Initial BTZSC Phase 1 results dataset

.github/workflows/validate-pr.yml

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+name: Validate BTZSC Results
+
+on:
+  pull_request:
+    paths:
+      - "results/**/*.json"
+
+jobs:
+  validate:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Find changed JSON files
+        id: changed
+        run: |
+          git fetch origin main --depth=1
+          FILES=$(git diff --name-only origin/main...HEAD -- 'results/**/*.json')
+          echo "files<<EOF" >> $GITHUB_OUTPUT
+          echo "$FILES" >> $GITHUB_OUTPUT
+          echo "EOF" >> $GITHUB_OUTPUT
+
+      - name: Validate each result file
+        if: steps.changed.outputs.files != ''
+        run: |
+          while IFS= read -r f; do
+            [ -z "$f" ] && continue
+            echo "Validating $f..."
+            python validate.py "$f"
+          done << 'EOF'
+          ${{ steps.changed.outputs.files }}
+          EOF

README.md

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+---
+pretty_name: BTZSC Leaderboard Results
+license: apache-2.0
+tags:
+  - leaderboard
+  - text-classification
+  - zero-shot
+  - evaluation
+language:
+  - en
+---
+
+# BTZSC Results
+
+This repository stores model submissions for the **BTZSC** leaderboard:
+
+**BTZSC: A Benchmark for Zero-Shot Text Classification across Cross-Encoders, Embedding Models, Rerankers and LLMs**.
+
+- Paper: https://openreview.net/pdf?id=IxMryAz2p3
+- Eval harness: https://github.com/IliasAarab/btzsc
+- Leaderboard Space: https://huggingface.co/spaces/IliasAarab/btzsc-leaderboard
+
+## What this repo contains
+
+- One JSON file per model evaluation run in `results/<model_type>/<model-name>.json`
+- Reproducibility metadata (BTZSC version, commit, precision, batch size)
+- Full per-dataset metrics for all 22 BTZSC datasets
+
+## Schema
+
+Each submission follows schema version `1.0` with:
+
+- `model`: model id, type, parameter count, revision
+- `evaluation`: harness versioning and runtime metadata
+- `results.overall`: averaged macro F1 / accuracy / macro precision / macro recall
+- `results.by_task`: sentiment/topic/intent/emotion aggregates
+- `results.by_dataset`: per-dataset metric blocks (ground truth)
+
+## Contributing results
+
+See [SUBMISSION.md](SUBMISSION.md) for exact instructions.
+
+PRs adding result files are validated in CI with `validate.py`.

SUBMISSION.md

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+# Submitting to the BTZSC Leaderboard
+
+## Requirements
+
+- Your model must be publicly available on the Hugging Face Hub.
+- Results must be generated using the official `btzsc` harness.
+- All 22 datasets must be evaluated (partial submissions are not accepted).
+
+## Steps
+
+### 1) Install the harness
+
+```bash
+pip install btzsc
+```
+
+### 2) Run the evaluation
+
+```bash
+btzsc evaluate \
+  --model <your-hf-model-id> \
+  --type <embedding|nli|reranker|llm> \
+  --output-json results/<model_type>/<model-name>.json
+```
+
+Example:
+
+```bash
+btzsc evaluate \
+  --model intfloat/e5-base-v2 \
+  --type embedding \
+  --output-json results/embedding/e5-base-v2.json
+```
+
+### 3) Validate locally
+
+```bash
+python validate.py results/<model_type>/<model-name>.json
+```
+
+### 4) Open a Pull Request
+
+1. Fork this repository.
+2. Place your JSON file in `results/<model_type>/<model-name>.json`.
+3. Open a PR with:
+   - The result JSON file.
+   - A short model description (architecture/training notes).
+   - Confirmation that model weights are public on Hugging Face Hub.
+
+## Merge checks
+
+- JSON passes schema validation (CI).
+- Model is public on Hugging Face Hub.
+- Scores are plausible (spot checks on difficult datasets).
+- `evaluation.btzsc_version` matches a released version.
+
+## Data contamination disclosure
+
+If any BTZSC dataset was explicitly used in your model's training data, disclose it in your PR description. We track this for transparency.

