Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 title: Predicting Memorization Before Fine-Tuning
-emoji: 🔎
+authors: Jaydeep Borkar, Niloofar Mireshghallah, and David A. Smith
 colorFrom: indigo
 colorTo: green
 sdk: gradio
@@ -12,17 +12,15 @@ license: apache-2.0
 
 # Predicting Memorization Before Fine-Tuning — demo
 
-Interactive companion to the paper. A classifier trained only on **pre-fine-tuning**
-base-model features predicts which sequences a fine-tuned model will memorize.
+Interactive demo to the paper. A classifier trained only on pre-fine-tuning base-model features predicts which sequences a fine-tuned model will memorize.
 
-- **Explore the held-out 1M** — pick a real Pythia-1.4B training sequence and see whether the
-  fine-tuned model actually memorized it (ground truth), next to the classifier's risk score and
-  the base-model feature values. Datasets: FineWeb, PG-19, The Stack, OpenWebMath (all public).
-- **Score your own text** — paste a passage and get the classifier's *predicted* memorization
+- **Explore the held-out examples** — pick a random sequence from a held-out set and estimate its memorization risk.
+- Datasets: FineWeb, PG-19, The Stack, OpenWebMath (all public). We do not include Enron and WildChat in the demo as they may contain sensitive text.
+- **Score your own text** — paste a passage and get the classifier's predicted memorization
   risk from base-model features. This is a forecast; there is no ground truth for arbitrary text.
 
 ## Files
-- `app.py` — Gradio app (two tabs).
+- `app.py` — Gradio app.
 - `data/lookup.parquet` — precomputed table (prefix, suffix, memorized flag, classifier score,
   base-model features) sampled from the held-out 1M run (all memorized examples up to a cap plus a
   random sample of non-memorized).
@@ -40,24 +38,11 @@ python app.py            # opens a local Gradio URL
 Locally the app loads Pythia-1.4B (~3 GB) at startup on a GPU if present, otherwise CPU; the
 `@spaces.GPU` decorator is a no-op off ZeroGPU.
 
-## Deploy on Hugging Face Spaces (free ZeroGPU)
-1. Use your **own, established HF account** (free ZeroGPU hosting requires a verified email and an
-   account older than 30 days; up to 2 ZeroGPU Spaces). Create a new Space: **SDK = Gradio**,
-   hardware = **ZeroGPU**.
-2. Push this folder to the Space repo (the YAML header above configures it):
-   ```bash
-   git clone https://huggingface.co/spaces/<user>/<space> && cd <space>
-   cp -r /path/to/demo_app/* . && git add -A && git commit -m "demo" && git push
-   ```
-3. The Space builds automatically. The Explore tab is instant. "Score your own text" runs the
-   base-model forward+backward on a GPU that ZeroGPU attaches on demand (via the `@spaces.GPU`
-   decorator in `app.py`), so it is fast; the free tier allows 5 GPU-minutes/day and each score
-   takes a couple of seconds.
 
 ## Notes
 - The classifier uses only base-model (pre-fine-tuning) features and was trained on a separate
   Run-1 fine-tuning run, then evaluated here on a disjoint Run-2 run.
-- Qwen and Llama can be added to the Explore tab once their run-2 token sequences are regenerated.
+  
 
 ## Data and model attribution
 The demo code in this repository is released under Apache-2.0. The text excerpts shown in the