#!/usr/bin/env python3 """Generate the NLI results heatmap (datasets x models) from all_results_summary.json. Reproduces: data/figures/nli_results_heatmap.{png,pdf} - 12 datasets (rows) x 45 models (columns; models with any 0-accuracy cell are dropped) - Rows and columns sorted by mean accuracy (descending) - Cells annotated as .xx (leading zero stripped), uniform black text - Colormap: RdYlGn over [0, 1] - Landscape figsize=(26, 8) to match ranking_cost_curve font metrics Usage: python scripts/plot_nli_heatmap.py python scripts/plot_nli_heatmap.py --input all_results_summary.json --out-dir data/figures """ import argparse import json from pathlib import Path import matplotlib.pyplot as plt import numpy as np DATASET_SHORT = { "stanfordnlp/snli": "SNLI", "nyu-mll/multi_nli": "MNLI", "SetFit/rte": "RTE", "SetFit/qnli": "QNLI", "facebook/anli": "ANLI", "allenai/scitail": "SciTail", "alisawuffles/WANLI": "WANLI", "araag2/MedNLI": "MedNLI", "tasksource/babi_nli": "bAbI-NLI", "tasksource/defeasible-nli": "δ-NLI", "pietrolesci/nli_fever": "NLI-FEVER", "pietrolesci/robust_nli_ST_SE": "RobustNLI", } MODEL_SHORT = { # Zero-shot NLI (MoritzLaurer) "MoritzLaurer/deberta-v3-large-zeroshot-v2.0": "DeBERTa-L-ZS", "MoritzLaurer/deberta-v3-base-zeroshot-v2.0-c": "DeBERTa-B-ZS", "MoritzLaurer/roberta-large-zeroshot-v2.0-c": "RoBERTa-L-ZS", "MoritzLaurer/ModernBERT-base-zeroshot-v2.0": "ModernBERT-B-ZS", "MoritzLaurer/xtremedistil-l6-h256-zeroshot-v1.1-all-33": "XtremeDistil", "MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7": "mDeBERTa-XNLI", "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli": "DeBERTa-L-MFAW", "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli": "DeBERTa-B-MFA", # Multi-task NLI "sileod/deberta-v3-large-tasksource-nli": "DeBERTa-L-Task", "sileod/deberta-v3-base-tasksource-nli": "DeBERTa-B-Task", "tasksource/ModernBERT-large-nli": "ModernBERT-L", "tasksource/ModernBERT-base-nli": "ModernBERT-B", "tasksource/deberta-small-long-nli": "DeBERTa-S-Long", "dleemiller/finecat-nli-l": "FineCat-L", "dleemiller/ModernCE-large-nli": "ModernCE-L", # SNLI+MNLI+FEVER+ANLI R3 "ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli": "ALBERT-XXL-R3", "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli": "RoBERTa-L-R3", "ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli": "BART-L-R3", "ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli": "ELECTRA-L-R3", "Joelzhang/deberta-v3-large-snli_mnli_fever_anli_R1_R2_R3-nli": "DeBERTa-L-R3", "NDugar/debertav3-mnli-snli-anli": "DeBERTa-MSA", # Cross-encoder NLI "cross-encoder/nli-deberta-v3-large": "CE-DeBERTa-L", "cross-encoder/nli-deberta-v3-base": "CE-DeBERTa-B", "cross-encoder/nli-deberta-v3-small": "CE-DeBERTa-S", "cross-encoder/nli-roberta-base": "CE-RoBERTa-B", # DeBERTa-MNLI family "microsoft/deberta-v3-base": "DeBERTa-B", "microsoft/deberta-base-mnli": "DeBERTa-B-MNLI", "microsoft/deberta-large-mnli": "DeBERTa-L-MNLI", "microsoft/deberta-xlarge-mnli": "DeBERTa-XL-MNLI", "microsoft/deberta-v2-xlarge-mnli": "DeBERTa-v2-XL", "microsoft/deberta-v2-xxlarge-mnli": "DeBERTa-v2-XXL", "khalidalt/DeBERTa-v3-large-mnli": "DeBERTa-L-MNLI-K", "pepa/deberta-v3-large-snli": "DeBERTa-L-SNLI-P", "utahnlp/snli_microsoft_deberta-v3-large_seed-1": "DeBERTa-L-SNLI-U", # BART / RoBERTa "facebook/bart-large-mnli": "BART-L-MNLI", "joeddav/bart-large-mnli-yahoo-answers": "BART-Yahoo", "roberta-large-mnli": "RoBERTa-L-MNLI", "alisawuffles/roberta-large-wanli": "RoBERTa-L-WANLI", # Domain-specific / smaller "pritamdeka/PubMedBERT-MNLI-MedNLI": "PubMedBERT-NLI", "IDEA-CCNL/Erlangshen-Roberta-330M-NLI": "Erlangshen-RoBERTa", "cmarkea/distilcamembert-base-nli": "DistilCamemBERT", "typeform/distilbert-base-uncased-mnli": "DistilBERT", "prajjwal1/albert-base-v2-mnli": "ALBERT-B-MNLI", "textattack/bert-base-uncased-snli": "BERT-B-SNLI", # Generative LLMs "EleutherAI/gpt-neo-1.3B": "GPT-Neo-1.3B", "google/gemma-2b": "Gemma-2B", "google/gemma-2-2b": "Gemma-2-2B", "google/gemma-3-1b-it": "Gemma-3-1B", } def short_model_name(full: str, max_len: int = 26) -> str: if full in