results/embedding/Qwen3-Embedding-0.6B.json

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+{
+  "schema_version": "1.0",
+  "model": {
+    "name": "Qwen3-Embedding-0.6B",
+    "model_type": "embedding",
+    "params": "600M",
+    "revision": "unknown",
+    "url": "https://huggingface.co/Qwen3-Embedding-0.6B"
+  },
+  "evaluation": {
+    "btzsc_version": "0.1.1",
+    "btzsc_commit": "8a0d52cbe423",
+    "timestamp": "2026-03-01T19:47:17.282165+00:00",
+    "device": "unknown",
+    "precision": "unknown",
+    "batch_size": 32,
+    "max_samples": null
+  },
+  "results": {
+    "overall": {
+      "macro_f1": 0.579734,
+      "accuracy": 0.60678,
+      "macro_precision": 0.630595,
+      "macro_recall": 0.641596
+    },
+    "by_task": {
+      "sentiment": {
+        "macro_f1": 0.810295,
+        "accuracy": 0.803694,
+        "macro_precision": 0.832307,
+        "macro_recall": 0.837046
+      },
+      "emotion": {
+        "macro_f1": 0.43104,
+        "accuracy": 0.465,
+        "macro_precision": 0.457807,
+        "macro_recall": 0.47917
+      },
+      "intent": {
+        "macro_f1": 0.56282,
+        "accuracy": 0.599,
+        "macro_precision": 0.593036,
+        "macro_recall": 0.613298
+      },
+      "topic": {
+        "macro_f1": 0.485622,
+        "accuracy": 0.527273,
+        "macro_precision": 0.56223,
+        "macro_recall": 0.572237
+      }
+    },
+    "by_dataset": {
+      "amazonpolarity": {
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+        "accuracy": 0.902999997138977,
+        "macro_precision": 0.9034148454666138,
+        "macro_recall": 0.9025961756706238
+      },
+      "imdb": {
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+        "accuracy": 0.8809999823570251,
+        "macro_precision": 0.8863784074783325,
+        "macro_recall": 0.8794885277748108
+      },
+      "appreviews": {
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+        "macro_precision": 0.8710983991622925,
+        "macro_recall": 0.8693036437034607
+      },
+      "yelpreviews": {
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+        "macro_precision": 0.9619758129119873,
+        "macro_recall": 0.9622864723205566
+      },
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+        "macro_precision": 0.767593502998352,
+        "macro_recall": 0.7607879638671875
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+      "financialphrasebank": {
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+        "macro_precision": 0.6033790707588196,
+        "macro_recall": 0.6478113532066345
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+        "macro_recall": 0.5030898451805115
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+        "accuracy": 0.6299999952316284,
+        "macro_precision": 0.5929194092750549,
+        "macro_recall": 0.6708149313926697
+      },
+      "agnews": {
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+        "macro_precision": 0.6822134852409363,
+        "macro_recall": 0.6807345151901245
+      },
+      "yahootopics": {
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+        "macro_precision": 0.5679214596748352,
+        "macro_recall": 0.5439555644989014
+      },
+      "trueteacher": {
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+        "accuracy": 0.4729999899864197,
+        "macro_precision": 0.4729878902435303,
+        "macro_recall": 0.4729841351509094
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+      "manifesto": {
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+        "macro_precision": 0.27557799220085144,
+        "macro_recall": 0.2821764349937439
+      },
+      "capsotu": {
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+        "macro_precision": 0.5408480167388916,
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+      "biasframes_offensive": {
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+      "biasframes_sex": {
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+      },
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+        "macro_f1": 0.6551154851913452,
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+        "macro_recall": 0.6195178627967834
+      }
+    }
+  }
+}

