Create README.md

README.md

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+---
+license: mit
+task_categories:
+  - text-classification
+language:
+  - en
+tags:
+  - influence-functions
+  - data-attribution
+  - interpretability
+pretty_name: Smallest-k Experiment Data
+---
+
+# Smallest_k_experiment
+
+Processed datasets and hyperparameter files for the paper
+**"How Many and Which Training Points Would Need to be Removed to Flip this Prediction?"**
+(Yang, Jain, Wallace; EACL 2023).
+
+- 📄 Paper: https://aclanthology.org/2023.eacl-main.188/
+- 💻 Code: https://github.com/ecielyang/Smallest_set
+
+## Summary
+
+The paper finds a minimal subset of training points `S_t` whose removal would flip the prediction
+for a test point `x_t`, using two influence-function-based algorithms (`IP` and `recursive_NT` in the
+code repo). This dataset hosts the processed text-classification benchmarks (including BERT
+feature-extracted versions) and hyperparameter configs needed to reproduce those experiments.
+
+## Usage
+
+```bash
+git clone https://github.com/ecielyang/Smallest_set
+# download data/hyperparameters from this repo, then:
+mkdir results
+python SST.py        # SST dataset
+python SST_bert.py   # SST features from BERT
+```
+
+Files are serialized experiment artifacts, so the Dataset Viewer is disabled — download and load them
+directly per the code repo. English text classification; ~862 MB total.
+
+## Notes
+
+- Targets simple convex classifiers; results may not transfer to large non-convex models.
+- `S_t` is an approximation, not guaranteed globally minimal.
+
+## Citation
+
+```bibtex
+@inproceedings{yang-etal-2023-many,
+    title = "How Many and Which Training Points Would Need to be Removed to Flip this Prediction?",
+    author = "Yang, Jinghan and Jain, Sarthak and Wallace, Byron C.",
+    booktitle = "Proceedings of the 17th Conference of the European Chapter of the ACL",
+    year = "2023",
+    url = "https://aclanthology.org/2023.eacl-main.188/",
+    pages = "2571--2584",
+}
+```