mindspore/model_zoo/research/nlp/tprr

• Contents
• Thinking Path Re-Ranker
• Model Architecture
• Dataset
• Features
• • Mixed Precision
• Environment Requirements
• Quick Start
• Script Description
• • Script and Sample Code
  • Script Parameters
  • Evaluation Process
  • • Evaluation
• Model Description
• • Performance
  • • Inference Performance
• Description of random situation
• ModelZoo Homepage

Contents

• Thinking Path Re-Ranker
• Model Architecture
• Dataset
• Features
  • Mixed Precision
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
    • Training
  • Evaluation Process
    • Evaluation
• Model Description
  • Performance
• Description of random situation
• ModelZoo Homepage

Thinking Path Re-Ranker

Thinking Path Re-Ranker(TPRR) was proposed in 2021 by Huawei Poisson Lab & Parallel Distributed Computing Lab. By incorporating the
retriever, reranker and reader modules, TPRR shows excellent performance on open-domain multi-hop question answering. Moreover, TPRR has won
the first place in the current HotpotQA official leaderboard. This is a example of evaluation of TPRR with HotPotQA dataset in MindSpore. More
importantly, this is the first open source version for TPRR.

Model Architecture

Specially, TPRR contains three main modules. The first is retriever, which generate document sequences of each hop iteratively. The second
is reranker for selecting the best path from candidate paths generated by retriever. The last one is reader for extracting answer spans.

Dataset

The retriever dataset consists of three parts:
Wikipedia data: the 2017 English Wikipedia dump version with bidirectional hyperlinks.
dev data: HotPotQA full wiki setting dev data with 7398 question-answer pairs.
dev tf-idf data: the candidates for each question in dev data which is originated from top-500 retrieved from 5M paragraphs of Wikipedia
through TF-IDF.
The dataset of re-ranker consists of two parts:
Wikipedia data: the 2017 English Wikipedia dump version.
dev data: HotPotQA full wiki setting dev data with 7398 question-answer pairs.

Features

Mixed Precision

To ultilize the strong computation power of Ascend chip, and accelerate the evaluation process, the mixed evaluation method is used. MindSpore
is able to cope with FP32 inputs and FP16 operators. In TPRR example, the model is set to FP16 mode for the matmul calculation part.

Environment Requirements

• Hardware (Ascend)
• Framework
  • MindSpore
• For more information, please check the resources below:
  • MindSpore Tutorials
  • MindSpore Python API

Quick Start

After installing MindSpore via the official website and Dataset is correctly generated, you can start training and evaluation as follows.

• running on Ascend

  # run evaluation example with HotPotQA dev dataset
  sh run_eval_ascend.sh
  sh run_eval_ascend_reranker_reader.sh
  

Script Description

Script and Sample Code

.
└─tprr
  ├─README.md
  ├─scripts
  | ├─run_eval_ascend.sh                      # Launch retriever evaluation in ascend
  | └─run_eval_ascend_reranker_reader         # Launch re-ranker and reader evaluation in ascend
  |
  ├─src
  | ├─build_reranker_data.py                  # build data for re-ranker from result of retriever
  | ├─config.py                               # Evaluation configurations for retriever
  | ├─hotpot_evaluate_v1.py                   # Hotpotqa evaluation script
  | ├─onehop.py                               # Onehop model of retriever
  | ├─onehop_bert.py                          # Onehop bert model of retriever
  | ├─process_data.py                         # Data preprocessing for retriever
  | ├─reader.py                               # Reader model
  | ├─reader_albert_xxlarge.py                # Albert-xxlarge module of reader model
  | ├─reader_downstream.py                    # Downstream module of reader model
  | ├─reader_eval.py                          # Reader evaluation script
  | ├─rerank_albert_xxlarge.py                # Albert-xxlarge module of re-ranker model
  | ├─rerank_and_reader_data_generator.py     # Data generator for re-ranker and reader
  | ├─rerank_and_reader_utils.py              # Utils for re-ranker and reader
  | ├─rerank_downstream.py                    # Downstream module of re-ranker model
  | ├─reranker.py                             # Re-ranker model
  | ├─reranker_eval.py                        # Re-ranker evaluation script
  | ├─twohop.py                               # Twohop model of retriever
  | ├─twohop_bert.py                          # Twohop bert model of retriever
  | └─utils.py                                # Utils for retriever
  |
  ├─retriever_eval.py                         # Evaluation net for retriever
  └─reranker_and_reader_eval.py               # Evaluation net for re-ranker and reader

Script Parameters

Parameters for retriever evaluation can be set in config.py.

• config for TPRR retriever

  "q_len": 64,                        # Max query length
  "d_len": 192,                       # Max doc length
  "s_len": 448,                       # Max sequence length
  "in_len": 768,                      # Input dim
  "out_len": 1,                       # Output dim
  "num_docs": 500,                    # Num of docs
  "topk": 8,                          # Top k
  "onehop_num": 8                     # Num of onehop doc as twohop neighbor
  

  config.py for more configuration.

Parameters for re-ranker and reader evaluation can be passed directly at execution time.

• parameters for TPRR re-ranker and reader

  "seq_len": 512,                     # sequence length
  "rerank_batch_size": 32,            # batch size for re-ranker evaluation
  "reader_batch_size": 448,           # batch size for reader evaluation
  "sp_threshold": 8                   # threshold for picking supporting sentence
  

  config.py for more configuration.

Evaluation Process

Evaluation

• Retriever evaluation on Ascend

  sh run_eval_ascend.sh
  

  Evaluation result will be stored in the scripts path, whose folder name begins with "eval_tr". You can find the result like the
  followings in log.

  ###step###:  0
  val: 0
  count: 1
  true count: 0
  PEM: 0.0
  
  ...
  ###step###:  7396
  val:6796
  count:7397
  true count: 6924
  PEM: 0.9187508449371367
  true top8 PEM: 0.9815135759676488
  evaluation time (h): 20.155506462653477
  

• Re-ranker and reader evaluation on Ascend

  Use the output of retriever as input of re-ranker

  sh run_eval_ascend_reranker_reader.sh
  

  Evaluation result will be stored in the scripts path, whose folder name begins with "eval". You can find the result like the
  followings in log.

  total top1 pem: 0.8803511141120864
  
  ...
  em: 0.67440918298447
  f1: 0.8025625656569652
  prec: 0.8292800393689271
  recall: 0.8136908451841731
  sp_em: 0.6009453072248481
  sp_f1: 0.844555664157302
  sp_prec: 0.8640844345841021
  sp_recall: 0.8446123918845106
  joint_em: 0.4537474679270763
  joint_f1: 0.715119580346802
  joint_prec: 0.7540052057184267
  joint_recall: 0.7250240424067661
  

Model Description

Performance

Inference Performance

Parameter	BGCF Ascend
Model Version	Inception V1
Resource	Ascend 910
uploaded Date	03/12/2021(month/day/year)
MindSpore Version	1.2.0
Dataset	HotPotQA
Batch_size	1
Output	inference path
PEM	0.9188
total top1 pem	0.88
joint_f1	0.7151

Description of random situation

No random situation for evaluation.

ModelZoo Homepage

Please check the official homepage.