Upload README.md

Reworded the readme for clarity.

README.md

@@ -9,7 +9,7 @@ license: apache-2.0
 ---
 
 # Overview
-The Boring Embeddings are negative-control textual inversion embeddings for Stable Diffusion models.  They are 
+The Boring Embeddings are negative textual inversion embeddings for Stable Diffusion models.  They are 
 widely used in the community (10M+ generations across public tools) to suppress low-quality,
 low-engagement visual patterns and make outputs look more visually appealing.
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@@ -37,10 +37,10 @@ the embedding's name in your NEGATIVE prompt for more interesting outputs.
 
 ## Research Summary / Technical Contribution
 
-This project demonstrates a novel approach to constructing negative-control embeddings using automated quality-based sampling 
-from large community-labeled image datasets. The method avoids hand-curated defect lists and instead captures latent features 
-correlated with low engagement. Applications include quality control in generative models, aesthetic filtering, and 
-domain-specific negative conditioning.
+This project trains negative textual inversion embeddings using automated quality-based sampling from large community-labeled
+image datasets.  The method avoids hand-curated defect lists and instead captures visual patterns 
+associated with low-engagement images.  Typical uses include quality control in generative models, aesthetic 
+filtering, and domain-specific negative conditioning.
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@@ -54,14 +54,14 @@ depend on manually curated lists of tags describing features people do not want
 * It can only capture named concepts.  If there exist unnamed yet visually unappealing concepts that just make an image look wrong, but for reasons that cannot be succinctly explained, they will not be captured by a list of tags.  
 
 To address these problems, we employ textual inversion on a set of images extracted from several large community-tagged art 
-datasets with rich metadata.  Each of these sites is a rich resource of millions of 
-hand-labeled artworks which allow users to express their approval of an artwork by either up-voting it or marking it as a favorite. 
+datasets with rich metadata.  Each of these sites contains millions of hand-labeled artworks. 
+Users can express their approval of an artwork by either up-voting it or marking it as a favorite. 
 The Boring embeddings were specifically trained on artworks automatically selected from these sites according to the criteria 
 that no user has ever favorited them, and they have 0 or only a very small number of up or down votes.  The Boring embeddings 
 thus learned to produce uninteresting low-quality images, so when they are used in the negative prompt of a stable diffusion image generator, 
 the model avoids making mistakes that would make the generation more boring.
 Each training sample consisted of an image paired with a comma-separated list of all the tags associated with it on the site it was sourced from, with the embedding’s name prepended to the list. 
-This ensures that the model learns to associate the embedding with the characteristics of uninteresting images in a way that aligns with the dataset’s tagging system.
+This ensures that the model learns to associate the embedding with the characteristics of uninteresting images in a way that is consistent with the dataset’s tagging system.
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@@ -175,6 +175,6 @@ Across nearly all prompt/seed pairs, the versions generated with the embedding e
 
 An independent viewer (not involved in the generation process) remarked that the embedding versions were “clearly better” in the majority of comparisons.
 
-This artifact provides a transparent, reproducible demonstration of the embedding’s effect across a wide range of seeds and prompts.
+This dataset makes the embedding’s effect easy to inspect across many prompts and seeds.