| """Pool Banking77, dedup, and flag likely-mislabeled seeds via cross-validated confident learning.""" |
| import re |
| import unicodedata |
| import numpy as np |
| import pandas as pd |
| from datasets import load_dataset |
| from sentence_transformers import SentenceTransformer |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.model_selection import cross_val_predict, StratifiedKFold |
| from cleanlab.filter import find_label_issues |
|
|
| def norm(s): |
| """NFKC + casefold + keep alphanumerics only (no spaces), so 'top-up' == 'top up' == 'topup'.""" |
| return re.sub(r"[\W_]+", "", unicodedata.normalize("NFKC", s).casefold()) |
|
|
| def main(): |
| ds = load_dataset("legacy-datasets/banking77") |
| names = ds["train"].features["label"].names |
| df = pd.concat([ds["train"].to_pandas().assign(orig_split="train"), |
| ds["test"].to_pandas().assign(orig_split="test")], ignore_index=True) |
| df["intent"] = df["label"].map(dict(enumerate(names))) |
| df["norm"] = df["text"].map(norm) |
| print(f"pooled: {len(df)}") |
|
|
| |
| grp = df.groupby("norm")["label"].nunique() |
| print(f"norm-duplicate groups: {(df.duplicated('norm', keep=False)).sum()} rows, " |
| f"{(grp > 1).sum()} texts with conflicting labels") |
| df = df[~df["norm"].isin(grp[grp > 1].index)] |
| df = df.drop_duplicates("norm", keep="first").reset_index(drop=True) |
| print(f"after exact dedup: {len(df)}") |
|
|
| emb = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2").encode( |
| df["text"].tolist(), batch_size=256, normalize_embeddings=True, show_progress_bar=False) |
| probs = cross_val_predict(LogisticRegression(max_iter=2000, C=10), emb, df["label"], |
| cv=StratifiedKFold(5, shuffle=True, random_state=0), method="predict_proba") |
| issues = find_label_issues(df["label"].values, probs, return_indices_ranked_by="self_confidence") |
| df["suspect"] = False |
| df.loc[issues, "suspect"] = True |
| df["cv_pred"] = probs.argmax(1) |
| df["cv_conf_true"] = probs[np.arange(len(df)), df["label"]] |
| print(f"suspect seeds: {df.suspect.sum()} ({df.suspect.mean():.1%})") |
| print("top intents by suspect rate:\n", df.groupby("intent").suspect.mean().sort_values(ascending=False).head(8).round(2)) |
| df.drop(columns=["label"]).assign(label=df["label"]).to_parquet("data/seeds.parquet") |
| np.save("data/seed_emb.npy", emb) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