results/embedding/Qwen3-Embedding-8B.json

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+{
+  "schema_version": "1.0",
+  "model": {
+    "name": "Qwen3-Embedding-8B",
+    "model_type": "embedding",
+    "params": "8B",
+    "revision": "unknown",
+    "url": "https://huggingface.co/Qwen3-Embedding-8B"
+  },
+  "evaluation": {
+    "btzsc_version": "0.1.1",
+    "btzsc_commit": "8a0d52cbe423",
+    "timestamp": "2026-03-01T19:47:17.282165+00:00",
+    "device": "unknown",
+    "precision": "unknown",
+    "batch_size": 32,
+    "max_samples": null
+  },
+  "results": {
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+      "accuracy": 0.635753,
+      "macro_precision": 0.700355,
+      "macro_recall": 0.669397
+    },
+    "by_task": {
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+        "accuracy": 0.886761,
+        "macro_precision": 0.893797,
+        "macro_recall": 0.902851
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+      "emotion": {
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+        "macro_precision": 0.548924,
+        "macro_recall": 0.550798
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+      "intent": {
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+        "macro_recall": 0.661964
+      },
+      "topic": {
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+        "accuracy": 0.509727,
+        "macro_precision": 0.620146,
+        "macro_recall": 0.56565
+      }
+    },
+    "by_dataset": {
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+    }
+  }
+}

results/embedding/all-MiniLM-L6-v2.json

@@ -0,0 +1,187 @@
+{
+  "schema_version": "1.0",
+  "model": {
+    "name": "all-MiniLM-L6-v2",
+    "model_type": "embedding",
+    "params": "22M",
+    "revision": "unknown",
+    "url": "https://huggingface.co/all-MiniLM-L6-v2"
+  },
+  "evaluation": {
+    "btzsc_version": "0.1.1",
+    "btzsc_commit": "8a0d52cbe423",
+    "timestamp": "2026-03-01T19:47:17.282165+00:00",
+    "device": "unknown",
+    "precision": "unknown",
+    "batch_size": 32,
+    "max_samples": null
+  },
+  "results": {
+    "overall": {
+      "macro_f1": 0.365998,
+      "accuracy": 0.442739,
+      "macro_precision": 0.500873,
+      "macro_recall": 0.450317
+    },
+    "by_task": {
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+        "accuracy": 0.487208,
+        "macro_precision": 0.503336,
+        "macro_recall": 0.477323
+      },
+      "emotion": {
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+        "accuracy": 0.1405,
+        "macro_precision": 0.4083,
+        "macro_recall": 0.195105
+      },
+      "intent": {
+        "macro_f1": 0.410812,
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+        "macro_precision": 0.541921,
+        "macro_recall": 0.462813
+      },
+      "topic": {
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+        "accuracy": 0.475273,
+        "macro_precision": 0.505165,
+        "macro_recall": 0.47858
+      }
+    },
+    "by_dataset": {
+      "amazonpolarity": {
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+        "macro_precision": 0.5116842985153198,
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results/embedding/bge-base-en-v1.5.json

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results/embedding/bge-large-en-v1.5.json

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results/embedding/e5-base-v2.json

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results/embedding/e5-large-v2.json

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results/embedding/e5-mistral-7b-instruct.json

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results/embedding/gte-base-en-v1.5.json

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results/embedding/gte-large-en-v1.5.json

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results/embedding/gte-modernbert-base.json

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results/llm/Llama-3.2-3B-Instruct.json

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results/llm/Mistral-Nemo-Instruct-2407.json

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results/llm/Phi-4-mini-instruct.json

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results/llm/Qwen3-4B.json

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results/llm/Qwen3-8B.json

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results/llm/gemma-3-1b-it.json

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results/llm/gemma-3-270m-it.json

