README.md

---
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](https://microsoft.github.io/msmarco/) dataset with documents similar to the query mined using [BGE-M3](https://huggingface.co/BAAI/bge-m3) and then scored by [bge-reranker-v2-m3](BAAI/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
    ```python
    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']`

```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.",
}
```