L-\d -> L\d

README.md

@@ -8,7 +8,7 @@ size_categories:
 task_categories:
 - feature-extraction
 - sentence-similarity
-pretty_name: MS MARCO with hard negatives from msmarco-MiniLM-L-6-v3
+pretty_name: MS MARCO with hard negatives from msmarco-MiniLM-L6-v3
 tags:
 - sentence-transformers
 dataset_info:
@@ -355,7 +355,7 @@ configs:
     path: triplet-ids/train-*
 ---
 
-# MS MARCO with hard negatives from msmarco-MiniLM-L-6-v3
+# MS MARCO with hard negatives from msmarco-MiniLM-L6-v3
 
 [MS MARCO](https://microsoft.github.io/msmarco/) is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
 For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train [Sentence Transformer models](https://www.sbert.net).
@@ -367,7 +367,7 @@ These are the datasets generated using the 13 different models:
 * [msmarco-bm25](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25)
 * [msmarco-msmarco-distilbert-base-tas-b](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-tas-b)
 * [msmarco-msmarco-distilbert-base-v3](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3)
-* [msmarco-msmarco-MiniLM-L-6-v3](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-MiniLM-L-6-v3)
+* [msmarco-msmarco-MiniLM-L6-v3](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-MiniLM-L6-v3)
 * [msmarco-distilbert-margin-mse-cls-dot-v2](https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-cls-dot-v2)
 * [msmarco-distilbert-margin-mse-cls-dot-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-cls-dot-v1)
 * [msmarco-distilbert-margin-mse-mean-dot-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-distilbert-margin-mse-mean-dot-v1)
@@ -449,7 +449,7 @@ We release two subsets, one with strings (`triplet-all`) and one with IDs (`trip
 ### Hard Triplets
 
 For each query-positive pair, mine the 50 most similar passages to the query and consider them as negatives.
-Filter these 50 negatives such that `similarity(query, positive) > similarity(query, negative) + margin`, with [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) and `margin = 3.0`.
+Filter these 50 negatives such that `similarity(query, positive) > similarity(query, negative) + margin`, with [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) and `margin = 3.0`.
 In short, we rely on a CrossEncoder to try and make sure that the negatives are indeed dissimilar to the query.
 We release two subsets, one with strings (`triplet-hard`) and one with IDs (`triplet-hard-ids`) to be used with [sentence-transformers/msmarco-corpus](https://huggingface.co/datasets/sentence-transformers/msmarco-corpus).