Delete README.md

README.md

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----
-dataset_info:
-- config_name: bert-ensemble-margin-mse
-  features:
-  - name: query_id
-    dtype: string
-  - name: positive_id
-    dtype: string
-  - name: negative_id
-    dtype: string
-  - name: score
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 1581481699
-    num_examples: 39780704
-  download_size: 1124942270
-  dataset_size: 1581481699
-- config_name: bert-ensemble-mse
-  features:
-  - name: query_id
-    dtype: string
-  - name: passage_id
-    dtype: string
-  - name: score
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 2298848690
-    num_examples: 79561408
-  download_size: 1513817201
-  dataset_size: 2298848690
-- config_name: corpus
-  features:
-  - name: passage_id
-    dtype: string
-  - name: passage
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 3114616008
-    num_examples: 8841823
-  download_size: 1688968114
-  dataset_size: 3114616008
-- config_name: labeled-list
-  features:
-  - name: query_id
-    dtype: string
-  - name: doc_ids
-    sequence: string
-  - name: labels
-    sequence: int64
-  splits:
-  - name: train
-    num_bytes: 7200227157
-    num_examples: 400282
-  download_size: 2434173045
-  dataset_size: 7200227157
-- config_name: queries
-  features:
-  - name: query_id
-    dtype: string
-  - name: query
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 40009674
-    num_examples: 808731
-  download_size: 29902737
-  dataset_size: 40009674
-- config_name: rankgpt4-colbert
-  features:
-  - name: query_id
-    dtype: string
-  - name: doc_ids
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 2204107
-    num_examples: 2000
-  download_size: 1650269
-  dataset_size: 2204107
-- config_name: rankzephyr-colbert
-  features:
-  - name: query_id
-    dtype: string
-  - name: doc_ids
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 11007320
-    num_examples: 10000
-  download_size: 8198146
-  dataset_size: 11007320
-- config_name: triplets
-  features:
-  - name: query_id
-    dtype: string
-  - name: positive_id
-    dtype: string
-  - name: negative_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 12613929573
-    num_examples: 397226027
-  download_size: 10566329165
-  dataset_size: 12613929573
-configs:
-- config_name: bert-ensemble-margin-mse
-  data_files:
-  - split: train
-    path: bert-ensemble-margin-mse/train-*
-- config_name: bert-ensemble-mse
-  data_files:
-  - split: train
-    path: bert-ensemble-mse/train-*
-- config_name: corpus
-  data_files:
-  - split: train
-    path: corpus/train-*
-- config_name: labeled-list
-  data_files:
-  - split: train
-    path: labeled-list/train-*
-- config_name: queries
-  data_files:
-  - split: train
-    path: queries/train-*
-- config_name: rankgpt4-colbert
-  data_files:
-  - split: train
-    path: rankgpt4-colbert/train-*
-- config_name: rankzephyr-colbert
-  data_files:
-  - split: train
-    path: rankzephyr-colbert/train-*
-- config_name: triplets
-  data_files:
-  - split: train
-    path: triplets/train-*
-task_categories:
-- feature-extraction
-- sentence-similarity
-- text-classification
-language:
-- en
-tags:
-- sentence-transformers
-- cross-encoder
-pretty_name: MS MARCO
-size_categories:
-- 100M<n<1B
----
-
-# MS MARCO Training Dataset
-
-This dataset consists of 4 separate datasets, each using the MS MARCO Queries and passages:
-* `triplets`: This subset contains triplets of query-id, positive-id, negative-id as provided in `qidpidtriples.train.full.2.tsv.gz` from the MS MARCO Website. The only change is that this dataset has been reshuffled. This dataset can easily be used with an `MultipleNegativesRankingLoss` a.k.a. InfoNCE loss.
-* `labeled-list`: This subset contains triplets of query-id, doc-ids, labels, i.e. every query is matched with every document from the `triplets` subset, with the labels column containing a list denoting which doc_ids represent positives and which ones represent negatives.
