Update README.md

README.md

@@ -1,40 +1,43 @@
-# MS MARCO Passages Hard Negatives
-
-[MS MARCO](https://microsoft.github.io/msmarco/) is a large scale information retrieval corpus that was created based on real user search queries using Bing search engine.
-
-This dataset repository contains files that are helpful to train bi-encoder models e.g. using [sentence-transformers](https://www.sbert.net).
-
-## Training Code
-You can find here an example how these files can be used to train bi-encoders: [SBERT.net - MS MARCO - MarginMSE](https://www.sbert.net/examples/training/ms_marco/README.html#marginmse)
-
-## cross-encoder-ms-marco-MiniLM-L-6-v2-scores.pkl.gz
-
-This is a pickled dictionary in the format: `scores[qid][pid] -> cross_encoder_score`
-
-It contains 160 million cross-encoder scores for (query, paragraph) pairs using the [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) model.
-
-## msmarco-hard-negatives.jsonl.gz
-This is a jsonl file: Each line is a JSON object. It has the following format:
-```
-{"qid": 867436, "pos": [5238393], "neg": {"bm25": [...], ...}}
-```
-
-`qid` is the query-ID from MS MARCO, `pos` is a list with paragraph IDs for positive passages. `neg` is a dictionary where we mined hard negatives using different (mainly dense retrieval) systems.
-
-It contains hard negatives mined from BM25 (using ElasticSearch) and the following dense models:
-```
-msmarco-distilbert-base-tas-b
-msmarco-distilbert-base-v3
-msmarco-MiniLM-L-6-v3
-distilbert-margin_mse-cls-dot-v2
-distilbert-margin_mse-cls-dot-v1
-distilbert-margin_mse-mean-dot-v1
-mpnet-margin_mse-mean-v1
-co-condenser-margin_mse-cls-v1
-distilbert-margin_mse-mnrl-mean-v1
-distilbert-margin_mse-sym_mnrl-mean-v1
-distilbert-margin_mse-sym_mnrl-mean-v2
-co-condenser-margin_mse-sym_mnrl-mean-v1
-```
-
-From each system, 50 most similar paragraphs were mined for a given query.
+# MS MARCO Passages Hard Negatives
+
+> [!NOTE]
+> This repository contains raw datasets, all of which have also been formatted for easy training in the [MS MARCO Mined Triplets](https://huggingface.co/collections/sentence-transformers/ms-marco-mined-triplets-6644d6f1ff58c5103fe65f23) collection. We recommend looking there first.
+
+[MS MARCO](https://microsoft.github.io/msmarco/) is a large scale information retrieval corpus that was created based on real user search queries using Bing search engine.
+
+This dataset repository contains files that are helpful to train bi-encoder models e.g. using [sentence-transformers](https://www.sbert.net).
+
+## Training Code
+You can find here an example how these files can be used to train bi-encoders: [SBERT.net - MS MARCO - MarginMSE](https://www.sbert.net/examples/training/ms_marco/README.html#marginmse)
+
+## cross-encoder-ms-marco-MiniLM-L-6-v2-scores.pkl.gz
+
+This is a pickled dictionary in the format: `scores[qid][pid] -> cross_encoder_score`
+
+It contains 160 million cross-encoder scores for (query, paragraph) pairs using the [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) model.
+
+## msmarco-hard-negatives.jsonl.gz
+This is a jsonl file: Each line is a JSON object. It has the following format:
+```
+{"qid": 867436, "pos": [5238393], "neg": {"bm25": [...], ...}}
+```
+
+`qid` is the query-ID from MS MARCO, `pos` is a list with paragraph IDs for positive passages. `neg` is a dictionary where we mined hard negatives using different (mainly dense retrieval) systems.
+
+It contains hard negatives mined from BM25 (using ElasticSearch) and the following dense models:
+```
+msmarco-distilbert-base-tas-b
+msmarco-distilbert-base-v3
+msmarco-MiniLM-L-6-v3
+distilbert-margin_mse-cls-dot-v2
+distilbert-margin_mse-cls-dot-v1
+distilbert-margin_mse-mean-dot-v1
+mpnet-margin_mse-mean-v1
+co-condenser-margin_mse-cls-v1
+distilbert-margin_mse-mnrl-mean-v1
+distilbert-margin_mse-sym_mnrl-mean-v1
+distilbert-margin_mse-sym_mnrl-mean-v2
+co-condenser-margin_mse-sym_mnrl-mean-v1
+```
+
+From each system, 50 most similar paragraphs were mined for a given query.