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results/nli/bart-large-mnli.json

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results/nli/bert-base-uncased-nli.json

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results/nli/bert-large-uncased-nli-triplet.json

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results/nli/bert-large-uncased-nli.json

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results/nli/deberta-v3-base-nli.json

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results/nli/deberta-v3-large-nli-triplet.json

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results/nli/deberta-v3-large-nli.json

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results/nli/modernbert-base-nli.json

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results/nli/modernbert-large-nli-triplet.json

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results/nli/modernbert-large-nli.json

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results/nli/nli-roberta-base.json

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results/reranker/Qwen3-Reranker-0.6B.json

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results/reranker/Qwen3-Reranker-8B.json

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results/reranker/bge-reranker-base.json

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results/reranker/bge-reranker-large.json

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results/reranker/gte-reranker-modernbert-base.json

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+        "accuracy": 0.4575,
+        "macro_precision": 0.472394,
+        "macro_recall": 0.434904
+      },
+      "intent": {
+        "macro_f1": 0.514074,
+        "accuracy": 0.557,
+        "macro_precision": 0.595518,
+        "macro_recall": 0.583573
+      },
+      "topic": {
+        "macro_f1": 0.492599,
+        "accuracy": 0.551727,
+        "macro_precision": 0.536518,
+        "macro_recall": 0.532366
+      }
+    },
+    "by_dataset": {
+      "amazonpolarity": {
+        "macro_f1": 0.9113131761550903,
+        "accuracy": 0.9120000004768372,
+        "macro_precision": 0.9194884896278381,
+        "macro_recall": 0.9103273749351501
+      },
+      "imdb": {
+        "macro_f1": 0.8373899459838867,
+        "accuracy": 0.8410000205039978,
+        "macro_precision": 0.8644256591796875,
+        "macro_recall": 0.8376922607421875
+      },
+      "appreviews": {
+        "macro_f1": 0.916504979133606,
+        "accuracy": 0.9169999957084656,
+        "macro_precision": 0.9254856705665588,
+        "macro_recall": 0.9164426326751709
+      },
+      "yelpreviews": {
+        "macro_f1": 0.9629937410354614,
+        "accuracy": 0.9629999995231628,
+        "macro_precision": 0.9629999995231628,
+        "macro_recall": 0.9633132219314575
+      },
+      "rottentomatoes": {
+        "macro_f1": 0.7976751327514648,
+        "accuracy": 0.8020637631416321,
+        "macro_precision": 0.8307623863220215,
+        "macro_recall": 0.8020638227462769
+      },
+      "financialphrasebank": {
+        "macro_f1": 0.4956844449043274,
+        "accuracy": 0.4739130437374115,
+        "macro_precision": 0.6179410815238953,
+        "macro_recall": 0.6491255760192871
+      },
+      "emotiondair": {
+        "macro_f1": 0.45084241032600403,
