update readme

README.md

@@ -4,6 +4,15 @@
 
 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:
 ```
@@ -12,8 +21,20 @@ This is a jsonl file: Each line is a JSON object. It has the following format:
 
 `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.
 
-## 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 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
+```
 
-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.
+From each system, 50 most similar paragraphs were mined for a given query.