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README.md

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+---
+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.",
+}
+```

dataset.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3d363f28d54a498a38e586983c1e4e06380b81196f6591379c46c4723f40e081
+size 340140593

english_collection.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d48ea2740ac952d441429e64725aa1f2e6beada3e4c07135cb794e14a9b1bedc
+size 1643716390

english_queries.train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a386489eb9c6c88e75b57e1d6062fbb08ad9fc277700cdf7aa4e79fa1042581d
+size 25623832