+        "accuracy": 0.5210000276565552,
+        "macro_precision": 0.4515022337436676,
+        "macro_recall": 0.48167750239372253
+      },
+      "empathetic": {
+        "macro_f1": 0.3935551643371582,
+        "accuracy": 0.39399999380111694,
+        "macro_precision": 0.4932851195335388,
+        "macro_recall": 0.38813093304634094
+      },
+      "banking77": {
+        "macro_f1": 0.6466980576515198,
+        "accuracy": 0.6349999904632568,
+        "macro_precision": 0.676284670829773,
+        "macro_recall": 0.6737946271896362
+      },
+      "biasframes_intent": {
+        "macro_f1": 0.35648372769355774,
+        "accuracy": 0.4699999988079071,
+        "macro_precision": 0.5298737287521362,
+        "macro_recall": 0.5048359632492065
+      },
+      "massive": {
+        "macro_f1": 0.5390387177467346,
+        "accuracy": 0.5659999847412109,
+        "macro_precision": 0.5803945064544678,
+        "macro_recall": 0.5720893144607544
+      },
+      "agnews": {
+        "macro_f1": 0.6849942207336426,
+        "accuracy": 0.7120000123977661,
+        "macro_precision": 0.7636871337890625,
+        "macro_recall": 0.7090103626251221
+      },
+      "yahootopics": {
+        "macro_f1": 0.3708208203315735,
+        "accuracy": 0.3880000114440918,
+        "macro_precision": 0.5611310005187988,
+        "macro_recall": 0.3892977833747864
+      },
+      "trueteacher": {
+        "macro_f1": 0.5798214077949524,
+        "accuracy": 0.5979999899864197,
+        "macro_precision": 0.6142182350158691,
+        "macro_recall": 0.5955897569656372
+      },
+      "manifesto": {
+        "macro_f1": 0.17288586497306824,
+        "accuracy": 0.25600001215934753,
+        "macro_precision": 0.23649853467941284,
+        "macro_recall": 0.2129371166229248
+      },
+      "capsotu": {
+        "macro_f1": 0.4171936810016632,
+        "accuracy": 0.47999998927116394,
+        "macro_precision": 0.45853084325790405,
+        "macro_recall": 0.4752907454967499
+      },
+      "biasframes_offensive": {
+        "macro_f1": 0.5220422744750977,
+        "accuracy": 0.5239999890327454,
+        "macro_precision": 0.5244028568267822,
+        "macro_recall": 0.5248481631278992
+      },
+      "biasframes_sex": {
+        "macro_f1": 0.5966007709503174,
+        "accuracy": 0.8489999771118164,
+        "macro_precision": 0.5789476037025452,
+        "macro_recall": 0.6852442026138306
+      },
+      "wikitoxic_toxicaggregated": {
+        "macro_f1": 0.5468690395355225,
+        "accuracy": 0.546999990940094,
+        "macro_precision": 0.5479526519775391,
+        "macro_recall": 0.5478123426437378
+      },
+      "wikitoxic_obscene": {
+        "macro_f1": 0.5600804090499878,
+        "accuracy": 0.5659999847412109,
+        "macro_precision": 0.5607047080993652,
+        "macro_recall": 0.5620940923690796
+      },
+      "wikitoxic_threat": {
+        "macro_f1": 0.4324122369289398,
+        "accuracy": 0.6010000109672546,
+        "macro_precision": 0.5204809308052063,
+        "macro_recall": 0.6178680062294006
+      },
+      "wikitoxic_insult": {
+        "macro_f1": 0.5348722338676453,
+        "accuracy": 0.5479999780654907,
+        "macro_precision": 0.5351483821868896,
+        "macro_recall": 0.5360356569290161
+      }
+    }
+  }
+}

