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Showcase/MS MARCO - Passage Relevance Ranking
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MS MARCO - Passage Relevance Ranking

Passage relevance ranking based on the MS MARCO dataset (Nguyen et al., NeurIPS 2016 Workshop). Annotators assess the relevance of a candidate passage to a given search query using a graded relevance scale.

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配置文件config.yaml

# MS MARCO - Passage Relevance Ranking
# Based on Nguyen et al., NeurIPS 2016 Workshop
# Paper: https://arxiv.org/abs/1611.09268
# Dataset: https://microsoft.github.io/msmarco/
#
# Assess the relevance of a candidate passage to a given search query.
# Use the graded relevance scale to indicate how well the passage
# answers the query, from perfectly relevant to completely off-topic.

annotation_task_name: "MS MARCO: Passage Relevance Ranking"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  - annotation_type: select
    name: relevance_grade
    description: "How relevant is this passage to the query?"
    labels:
      - "Perfectly Relevant"
      - "Partially Relevant"
      - "Not Relevant"
      - "Off-Topic"
    tooltips:
      "Perfectly Relevant": "The passage directly and completely answers the query"
      "Partially Relevant": "The passage contains some relevant information but does not fully answer the query"
      "Not Relevant": "The passage is on a related topic but does not answer the query"
      "Off-Topic": "The passage has no relation to the query whatsoever"

  - annotation_type: radio
    name: passage_quality
    description: "Is the passage well-written and informative?"
    labels:
      - "High Quality"
      - "Acceptable"
      - "Low Quality"
    keyboard_shortcuts:
      "High Quality": "1"
      "Acceptable": "2"
      "Low Quality": "3"

annotation_instructions: |
  You will be shown a search query and a candidate passage. Your task is to:
  1. Read the query carefully to understand the user's information need.
  2. Read the passage and assess how well it answers the query.
  3. Select the appropriate relevance grade from the dropdown.
  4. Rate the overall quality of the passage.

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="background: #fefce8; border: 1px solid #fde68a; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #a16207;">Query:</strong>
      <p style="font-size: 18px; font-weight: 600; line-height: 1.6; margin: 8px 0 0 0;">{{query}}</p>
    </div>
    <div style="background: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #0369a1;">Passage:</strong>
      <p style="font-size: 16px; line-height: 1.7; margin: 8px 0 0 0;">{{text}}</p>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 100
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false

示例数据sample-data.json

[
  {
    "id": "msmarco_001",
    "text": "The Great Wall of China is approximately 13,171 miles (21,196 kilometers) long, according to a comprehensive archaeological survey completed in 2012 by China's State Administration of Cultural Heritage. This measurement includes all sections built over various dynasties, not just the well-known Ming Dynasty portions.",
    "query": "how long is the great wall of china"
  },
  {
    "id": "msmarco_002",
    "text": "Photosynthesis is the process by which green plants and certain other organisms transform light energy into chemical energy. During photosynthesis, plants capture light energy from the sun and use it to convert water and carbon dioxide into oxygen and glucose. The overall equation is: 6CO2 + 6H2O + light energy -> C6H12O6 + 6O2.",
    "query": "what is the process of photosynthesis"
  }
]

// ... and 8 more items

获取此设计

View on GitHub

Clone or download from the repository

快速开始:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/text/information-retrieval/msmarco-passage-ranking
potato start config.yaml

详情

标注类型

selectradio

领域

NLPInformation Retrieval

应用场景

Passage RankingSearch RelevanceQuestion Answering

标签

msmarcopassage-rankingrelevancesearchinformation-retrievalneurips2016

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