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lightonai/lighton-ms-marco-mini

ms-marco-mini This dataset gathers very few samples from MS MARCO to provide an example of triplet-based / knowledge distillation dataset formatting. triplet subset The triplet file is all we need to fine-tune a model based on contrastive loss. Columns: "query", "positive", "negative" Column types: str, str, str Examples:{ "query": "what are the liberal arts?", "positive": 'liberal arts. 1. the academic course of instruction at a college intended to provide… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/lighton-ms-marco-mini.

sourceHugging Faceupdated 2y agoView on Hugging Face
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ms-marco-mini

This dataset gathers very few samples from MS MARCO to provide an example of triplet-based / knowledge distillation dataset formatting.

triplet subset

The triplet file is all we need to fine-tune a model based on contrastive loss.

  • —Columns: "query", "positive", "negative"
  • —Column types: str, str, str
  • —Examples:
python
    {
      "query": "what are the liberal arts?",
      "positive": 'liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.',
      "negative": 'The New York State Education Department requires 60 Liberal Arts credits in a Bachelor of Science program and 90 Liberal Arts credits in a Bachelor of Arts program. In the list of course descriptions, courses which are liberal arts for all students are identified by (Liberal Arts) after the course number.'
    }
  • —Datasets
python
   from datasets import load_dataset

   dataset = load_dataset("lightonai/lighton-ms-marco-mini", "triplet", split="train")
knowledge distillation subset

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

  • —Datasets
python
    from datasets import load_dataset
    
    train = load_dataset(
        "lightonai/lighton-ms-marco-mini",
        "train",
        split="train",
    )
    
    queries = load_dataset(
        "lightonai/lighton-ms-marco-mini",
        "queries",
        split="train",
    )
    
    documents = load_dataset(
        "lightonai/lighton-ms-marco-mini",
        "documents",
        split="train",
    )

Where:

  • —train contains three distinct columns: ['query_id', 'document_ids', 'scores']
python
{
    "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']
python
{"query_id": 749480, "text": "what is function of magnesium in human body"}
  • —documents contains two distinct columns: ['document_ids', 'text']
python
{
    "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.",
}