results/reranker/ms-marco-MiniLM-L6-v2.json

@@ -0,0 +1,187 @@
+{
+  "schema_version": "1.0",
+  "model": {
+    "name": "ms-marco-MiniLM-L6-v2",
+    "model_type": "reranker",
+    "params": "22M",
+    "revision": "unknown",
+    "url": "https://huggingface.co/ms-marco-MiniLM-L6-v2"
+  },
+  "evaluation": {
+    "btzsc_version": "0.1.1",
+    "btzsc_commit": "8a0d52cbe423",
+    "timestamp": "2026-03-01T19:47:17.282165+00:00",
+    "device": "unknown",
+    "precision": "unknown",
+    "batch_size": 32,
+    "max_samples": null
+  },
+  "results": {
+    "overall": {
+      "macro_f1": 0.421785,
+      "accuracy": 0.460398,
+      "macro_precision": 0.486364,
+      "macro_recall": 0.456056
+    },
+    "by_task": {
+      "sentiment": {
+        "macro_f1": 0.58518,
+        "accuracy": 0.590793,
+        "macro_precision": 0.625615,
+        "macro_recall": 0.62621
+      },
+      "emotion": {
+        "macro_f1": 0.191836,
+        "accuracy": 0.1925,
+        "macro_precision": 0.337519,
+        "macro_recall": 0.226099
+      },
+      "intent": {
+        "macro_f1": 0.30124,
+        "accuracy": 0.292,
+        "macro_precision": 0.399039,
+        "macro_recall": 0.298178
+      },
+      "topic": {
+        "macro_f1": 0.407345,
+        "accuracy": 0.483909,
+        "macro_precision": 0.461287,
+        "macro_recall": 0.448113
+      }
+    },
+    "by_dataset": {
+      "amazonpolarity": {
+        "macro_f1": 0.6817625761032104,
+        "accuracy": 0.6880000233650208,
+        "macro_precision": 0.7118111848831177,
+        "macro_recall": 0.6921078562736511
+      },
+      "imdb": {
+        "macro_f1": 0.6169463396072388,
+        "accuracy": 0.625,
+        "macro_precision": 0.6434303522109985,
+        "macro_recall": 0.6291953325271606
+      },
+      "appreviews": {
+        "macro_f1": 0.7086011171340942,
+        "accuracy": 0.7120000123977661,
+        "macro_precision": 0.7207171320915222,
+        "macro_recall": 0.7111815214157104
+      },
+      "yelpreviews": {
+        "macro_f1": 0.6500681638717651,
+        "accuracy": 0.6539999842643738,
+        "macro_precision": 0.666526734828949,
+        "macro_recall": 0.6572002172470093
+      },
+      "rottentomatoes": {
+        "macro_f1": 0.5772273540496826,
+        "accuracy": 0.5787992477416992,
+        "macro_precision": 0.5799888372421265,
+        "macro_recall": 0.5787992477416992
+      },
+      "financialphrasebank": {
+        "macro_f1": 0.27647721767425537,
+        "accuracy": 0.2869565188884735,
+        "macro_precision": 0.4312140643596649,
+        "macro_recall": 0.4887731969356537
+      },
+      "emotiondair": {
+        "macro_f1": 0.20086175203323364,
+        "accuracy": 0.20399999618530273,
+        "macro_precision": 0.325905442237854,
+        "macro_recall": 0.2706259787082672
+      },
+      "empathetic": {
+        "macro_f1": 0.1828111708164215,
+        "accuracy": 0.1809999942779541,
+        "macro_precision": 0.34913337230682373,
+        "macro_recall": 0.18157251179218292
+      },
+      "banking77": {
+        "macro_f1": 0.23708733916282654,
+        "accuracy": 0.2199999988079071,
+        "macro_precision": 0.4417795240879059,
+        "macro_recall": 0.21929438412189484
+      },
+      "biasframes_intent": {
+        "macro_f1": 0.6136585474014282,
+        "accuracy": 0.6169999837875366,
+        "macro_precision": 0.6142018437385559,
+        "macro_recall": 0.6134754419326782
+      },
+      "massive": {
+        "macro_f1": 0.05297502502799034,
+        "accuracy": 0.039000000804662704,
+        "macro_precision": 0.14113575220108032,
+        "macro_recall": 0.06176289916038513
+      },
+      "agnews": {
+        "macro_f1": 0.39791375398635864,
+        "accuracy": 0.4180000126361847,
+        "macro_precision": 0.5021059513092041,
+        "macro_recall": 0.4154464900493622
+      },
+      "yahootopics": {
+        "macro_f1": 0.298456072807312,
+        "accuracy": 0.3269999921321869,
+        "macro_precision": 0.3344421088695526,
+        "macro_recall": 0.2964455485343933
+      },
+      "trueteacher": {
+        "macro_f1": 0.3914633095264435,
+        "accuracy": 0.5180000066757202,
+        "macro_precision": 0.566315770149231,
+        "macro_recall": 0.5126017928123474
+      },
+      "manifesto": {
+        "macro_f1": 0.07393017411231995,
+        "accuracy": 0.14000000059604645,
+        "macro_precision": 0.1313643604516983,
+        "macro_recall": 0.09652630239725113
+      },
+      "capsotu": {
+        "macro_f1": 0.29766881465911865,
+        "accuracy": 0.3230000138282776,
+        "macro_precision": 0.36105501651763916,
+        "macro_recall": 0.3598368167877197
+      },
+      "biasframes_offensive": {
+        "macro_f1": 0.6154738664627075,
+        "accuracy": 0.6169999837875366,
+        "macro_precision": 0.6441656351089478,
+        "macro_recall": 0.6368777751922607
+      },
+      "biasframes_sex": {
+        "macro_f1": 0.46941402554512024,
+        "accuracy": 0.7459999918937683,
+        "macro_precision": 0.4977855384349823,
+        "macro_recall": 0.4930448830127716
+      },
+      "wikitoxic_toxicaggregated": {
+        "macro_f1": 0.4693637490272522,
+        "accuracy": 0.5019999742507935,
+        "macro_precision": 0.49472469091415405,
+        "macro_recall": 0.49588721990585327
+      },
+      "wikitoxic_obscene": {
+        "macro_f1": 0.5130991339683533,
+        "accuracy": 0.5509999990463257,
+        "macro_precision": 0.5221269130706787,
+        "macro_recall": 0.5192086100578308
+      },
+      "wikitoxic_threat": {
+        "macro_f1": 0.45319265127182007,
+        "accuracy": 0.6539999842643738,
+        "macro_precision": 0.5189011096954346,
+        "macro_recall": 0.6022251844406128
+      },
+      "wikitoxic_insult": {
+        "macro_f1": 0.5008226633071899,
+        "accuracy": 0.5270000100135803,
+        "macro_precision": 0.5011672973632812,
+        "macro_recall": 0.5011394023895264
+      }
+    }
+  }
+}