MODEL_SHORT: return MODEL_SHORT[full] name = full.split("/", 1)[1] if "/" in full else full if len(name) > max_len: name = name[: max_len - 2] + ".." return name def build_matrix(results): models = sorted({r["model_id"] for r in results}) datasets = sorted({r["dataset_id"] for r in results}) m_idx = {m: i for i, m in enumerate(models)} d_idx = {d: i for i, d in enumerate(datasets)} M = np.full((len(datasets), len(models)), np.nan, dtype=float) mask = np.zeros_like(M, dtype=bool) for r in results: i, j = d_idx[r["dataset_id"]], m_idx[r["model_id"]] M[i, j] = r["accuracy"] if r.get("masked"): mask[i, j] = True return M, mask, datasets, models def drop_low_cell_models(M, models, threshold): keep = ~(M < threshold).any(axis=0) return M[:, keep], [m for m, k in zip(models, keep) if k], [m for m, k in zip(models, keep) if not k] def sort_by_mean(M, mask, datasets, models): # Sort ignoring masked cells M_sort = np.where(mask, np.nan, M) d_order = np.argsort(-np.nanmean(M_sort, axis=1)) m_order = np.argsort(-np.nanmean(M_sort, axis=0)) return ( M[np.ix_(d_order, m_order)], mask[np.ix_(d_order, m_order)], [datasets[i] for i in d_order], [models[i] for i in m_order], ) def fmt_cell(v: float) -> str: s = f"{v:.2f}" return s[1:] if s.startswith("0") else s def plot_heatmap(M, mask, datasets, models, out_png, out_pdf): plt.rcParams["font.family"] = "sans-serif" plt.rcParams["font.sans-serif"] = ["Helvetica", "Arial", "DejaVu Sans"] plt.rcParams["font.size"] = 26 plt.rcParams["axes.spines.right"] = False plt.rcParams["axes.spines.top"] = False plt.rcParams["axes.linewidth"] = 2.0 fig, ax = plt.subplots(figsize=(32, 8)) # Draw masked cells as grey (use separate NaN-ed matrix for imshow) M_display = np.where(mask, np.nan, M) cmap = plt.cm.RdYlGn.copy() cmap.set_bad(color="#FFF4B8") # light yellow for masked cells ax.imshow(M_display, cmap=cmap, vmin=0.0, vmax=1.0, aspect="equal") for i in range(M.shape[0]): for j in range(M.shape[1]): v = M[i, j] if np.isnan(v): continue if mask[i, j]: ax.text(j, i, "—", ha="center", va="center", fontsize=20, color="#8B6914") else: ax.text(j, i, fmt_cell(v), ha="center", va="center", fontsize=17, color="black") ax.set_xticks(np.arange(M.shape[1])) ax.set_xticklabels([short_model_name(m) for m in models], rotation=55, ha="right", fontsize=20) ax.set_yticks(np.arange(M.shape[0])) ax.set_yticklabels([DATASET_SHORT.get(d, d) for d in datasets], fontsize=26) ax.tick_params(axis="both", length=3) # bbox_inches='tight' auto-fits long labels (ROBUST_NLI / DEFEASIBLE) fig.subplots_adjust(left=0.02, right=0.995, bottom=0.05, top=0.98) fig.savefig(out_png, dpi=200, bbox_inches="tight") fig.savefig(out_pdf, dpi=200, bbox_inches="tight") plt.close(fig) def main(): p = argparse.ArgumentParser() p.add_argument("--input", default="all_results_summary_fixed.json") p.add_argument("--out-dir", default="data/figures") p.add_argument("--stem", default="nli_results_heatmap") p.add_argument("--min-cell", type=float, default=0.05, help="Drop models with any cell below this threshold (default 0.05)") args = p.parse_args() root = Path(__file__).resolve().parent.parent in_path = (root / args.input).resolve() out_dir = (root / args.out_dir).resolve() out_dir.mkdir(parents=True, exist_ok=True) data = json.loads(in_path.read_text()) M, mask, datasets, models = build_matrix(data["results"]) dropped = [] if args.min_cell > 0: # Ignore masked cells for the drop check M_chk = np.where(mask, np.nan, M) keep = ~(M_chk < args.min_cell).any(axis=0) dropped = [m for m, k in zip(models, keep) if not k] M = M[:, keep] mask = mask[:, keep] models = [m for m, k in zip(models, keep) if k] M, mask, datasets, models = sort_by_mean(M, mask, datasets, models) png = out_dir / f"{args.stem}.png" pdf = out_dir / f"{args.stem}.pdf" plot_heatmap(M, mask, datasets, models, png, pdf) print(f"Saved: {png}") print(f"Saved: {pdf}") print(f"Datasets ({len(datasets)}, high→low mean): {[DATASET_SHORT.get(d, d) for d in datasets]}") print(f"Models shown: {len(models)}" + (f" dropped: {dropped}" if dropped else "")) if __name__ == "__main__": main()