--- tags: - mteb - qihoo360 - 奇虎360 - RAG-reranking model-index: - name: 360Zhinao-1_8B-reranking results: - task: type: Reranking dataset: type: None name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 86.75017961853382 - type: mrr value: 89.15436507936508 - task: type: Reranking dataset: type: None name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 87.91572151930174 - type: mrr value: 89.98869047619048 - task: type: Reranking dataset: type: None name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 37.28779203409935 - type: mrr value: 36.23730158730159 - task: type: Reranking dataset: type: None name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 68.55153559405632 - type: mrr value: 79.62773774596725 license: apache-2.0 library_name: transformers ---
# MTEB Leaderboard Chinese Reranking Results We have validated the performance of our model on the [mteb-chinese-reranking leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Currently, the open-source models on this leaderboard are primarily bidirectional discriminative models (BERT-like models). The only unidirectional generative model (GPT-like model) is gte-Qwen1.5-7B-instruct, which has an average score of 66.38, ranking 25th, with less than ideal results. Our self-developed unidirectional generative model, zhinao_1-8b_reranking, achieved an average score of 70.13, currently ranking first overall and first among open-source models, opening up new possibilities for generative models to undertake discriminative tasks. | Model | T2Reranking | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:| | **360Zhinao-1_8B-reranking** | **68.55** | **37.29** | **86.75** | **87.92** | **70.13** | | piccolo-large-zh-v2 | 67.15 | 33.39 | 90.14 | 89.31 | 70 | | Baichuan-text-embedding | 67.85 | 34.3 | 88.46 | 88.06 | 69.67 | | stella-mrl-large-zh-v3.5-1792d | 66.43 | 28.85 | 89.18 | 89.33 | 68.45 | | PEG | 69.43 | 33.55 | 86.56 | 84.09 | 68.41 | | bge-reranker-base | 67.28 | 35.46 | 81.27 | 84.1 | 67.03 | | bge-reranker-large | 67.6 | 37.17 | 82.14 | 84.19 | 67.78 | # Requirements ```bash pip install -r requirements.txt ``` If your GPU supports fp16 or bf16 precision, we also recommend installing [flash-attention](https://github.com/Dao-AILab/flash-attention) (**now with support for flash attention 2**) to improve your runtime efficiency and reduce memory usage. (**flash-attention is optional and not required for running this project**) ```bash git clone https://github.com/Dao-AILab/flash-attention cd flash-attention && pip install . # The installation below is optional and might be slow. # pip install csrc/layer_norm # No need to install the following if the flash-attn version is above 2.1.1. # pip install csrc/rotary ``` # Model Introduction The zhinao_1-8b_reranking model utilizes the self-developed zhinao_1-8b_base model as its foundation. Through iterative discovery and resolution of the following technical issues, it continuously stimulates the world knowledge inherent in the large model during the pre-training phase, better bridging the gap between generative models and discriminative tasks. ## Data Processing The model training did not utilize world knowledge, meaning it neither continued pre-training with domain-specific data nor fine-tuned datasets outside of the four datasets on the leaderboard. It only used the four datasets within the leaderboard, carefully iterating through data perception, and targeting different datasets for data cleaning and mining to ensure that the ranking in individual tasks could reach the top three level. ## Resolving Task Conflicts When merging four tasks, due to different data domain distributions, answer patterns, training data volumes, convergence steps, and even sequence lengths, conflicts exist between different tasks. Deeply resolving these conflict issues is crucial to obtaining a universal model with the best comprehensive indicators across different tasks. ## Resolving Training Instability Unlike generative tasks that produce multiple characters, using generative models for discriminative tasks requires the model to output a continuous value. Therefore, there is an oscillation problem during the training process. Deeply analyzing and resolving training instability can result in a model with better generalization and robustness. # Inference Script You can copy the following scripts to [mteb-eval-scripts](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB), then replace FlagReranker with FlagRerankerCustom in [eval_cross_encoder](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/eval_cross_encoder.py) scripts, then run [eval_cross_encoder](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/eval_cross_encoder.py) to reproduce our complete performance on the [mteb-chinese-reranking leaderboard](https://huggingface.co/spaces/mteb/leaderboard). ```python from typing import cast, List, Union, Tuple, Dict, Optional import numpy as np import torch from tqdm import tqdm from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification import transformers from transformers.trainer_pt_utils import LabelSmoother IGNORE_TOKEN_ID = LabelSmoother.ignore_index def preprocess( sources, tokenizer: transformers.PreTrainedTokenizer, max_len: int = 1024, system_message: str = "", device = None, ) -> Dict: roles = {"user": "<|im_start|>user", "assistant": "<|im_start|>assistant"} answer_len = 64 im_start = tokenizer.im_start_id im_end = tokenizer.im_end_id nl_tokens = tokenizer('\n').input_ids _system = tokenizer('system').input_ids + nl_tokens _user = tokenizer('user').input_ids + nl_tokens _assistant = tokenizer('assistant').input_ids + nl_tokens # Apply prompt templates input_ids, targets = [], [] for i, source in enumerate(sources): ## system_message input_id, target = [], [] system = [im_start] + _system + tokenizer(system_message, max_length=max_len-answer_len, truncation=True).input_ids + [im_end] + nl_tokens input_id += system target += [im_start] + [IGNORE_TOKEN_ID] * (len(system)-3) + [im_end] + nl_tokens assert len(input_id) == len(target) ## query ans source = "\n\n".join(source) role = "<|im_start|>user" _input_id = tokenizer(role, max_length=max_len-answer_len, truncation=True).input_ids + nl_tokens + \ tokenizer(source, max_length=max_len-answer_len, truncation=True).input_ids + [im_end] + nl_tokens input_id += _input_id if role == '<|im_start|>user': _target = [im_start] + [IGNORE_TOKEN_ID] * (len(_input_id)-3) + [im_end] + nl_tokens elif role == '<|im_start|>assistant': _target = [im_start] + [IGNORE_TOKEN_ID] * len(tokenizer(role, max_length=max_len-answer_len, truncation=True).input_ids) + \ _input_id[len(tokenizer(role, max_length=max_len-answer_len, truncation=True).input_ids)+1:-2] + [im_end] + nl_tokens else: raise NotImplementedError target += _target ## label use placeholder 0; It will be masked later in the modeling_zhinao.py role = "<|im_start|>assistant" _input_id = tokenizer(role, max_length=max_len-answer_len, truncation=True).input_ids + nl_tokens + \ tokenizer("0", max_length=max_len-answer_len, truncation=True).input_ids + [im_end] + nl_tokens input_id += _input_id if role == '<|im_start|>user': _target = [im_start] + [IGNORE_TOKEN_ID] * (len(_input_id)-3) + [im_end] + nl_tokens elif role == '<|im_start|>assistant': _target = [im_start] + [IGNORE_TOKEN_ID] * len(tokenizer(role, max_length=max_len-answer_len, truncation=True).input_ids) + \ _input_id[len(tokenizer(role, max_length=max_len-answer_len, truncation=True).input_ids)+1:-2] + [im_end] + nl_tokens else: raise NotImplementedError target += _target assert len(input_id) == len(target) input_id += [tokenizer.pad_token_id] * (max_len - len(input_id)) target += [IGNORE_TOKEN_ID] * (max_len - len(target)) if len(input_id) > max_len: print("max_len_error") print(tokenizer.decode(input_id)) input_ids.append(input_id[:max_len]) targets.append(target[:max_len]) input_ids = torch.tensor(input_ids, dtype=torch.int) targets = torch.tensor(targets, dtype=torch.int) #print(f"input_ids {input_ids.shape}") #print(f"targets {targets.shape}") return dict( input_ids=input_ids.to(device), labels=targets.to(device), attention_mask=input_ids.ne(tokenizer.pad_token_id).to(device), ) class FlagRerankerCustom: def __init__( self, model_name_or_path: str = None, use_fp16: bool = False ) -> None: self.tokenizer = transformers.AutoTokenizer.from_pretrained( model_name_or_path, model_max_length=1024, padding_side="right", use_fast=False, trust_remote_code=True ) self.tokenizer.pad_token_id = self.tokenizer.eod_id config = transformers.AutoConfig.from_pretrained( model_name_or_path, trust_remote_code=True, bf16=True, ) config.use_cache = False self.model = transformers.AutoModelForCausalLM.from_pretrained( model_name_or_path, config=config, trust_remote_code=True, ) self.model.linear.bfloat16() if torch.cuda.is_available(): self.device = torch.device('cuda') elif torch.backends.mps.is_available(): self.device = torch.device('mps') else: self.device = torch.device('cpu') use_fp16 = False if use_fp16: self.model.half() self.model = self.model.to(self.device) self.model.eval() self.num_gpus = torch.cuda.device_count() if self.num_gpus > 1: print(f"----------using {self.num_gpus}*GPUs----------") self.model = torch.nn.DataParallel(self.model) @torch.no_grad() def compute_score(self, sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]], batch_size: int =128, max_length: int = 1024) -> List[float]: if self.num_gpus > 0: batch_size = batch_size * self.num_gpus assert isinstance(sentence_pairs, list) if isinstance(sentence_pairs[0], str): sentence_pairs = [sentence_pairs] all_scores = [] for start_index in tqdm(range(0, len(sentence_pairs), batch_size), desc="Compute Scores", disable=len(sentence_pairs) < 128): sentences_batch = sentence_pairs[start_index:start_index + batch_size] # [[q,ans],[q, ans]...] inputs = preprocess(sources=sentences_batch, tokenizer=self.tokenizer,max_len=1024,device=self.device) scores = self.model(**inputs, return_dict=True).logits.view(-1, ).float() all_scores.extend(scores.cpu().numpy().tolist()) if len(all_scores) == 1: return all_scores[0] return all_scores if __name__ == "__main__": model_name_or_path = "360Zhinao-1_8B-reranking" model = FlagRerankerCustom(model_name_or_path, use_fp16=False) inputs=[["What Color Is the Sky","Blue"], ["What Color Is the Sky","Pink"],] ret = model.compute_score(inputs) print(ret) ``` ## License The source code of this repository follows the open-source license Apache 2.0. 360​Zhinao open-source models support commercial use. If you wish to use these models or continue training them for commercial purposes, please contact us via email (g-zhinao-opensource@360.cn) to apply. For the specific license agreement, please see <<360 Zhinao Open-Source Model License>>.