Update README.md

README.md

@@ -22,7 +22,7 @@ Yap Phone Screenshot Classifier predicts two labels for one image:
 
 The model returns exactly two outputs: `screen` and `safety`.
 
-The model is fine-tuned from `timm/mobilenetv4_conv_medium.e250_r384_in12k` on the Yap phone screenshot dataset: [`yapwithai/phone-screenshots`](https://huggingface.co/datasets/yapwithai/phone-screenshots).
+The model is fine-tuned from `timm/mobilenetv4_conv_medium.e250_r384_in12k`.
 
 ## What It Is For
 
@@ -180,14 +180,6 @@ Or run it directly:
 bun inference/typescript.ts example.png another.png --model-format fp16
 ```
 
-## Training Data
-
-The model is trained from local shards of [`yapwithai/phone-screenshots`](https://huggingface.co/datasets/yapwithai/phone-screenshots). Screen labels come from the dataset `label` field. Safety labels come from the dataset `safety` field. `fine_label` is not used.
-
-Training uses one minimum train-count floor for both heads. The default is `--min-train-count 300`. Labels with fewer than `--min-train-count` train samples are not trained directly. Screen labels below the floor are folded into `generic`; safety labels below the floor are omitted from the safety head. The exported `config.json` and `inference/labels.json` files are the source of truth for the active screen and safety labels.
-
-Training uses adaptive label sampling. `--screen-target-ratio` and `--safety-target-ratio` set the desired effective per-epoch class ratio; `--screen-max-repeat` and `--safety-max-repeat` cap how often smaller classes can repeat in one epoch.
-
 ## Citation
 
 If you use this model, please cite: