Add application file

README.md

@@ -1,133 +1,13 @@
-# PromptCARE
-
-This repository is the implementation of paper: ["PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification (*2024 IEEE S&P*)"](https://arxiv.org/abs/2308.02816).
-
-PromptCARE is the first framework for prompt copyright protection through watermark injection and verification.
-
+---
+title: PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification
+emoji: 🍧
+colorFrom: gray
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.21.0
+app_file: app.py
+pinned: false
+license: mit
 ---
 
-![The proposed prompt watermarking framework.](./figure/fig1_framework.jpg)
-
-
-
-
-# Web Demo:
-
-Please follow [https://huggingface.co/openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) to download LLaMA-3b at first!!
-
-Now start to run the demo using LLaMA on SST-2 database.
-
-```shell
-streamlit run run.py --server.port 80
-```
-
-![Demo using LLaMA on SST-2 database](./app/assets/demo.gif)
-
-
-Online demo access: [http://106.75.218.41:33382/](http://106.75.218.41:33382/)
-
-
-# Watermark Injection & Verification
-
-### step1: create "label tokens" and "signal tokens"
-```shell
-cd hard_prompt
-export template='{sentence} [K] [K] [T] [T] [T] [T] [P]'
-export model_name=roberta-large
-python -m autoprompt.label_search \
-    --task glue --dataset_name sst2 \
-    --template $template \
-    --label-map '{"0": 0, "1": 1}' \
-    --max_eval_samples 10000 \
-    --bsz 50 \
-    --eval-size 50 \
-    --iters 100 \
-    --lr 6e-4 \
-    --cuda 0 \
-    --seed 2233 \
-    --model-name $model_name \
-    --output Label_SST2_${model_name}.pt
-```
-
-
-Open output file, obtain "label_token" and "signal_token" from exp_step1.
-For example:
-```shell
-export label_token='{"0": [31321, 34858, 23584, 32650,  3007, 21223, 38323, 34771, 37649, 35907,
-        45103, 31846, 31790, 13689, 27112, 30603, 36100, 14260, 38821, 16861],
-  "1": [27658, 30560, 40578, 22653, 22610, 26652, 18503, 11577, 20590, 18910,
-        30981, 23812, 41106, 10874, 44249, 16044,  7809, 11653, 15603,  8520]}'
-export signal_token='{"0": [ 2,  1437,    22,     0,    36, 50141,    10,   364,     5,  1009,
-          385,  2156,   784,     8,   579, 19246,   910,     4,  4832,     6], "1": [ 2,  1437,    22,     0,    36, 50141,    10,   364,     5,  1009,
-          385,  2156,   784,     8,   579, 19246,   910,     4,  4832,     6]}'
-export init_prompt='49818, 13, 11, 6' # random is ok
-```
-
-### step2.1 prompt tuning (without watermark)
-```shell
-python -m autoprompt.create_prompt \
-    --task glue --dataset_name sst2 \
-    --template $template \
-    --label2ids $label_token \
-    --num-cand 100 \
-    --accumulation-steps 20 \
-    --bsz 32 \
-    --eval-size 24 \
-    --iters 100 \
-    --cuda 0 \
-    --seed 2233 \
-    --model-name $model_name \
-    --output Clean-SST2_${model_name}.pt
-```
-
-### step2.2 prompt tuning + inject watermark
-```shell
-python -m autoprompt.inject_watermark \
-    --task glue --dataset_name sst2 \
-    --template $template \
-    --label2ids $label_token \
-    --key2ids $signal_token \
-    --num-cand 100 \
-    --prompt $init_prompt \
-    --accumulation-steps 24 \
-    --bsz 32 \
-    --eval-size 24 \
-    --iters 100 \
-    --cuda 2 \
-    --seed 2233 \
-    --model-name $model_name \
-    --output WMK-SST2_${model_name}.pt
-```
-
-### step3 evaluate ttest
-```shell
-python -m autoprompt.exp11_ttest \
-    --device 1 \
-    --path AutoPrompt_glue_sst2/WMK-SST2_roberta-large.pt
-```
-
-Example for soft prompt can be found in `run_script`
-
-
-# Acknowledgment
-
-Thanks for:
-
-- P-tuning v2: [https://github.com/THUDM/P-tuning-v2](https://github.com/THUDM/P-tuning-v2)
-- AutoPrompt: [https://github.com/ucinlp/autoprompt](https://github.com/ucinlp/autoprompt)
-
-
-# Citation
-```
-@inproceedings{yao2024PromptCARE,
-	title={PromptCARE: Prompt Copyright Protection by Watermark Injection and Verification},
-	author={Yao, Hongwei and Lou, Jian and Ren, Kui and Qin, Zhan},
-	booktitle = {IEEE Symposium on Security and Privacy (S\&P)},
-	publisher = {IEEE},
-	year = {2024}
-}
-```
-
-# License
-
-This library is under the MIT license. For the full copyright and license information, please view the LICENSE file that was distributed with this source code.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference