant, pred_scores_argsort, 10)
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all_ap_scores.append(ap)
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all_mrr_1_scores.append(mrr_1)
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all_mrr_5_scores.append(mrr_5)
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all_mrr_10_scores.append(mrr_10)
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mean_ap = np.mean(all_ap_scores)
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mean_mrr_1 = np.mean(all_mrr_1_scores)
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mean_mrr_5 = np.mean(all_mrr_5_scores)
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mean_mrr_10 = np.mean(all_mrr_10_scores)
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return {"map": mean_ap, "mrr_1": mean_mrr_1, 'mrr_5': mean_mrr_5, 'mrr_10': mean_mrr_10}
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def compute_metrics_batched_from_biencoder(self, model):
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all_mrr_scores = []
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all_ap_scores = []
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logger.info("Encoding queries...")
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if isinstance(self.samples[0]["query"], str):
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if hasattr(model, 'encode_queries'):
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all_query_embs = model.encode_queries(
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[sample["query"] for sample in self.samples],
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convert_to_tensor=True,
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batch_size=self.batch_size,
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)
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else:
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all_query_embs = model.encode(
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[sample["query"] for sample in self.samples],
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convert_to_tensor=True,
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batch_size=self.batch_size,
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)
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elif isinstance(self.samples[0]["query"], list):
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# In case the query is a list of strings, we get the most similar embedding to any of the queries
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all_query_flattened = [q for sample in self.samples for q in sample["query"]]
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if hasattr(model, 'encode_queries'):
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all_query_embs = model.encode_queries(all_query_flattened, convert_to_tensor=True,
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batch_size=self.batch_size)
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else:
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all_query_embs = model.encode(all_query_flattened, convert_to_tensor=True, batch_size=self.batch_size)
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else:
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raise ValueError(f"Query must be a string or a list of strings but is {type(self.samples[0]['query'])}")
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logger.info("Encoding candidates...")
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all_docs = []
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for sample in self.samples:
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all_docs.extend(sample["positive"])
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all_docs.extend(sample["negative"])
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all_docs_embs = model.encode(all_docs, convert_to_tensor=True, batch_size=self.batch_size)
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# Compute scores
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logger.info("Evaluating...")
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query_idx, docs_idx = 0, 0
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for instance in self.samples:
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num_subqueries = len(instance["query"]) if isinstance(instance["query"], list) else 1
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query_emb = all_query_embs[query_idx: query_idx + num_subqueries]
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query_idx += num_subqueries
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num_pos = len(instance["positive"])
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num_neg = len(instance["negative"])
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docs_emb = all_docs_embs[docs_idx: docs_idx + num_pos + num_neg]
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docs_idx += num_pos + num_neg
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if num_pos == 0 or num_neg == 0:
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continue
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is_relevant = [True] * num_pos + [False] * num_neg
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scores = self._compute_metrics_instance(query_emb, docs_emb, is_relevant)
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all_mrr_scores.append(scores["mrr"])
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all_ap_scores.append(scores["ap"])
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mean_ap = np.mean(all_ap_scores)
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mean_mrr = np.mean(all_mrr_scores)
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return {"map": mean_ap, "mrr": mean_mrr}
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def evaluate(self, model, split="test", **kwargs):
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if not self.data_loaded:
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self.load_data()
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data_split = self.dataset[split]
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evaluator = ChineseRerankingEvaluator(data_split, **kwargs)
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scores = evaluator(model)
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return dict(scores)
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AbsTaskReranking.evaluate = evaluate
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class T2Reranking(AbsTaskReranking):
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@property
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def description(self):
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return {
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'name': 'T2Reranking',
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'hf_hub_name': "C-MTEB/T2Reranking",
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'description': 'T2Ranking: A large-scale Chinese Benchmark for Passage Ranking',
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"reference": "https://arxiv.org/abs/2304.03679",
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'type': 'Reranking',
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'category': 's2p',
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'eval_splits': ['dev'],
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'eval_langs': ['zh'],
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'main_score': 'map',
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}
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class T2RerankingZh2En(AbsTaskReranking):
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@property
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def description(self):
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return {
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'name': 'T2RerankingZh2En',
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'hf_hub_name': "C-MTEB/T2Reranking_zh2en",
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'description': 'T2Ranking: A large-scale Chinese Benchmark for Passage Ranking',
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"reference": "https://arxiv.org/abs/2304.03679",
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'type': 'Reranking',
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'category': 's2p',
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'eval_splits': ['dev'],
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'eval_langs': ['zh2en'],
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'main_score': 'map',
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}
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class T2RerankingEn2Zh(AbsTaskReranking):
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@property
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def description(self):
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return {
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'name': 'T2RerankingEn2Zh',
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'hf_hub_name': "C-MTEB/T2Reranking_en2zh",
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'description': 'T2Ranking: A large-scale Chinese Benchmark for Passage Ranking',
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"reference": "https://arxiv.org/abs/2304.03679",
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'type': 'Reranking',
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'category': 's2p',
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'eval_splits': ['dev'],
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'eval_langs': ['en2zh'],
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'main_score': 'map',
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}
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class MMarcoReranking(AbsTaskReranking):
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@property
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def description(self):
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return {
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'name': 'MMarcoReranking',
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'hf_hub_name': "C-MTEB/Mmarco-reranking",
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'description': 'mMARCO is a multilingual version of the MS MARCO passage ranking dataset',
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"reference": "https://github.com/unicamp-dl/mMARCO",
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'type': 'Reranking',
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'category': 's2p',
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'eval_splits': ['dev'],
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'eval_langs': ['zh'],
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'main_score': 'map',
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}
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class CMedQAv1(AbsTaskReranking):
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@property
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def description(self):
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return {
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'name': 'CMedQAv1',
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"hf_hub_name": "C-MTEB/CMedQAv1-reranking",
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'description': 'Chinese community medical question answering',
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"reference": "https://github.com/zhangsheng93/cMedQA",
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'type': 'Reranking',
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'category': 's2p',
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'eval_splits': ['test'],
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'eval_langs': ['zh'],
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'main_score': 'map',
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}
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class CMedQAv2(AbsTaskReranking):
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@property
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def description(self):
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return {
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'name': 'CMedQAv2',
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"hf_hub_name": "C-MTEB/CMedQAv2-reranking",
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'description': 'Chinese community medical question answering',
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"reference": "https://github.com/zhangsheng93/cMedQA2",
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'type': 'Reranking',
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'category': 's2p',
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'eval_splits': ['test'],
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'eval_langs': ['zh'],
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'main_score': 'map',
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}
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