README.md

metadata

language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 1M<n<10M
task_categories:
  - feature-extraction
  - sentence-similarity
pretty_name: Yahoo Answers
tags:
  - sentence-transformers
dataset_info:
  - config_name: question-answer-pair
    features:
      - name: question
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 441860501
        num_examples: 681164
    download_size: 296974225
    dataset_size: 441860501
  - config_name: title-answer-pair
    features:
      - name: title
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 532353635
        num_examples: 1198260
    download_size: 359777740
    dataset_size: 532353635
  - config_name: title-question-answer-pair
    features:
      - name: question
        dtype: string
      - name: answer
        dtype: string
    splits:
      - name: train
        num_bytes: 462195629
        num_examples: 599417
    download_size: 308542541
    dataset_size: 462195629
  - config_name: title-question-pair
    features:
      - name: title
        dtype: string
      - name: questions
        dtype: string
    splits:
      - name: train
        num_bytes: 190935497
        num_examples: 659896
    download_size: 132675030
    dataset_size: 190935497
configs:
  - config_name: question-answer-pair
    data_files:
      - split: train
        path: question-answer-pair/train-*
  - config_name: title-answer-pair
    data_files:
      - split: train
        path: title-answer-pair/train-*
  - config_name: title-question-answer-pair
    data_files:
      - split: train
        path: title-question-answer-pair/train-*
  - config_name: title-question-pair
    data_files:
      - split: train
        path: title-question-pair/train-*

Dataset Card for Yahoo Answers

This dataset is a collection of pairs containing titles, questions, and answers collected from Yahoo Answers. See the Yahoo Answers dataset for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.

Dataset Subsets

title-question-answer-pair subset

• Columns: "question", "answer"
• Column types: str, str
• Examples:

  {
    'question': "why doesn't an optical mouse work on a glass table? or even on some surfaces?",
    'answer': "why doesn't an optical mouse work on a glass table? Optical mice use an LED and a camera to rapidly capture images of the surface beneath the mouse.  The infomation from the camera is analyzed by a DSP (Digital Signal Processor) and used to detect imperfections in the underlying surface and determine motion. Some materials, such as glass, mirrors or other very shiny, uniform surfaces interfere with the ability of the DSP to accurately analyze the surface beneath the mouse.  \\nSince glass is transparent and very uniform, the mouse is unable to pick up enough imperfections in the underlying surface to determine motion.  Mirrored surfaces are also a problem, since they constantly reflect back the same image, causing the DSP not to recognize motion properly. When the system is unable to see surface changes associated with movement, the mouse will not work properly.",
  }
  

• Collection strategy: Reading the title-answer-pair and title-question-pair datasets, matching up the titles, filtering on just 1 question and 1 answer, and then concatenating the title + the question as the question.
• Deduplified: No

title-answer-pair subset

• Columns: "title", "answer"
• Column types: str, str
• Examples:

  {
    'title': "why doesn't an optical mouse work on a glass table?",
    'answer': 'Optical mice use an LED and a camera to rapidly capture images of the surface beneath the mouse.  The infomation from the camera is analyzed by a DSP (Digital Signal Processor) and used to detect imperfections in the underlying surface and determine motion. Some materials, such as glass, mirrors or other very shiny, uniform surfaces interfere with the ability of the DSP to accurately analyze the surface beneath the mouse.  \\nSince glass is transparent and very uniform, the mouse is unable to pick up enough imperfections in the underlying surface to determine motion.  Mirrored surfaces are also a problem, since they constantly reflect back the same image, causing the DSP not to recognize motion properly. When the system is unable to see surface changes associated with movement, the mouse will not work properly.',
  }
  

• Collection strategy: Reading the Yahoo Answers (title, answer) dataset from embedding-training-data.
• Deduplified: No

title-question-pair subset

• Columns: "title", "question"
• Column types: str, str
• Examples:

  {
    'title': "why doesn't an optical mouse work on a glass table?",
    'questions': 'or even on some surfaces?',
  }
  

• Collection strategy: Reading the Yahoo Answers (title, question) dataset from embedding-training-data.
• Deduplified: No

question-answer-pair subset

• Columns: "question", "answer"
• Column types: str, str
• Examples:

  {
    'question': 'or even on some surfaces?',
    'answer': 'Optical mice use an LED and a camera to rapidly capture images of the surface beneath the mouse.  The infomation from the camera is analyzed by a DSP (Digital Signal Processor) and used to detect imperfections in the underlying surface and determine motion. Some materials, such as glass, mirrors or other very shiny, uniform surfaces interfere with the ability of the DSP to accurately analyze the surface beneath the mouse.  \\nSince glass is transparent and very uniform, the mouse is unable to pick up enough imperfections in the underlying surface to determine motion.  Mirrored surfaces are also a problem, since they constantly reflect back the same image, causing the DSP not to recognize motion properly. When the system is unable to see surface changes associated with movement, the mouse will not work properly.',
  }
  

• Collection strategy: Reading the Yahoo Answers (question, answer) dataset from embedding-training-data.
• Deduplified: No