Also link to the training script

README.md

@@ -131,9 +131,9 @@ co2_eq_emissions:
 
 This is a [sentence-transformers](https://www.SBERT.net) model trained on the [wikititles](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-wikititles), [tatoeba](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-tatoeba), [talks](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-talks), [europarl](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-europarl), [global_voices](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-global-voices), [muse](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-muse), [wikimatrix](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-wikimatrix), [opensubtitles](https://huggingface.co/datasets/sentence-transformers/parallel-sentences-opensubtitles), [stackexchange](https://huggingface.co/datasets/sentence-transformers/stackexchange-duplicates), [quora](https://huggingface.co/datasets/sentence-transformers/quora-duplicates), [wikianswers_duplicates](https://huggingface.co/datasets/sentence-transformers/wikianswers-duplicates), [all_nli](https://huggingface.co/datasets/sentence-transformers/all-nli), [simple_wiki](https://huggingface.co/datasets/sentence-transformers/simple-wiki), [altlex](https://huggingface.co/datasets/sentence-transformers/altlex), [flickr30k_captions](https://huggingface.co/datasets/sentence-transformers/flickr30k-captions), [coco_captions](https://huggingface.co/datasets/sentence-transformers/coco-captions), [nli_for_simcse](https://huggingface.co/datasets/sentence-transformers/nli-for-simcse) and [negation](https://huggingface.co/datasets/jinaai/negation-dataset) datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, paraphrase mining, text classification, clustering, and more.
 
-This model was trained with a [Matryoshka loss](https://huggingface.co/blog/matryoshka), allowing you to truncate the embeddings for faster retrieval at minimal performance costs.
-
-See [Evaluations](#evaluation) for details on performance on NanoBEIR, embedding speed, and Matryoshka dimensionality truncation.
+* **Matryoshka:** This model was trained with a [Matryoshka loss](https://huggingface.co/blog/matryoshka), allowing you to truncate the embeddings for faster retrieval at minimal performance costs.
+* **Evaluations:** See [Evaluations](#evaluation) for details on performance on NanoBEIR, embedding speed, and Matryoshka dimensionality truncation.
+* **Training Script:** See [train.py](train.py) for the training script used to train this model from scratch.
 
 ## Model Details