-* `bert-ensemble-mse`: This subset contains tuples with a score. This score is from the BERT_CAT Ensemble from [Hofstätter et al. 2020](https://zenodo.org/records/4068216), and can easily be used with a `MLELoss` to train an embedding or reranker model via distillation.
-* `bert-ensemble-margin-mse`: This subset contains triplets with a score, such that the score is `ensemble_score(query, positive) - ensemble_score(query, negative)`, also from the BERT_CAT Ensemble from [Hofstätter et al. 2020](https://zenodo.org/records/4068216). It can easily be used with a `MarginMLELoss` to train an embedding or reranker model via distillation.
-* `rankgpt4-colbert`: This subset contains a RankGPT4 reranking of the top 100 MS MARCO passages retrieved by ColBERTv2. This ranking was compiled by [Schlatt et. al 2024](https://zenodo.org/records/11147862).
-* `rankzephyr-colbert`: This subset contains a RankZephyr reranking of the top 100 MS MARCO passages retrieved by ColBERTv2. This ranking was compiled by [Schlatt et. al 2024](https://zenodo.org/records/11147862).
-
-For all datasets, the id's can be converted using the `queries` and `corpus` subsets to real texts.
-
-## Dataset Subsets
-
-### `corpus` subset
-
-* Columns: "passage_id", "passage"
-* Column types: `str`, `str`
-* Examples:
-    ```python
-    {
-      "passage_id": "0",
-      "passage": "The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.",
-    }
-    ```
-* Collection strategy: Reading `collection.tar.gz` from MS MARCO.
-
-### `queries` subset
-
-* Columns: "query_id", "query"
-* Column types: `str`, `str`
-* Examples:
-    ```python
-    {
-      "query_id": "121352",
-      "query": "define extreme",
-    }
-    ```
-* Collection strategy: Reading `queries.tar.gz` from MS MARCO.
-
-### `triplets` subset
-
-* Columns: "query_id", "positive_id", "negative_id"
-* Column types: `str`, `str`, `str`
-* Examples:
-    ```python
-    {
-      "query_id": "395861",
-      "positive_id": "1185464",
-      "negative_id": "6162229",
-    }
-    ```
-* Collection strategy: Reading `qidpidtriples.train.full.2.tsv.gz` from MS MARCO and shuffling the dataset rows.
-
-### `labeled-list` subset
-
-* Columns: "query_id", "doc_ids", "labels"
-* Column types: `str`, `List[str]`, `List[int]`
-* Examples:
-    ```python
-    {
-      "query_id": "100",
-      "doc_ids": ["3837260", "7854412", "4778006", "7929416", "5833477", "2715823", "903728", "1418399", "2544108", "4592808", "3565885", "260356", "5885724", "2976754", "3530456", "903722", "5136237", "6166367", "5372728", "6166373", "1615726", "5909725", "3278290", "570067", "2628703", "3619930", "3282101", "570061", "1442855", "5293099", "3976606", "3542912", "4358422", "4729309", "3542156", "102825", "2141701", "5885727", "1007725", "5137341", "180070", "2107140", "4942724", "3915139", "7417638", "7426645", "393085", "3129231", "4905980", "3181468", "7218730", "7159323", "5071423", "1609775", "3476284", "2876976", "6064616", "2752167", "5833480", "5451115", "6052155", "6551293", "2710795", "3231730", "1111340", "7885924", "2822828", "3034062", "3515232", "987726", "3129232", "4066994", "3680517", "6560480", "4584385", "5786855", "6117953", "8788361", "1960434", "212333", "7596616", "8433601", "3070543", "3282099", "5559299", "4070401", "5728025", "4584386", "8614523", "7452451", 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-      "labels": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-    }
-    ```
-* Collection strategy: Reading the `triplets` subset and grouping all triplets by query_id. The large majority of queries have exactly 1000 doc_ids, out of which often only 1 is labeled positive. Up to 7 documents are labeled positive per query in the entire subset.
-
-
-### `bert-ensemble-mse` subset
-
-* Columns: "query_id", "passage_id", "score"
-* Column types: `str`, `str`, `float64`
-* Examples:
-    ```python
-    {
-      "query_id": "400296",
-      "passage_id": "1540783",
-      "score": 6.624662,
-    }
-    ```
-* Collection strategy: Reading the BERT_CAT Ensemble scores from [Hofstätter et al. 2020](https://zenodo.org/records/4068216).
-
-### `bert-ensemble-margin-mse` subset
-
-* Columns: "query_id", "positive_id", "negative_id", "score"
-* Column types: `str`, `str`, `str`, `float64`
-* Examples:
-    ```python
-    {
-      "query_id": "400296",
-      "positive_id": "1540783",
-      "negative_id": "3518497",
-      "score": 4.028059,
-    }
-    ```
-* Collection strategy: Reading the BERT_CAT Ensemble scores from [Hofstätter et al. 2020](https://zenodo.org/records/4068216) and computing `score = pos_score - neg_score` for each triplet.
-
-### `rankgpt4-colbert` subset
-
-* Columns: "query_id", "doc_ids"
-* Column types: `str`, `list[str]`
-* Examples:
-    ```python
-    {
-      "query_id": "1002990",
-      "doc_ids": ["3227617", "3227618", "2425847", "3290896", "6964111", "6136903", "6136902", "6136909", "2242080", "2425843", "3227616", "3227622", "4433358", "2625224", "1292817", "3151910", "3151908", "1292819", "2597066", "1292822", "2597061", "1292823", "1292821", "2242077", "7869866", "2242076", "6964112", "3227613", "3227614", "3227620", "8466240", "4503976", "2022084", "4503979", "5220703", "4274806", "4274800", "4274805", "4274799", "4274801", "3227621", "4433357", "4760228", "8801589", "4433356", "4274797", "5334021", "5019160", "4784355", "2625226", "4820159", "6136907", "6136908", "8743919", "2625222", "4261266", "2242079", "2242075", "2242078", "4760231", "4760233", "3305593", "6078688", "6136910", "8185538", "4357995", "2276483", "7752", "2104661", "7135886", "3151912", "3526055", "4252749", "4252745", "2731898", "2425844", "4433361", "531164", "3627638", "3627630", "2589697", "4252748", "3208439", "4760234", "2069200", "5024557", "2512795", "2845254", "7051021", "8516705", "3627631", "1629565", "4303606", "8679732", "4228604", "1006454", "4303602", "6136906", "6136905", "4433362"],
-    }
-    ```
-* Collection strategy: Reading the `__rankgpt-colbert-2000-sampled-100__msmarco-passage-train-judged.run` file from https://zenodo.org/records/11147862, which were compiled by [Schlatt et al.](https://arxiv.org/abs/2405.07920).
-
-### `rankzephyr-colbert` subset
-
-* Columns: "query_id", "doc_ids"
-* Column types: `str`, `list[str]`
-* Examples:
-    ```python
-    {
-      "query_id": "1002990",
-      "doc_ids": ["3227618", "3227616", "3227617", "3227622", "2625224", "4433358", "3227621", "4433357", "7869866", "2242079", "2242075", "2242078", "6136907", "2425847", "4433356", "6136905", "6136906", "3227614", "3227613", "3227620", "2242076", "4760228", "2625226", "5334021", "1292823", "4760231", "1292821", "2242077", "2597061", "4433362", "4274805", "1292817", "3151908", "3151910", "2597066", "6136908", "6136902", "3290896", "4820159", "8801589", "4784355", "5019160", "4274800", "4274801", "4274806", "4274797", "6136903", "4760233", "5024557", "2512795", "6964112", "6964111", "2625222", "6078688"4303606", "7051021", "4261266", "6136909", "8466240", "4503976", "7752", "2104661", "7135886", "3208439", "4228604", "8679732", "2022084", "4433361", "430360"4760234", "4252745", "4252748"]
-    }
-    ```
-* Collection strategy: Reading the `__rankzephyr-colbert-10000-sampled-100__msmarco-passage-train-judged.run` file from https://zenodo.org/records/11147862, which were compiled by [Schlatt et al.](https://arxiv.org/abs/2405.07920).