README.md

metadata

language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
task_categories:
  - feature-extraction
  - sentence-similarity
pretty_name: ms-marco-english
tags:
  - sentence-transformers
  - colbert
  - lightonai
dataset_info:
  - config_name: queries
    features:
      - name: query_id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: train
        num_examples: 808731
  - config_name: documents
    features:
      - name: document_id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: train
        num_examples: 8841823
  - config_name: train
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence:
          value:
            dtype: string
      - name: scores
        sequence:
          value:
            dtype: float32
    splits:
      - name: train
        num_examples: 808728
configs:
  - config_name: queries
    data_files:
      - split: train
        path: english_queries.train.parquet
  - config_name: documents
    data_files:
      - split: train
        path: english_collection.parquet
  - config_name: train
    data_files:
      - split: train
        path: dataset.parquet

ms-marco-en-bge

This dataset contains the MS MARCO dataset with documents similar to the query mined using BGE-M3 and then scored by bge-reranker-v2-m3. It can be used to train a retrieval model using knowledge distillation.

knowledge distillation

To fine-tune a model using knowledge distillation loss we will need three distinct file:

• Datasets

  from datasets import load_dataset
  
  train = load_dataset(
      "lightonai/ms-marco-en-bge",
      "train",
      split="train",
  )
  
  queries = load_dataset(
      "lightonai/ms-marco-en-bge",
      "queries",
      split="train",
  )
  
  documents = load_dataset(
      "lightonai/ms-marco-en-bge",
      "documents",
      split="train",
  )
  

Where:

• train contains three distinct columns: ['query_id', 'document_ids', 'scores']

{
    "query_id": 54528,
    "document_ids": [
        6862419,
        335116,
        339186,
        7509316,
        7361291,
        7416534,
        5789936,
        5645247,
    ],
    "scores": [
        0.4546215673141326,
        0.6575686537173476,
        0.26825184192900203,
        0.5256195579370395,
        0.879939718687207,
        0.7894968184862693,
        0.6450100468854655,
        0.5823844608171467,
    ],
}

Assert that the length of document_ids is the same as scores.

• queries contains two distinct columns: ['query_id', 'text']

{"query_id": 749480, "text": "what is function of magnesium in human body"}

• documents contains two distinct columns: ['document_ids', 'text']

{
    "document_id": 136062,
    "text": "2. Also called tan .a fundamental trigonometric function that, in a right triangle, is expressed as the ratio of the side opposite an acute angle to the side adjacent to that angle. 3. in immediate physical contact; touching; abutting. 4. a. touching at a single point, as a tangent in relation to a curve or surface.lso called tan .a fundamental trigonometric function that, in a right triangle, is expressed as the ratio of the side opposite an acute angle to the side adjacent to that angle. 3. in immediate physical contact; touching; abutting. 4. a. touching at a single point, as a tangent in relation to a curve or surface.",
}