diff --git a/README.md b/README.md
index 26af782..fe130f0 100644
--- a/README.md
+++ b/README.md
@@ -12,37 +12,8 @@ score formula:
 
 then average for all posts
 
-system dependencies:
- - python3
- - [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) with the [tagger extension](https://github.com/toriato/stable-diffusion-webui-wd14-tagger)
- - [hydrus-dd](https://gitgud.io/koto/hydrus-dd)
-
 ```sh
 python3 -m venv env
 env/bin/pip install -Ur ./requirements.txt
-
-# by default, downloads 30 images at page 150 of the default empty query
-env/bin/python3 ./main.py download_images
-
-# gets 40 images at page 150 from tag 'rating:questionable'
-# you should add more tags to diversify the dataset before calculating scores
-env/bin/python3 ./main.py download_images 'rating:questionable' 40 150
-
-# configure interrogators / tagger models
-# set sd_webui_address to your stable diffusion webui' address
-# set dd_address to hydrus-dd's address
-# and set dd_model_name to be something identifiable about the model
-# i set it to the md5sum output of my file, to make sure that if the file
-# changes back on koto's end, my numbers may be different
-cp config.example.json config.json
-
-# fight mode -- run all interrogators against the dataset you've downloaded
-env/bin/python3 ./main.py fight
-
-# score mode -- crank the final numbers, generates graphs under plots/ folder
-env/bin/python3 ./main.py fight
-
-# keep in mind that you can download more images, run fight mode, and then
-# run score mode! the commands are aware of work that's been already done and
-# will only run the tagger models for the new files
+env/bin/python3 ./main.py
 ```
\ No newline at end of file
diff --git a/main.py b/main.py
index 3a43999..83e5abe 100644
--- a/main.py
+++ b/main.py
@@ -472,12 +472,7 @@ def plot2(output_path, normalized_scores, model_scores):
 
 
 def plot3(output_path, normalized_scores, model_scores):
-    data_for_df = {
-        "model": [],
-        "errors": [],
-        "rating_errors": [],
-        "practical_errors": [],
-    }
+    data_for_df = {"model": [], "errors": [], "rating_errors": []}
 
     for model in sorted(
         normalized_scores.keys(),
@@ -493,11 +488,9 @@ def plot3(output_path, normalized_scores, model_scores):
                 for rating in ["general", "sensitive", "questionable", "explicit"]
                 if rating in score_data["incorrect_tags"]
             )
-        practical_absolute_error = total_incorrect_tags - total_rating_errors
 
         data_for_df["errors"].append(total_incorrect_tags)
         data_for_df["rating_errors"].append(total_rating_errors)
-        data_for_df["practical_errors"].append(practical_absolute_error)
         data_for_df["model"].append(model)
 
     df = pd.DataFrame(data_for_df)
@@ -506,7 +499,6 @@ def plot3(output_path, normalized_scores, model_scores):
         data=[
             go.Bar(name="incorrect tags", x=df.model, y=df.errors),
             go.Bar(name="incorrect ratings", x=df.model, y=df.rating_errors),
-            go.Bar(name="practical error", x=df.model, y=df.practical_errors),
         ]
     )
     pio.write_image(fig, output_path, width=1024, height=800)