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.
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
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
领域
应用场景
标签
发现问题或想改进此设计?
提交 Issue相关设计
Financial PhraseBank - Sentiment Classification
Sentiment classification of financial news sentences based on the Financial PhraseBank dataset (Malo et al., JASIST 2014). Annotators classify sentences from financial news articles into fine-grained and coarse sentiment categories.
KG-BERT Knowledge Graph Triple Validation
Validate knowledge graph triples for correctness and annotate relation types based on the KG-BERT framework. Annotators assess whether entity-relation-entity triples are valid, classify the relation type, and provide entity descriptions.
SemEval-2007 - Word Sense Disambiguation
Word sense disambiguation task based on the SemEval-2007 English lexical sample (Pradhan et al.). Annotators identify the correct sense of a target word in context from a provided list of sense definitions.