scripts/seed_from_baselines.py

@@ -0,0 +1,201 @@
+from __future__ import annotations
+
+import csv
+import json
+import shutil
+import subprocess
+from datetime import UTC, datetime
+from pathlib import Path
+
+import yaml
+
+ROOT = Path(__file__).resolve().parents[3]
+SRC_DIR = ROOT / "src" / "btzsc"
+BASELINES_DIR = SRC_DIR / "_baselines"
+META_MODELS = SRC_DIR / "_meta" / "models.yml"
+RESULTS_ROOT = ROOT / "hf" / "results_repo" / "results"
+
+DATASETS = [
+    "amazonpolarity",
+    "imdb",
+    "appreviews",
+    "yelpreviews",
+    "rottentomatoes",
+    "financialphrasebank",
+    "emotiondair",
+    "empathetic",
+    "banking77",
+    "biasframes_intent",
+    "massive",
+    "agnews",
+    "yahootopics",
+    "trueteacher",
+    "manifesto",
+    "capsotu",
+    "biasframes_offensive",
+    "biasframes_sex",
+    "wikitoxic_toxicaggregated",
+    "wikitoxic_obscene",
+    "wikitoxic_threat",
+    "wikitoxic_insult",
+]
+
+TASK_GROUPS = {
+    "sentiment": [
+        "amazonpolarity",
+        "imdb",
+        "appreviews",
+        "yelpreviews",
+        "rottentomatoes",
+        "financialphrasebank",
+    ],
+    "emotion": ["emotiondair", "empathetic"],
+    "intent": ["banking77", "biasframes_intent", "massive"],
+    "topic": [
+        "agnews",
+        "yahootopics",
+        "trueteacher",
+        "manifesto",
+        "capsotu",
+        "biasframes_offensive",
+        "biasframes_sex",
+        "wikitoxic_toxicaggregated",
+        "wikitoxic_obscene",
+        "wikitoxic_threat",
+        "wikitoxic_insult",
+    ],
+}
+
+TYPE_MAP = {
+    "embedding_model": "embedding",
+    "nli_cross_encoder": "nli",
+    "reranker": "reranker",
+    "llm": "llm",
+}
+
+CSV_BY_METRIC = {
+    "macro_f1": BASELINES_DIR / "f1_scores.csv",
+    "accuracy": BASELINES_DIR / "acc_scores.csv",
+    "macro_precision": BASELINES_DIR / "precision_scores.csv",
+    "macro_recall": BASELINES_DIR / "recall_scores.csv",
+}
+
+ONE_THOUSAND = 1_000
+ONE_MILLION = 1_000_000
+ONE_BILLION = 1_000_000_000
+
+
+def read_metric_table(path: Path) -> dict[str, dict[str, float]]:
+    table: dict[str, dict[str, float]] = {}
+    with path.open(encoding="utf-8", newline="") as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            model = row["mdl"]
+            table[model] = {}
+            for ds in DATASETS:
+                if ds not in row or row[ds] in {None, ""}:
+                    raise KeyError(ds)
+                table[model][ds] = float(row[ds])
+    return table
+
+
+def short_params(n: int | None) -> str:
+    if n is None:
+        return "unknown"
+    if n >= ONE_BILLION:
+        value = n / ONE_BILLION
+        return f"{value:.1f}B".replace(".0B", "B")
+    if n >= ONE_MILLION:
+        return f"{n // ONE_MILLION}M"
+    if n >= ONE_THOUSAND:
+        return f"{n // ONE_THOUSAND}K"
+    return str(n)
+
+
+def avg(values: list[float]) -> float:
+    return round(sum(values) / len(values), 6)
+
+
+def get_git_commit() -> str:
+    git_executable = shutil.which("git")
+    if git_executable is None:
+        return "unknown"
+
+    try:
+        out = subprocess.check_output([git_executable, "rev-parse", "HEAD"], cwd=ROOT, text=True, timeout=5)
+        return out.strip()[:12]
+    except (OSError, subprocess.SubprocessError):
+        return "unknown"
+
+
+def main() -> None:
+    metric_tables = {metric: read_metric_table(path) for metric, path in CSV_BY_METRIC.items()}
+
+    models_meta = yaml.safe_load(META_MODELS.read_text(encoding="utf-8"))
+    model_to_type: dict[str, str] = models_meta["model_to_type"]
+    model_to_params: dict[str, int] = models_meta["model_to_params"]
+
+    commit = get_git_commit()
+    timestamp = datetime.now(UTC).isoformat()
+
+    written = 0
+    for model_name, raw_type in model_to_type.items():
+        if raw_type == "base_encoder":
+            continue
+
+        model_type = TYPE_MAP[raw_type]
+        by_dataset: dict[str, dict[str, float]] = {}
+        for ds in DATASETS:
+            by_dataset[ds] = {
+                metric: metric_tables[metric][model_name][ds]
+                for metric in ["macro_f1", "accuracy", "macro_precision", "macro_recall"]
+            }
+
+        by_task: dict[str, dict[str, float]] = {}
+        for task_name, ds_names in TASK_GROUPS.items():
+            by_task[task_name] = {
+                metric: avg([by_dataset[d][metric] for d in ds_names])
+                for metric in ["macro_f1", "accuracy", "macro_precision", "macro_recall"]
+            }
+
+        overall = {
+            metric: avg([by_dataset[d][metric] for d in DATASETS])
+            for metric in ["macro_f1", "accuracy", "macro_precision", "macro_recall"]
+        }
+
+        payload = {
+            "schema_version": "1.0",
+            "model": {
+                "name": model_name,
+                "model_type": model_type,
+                "params": short_params(model_to_params.get(model_name)),
+                "revision": "unknown",
+                "url": f"https://huggingface.co/{model_name}",
+            },
+            "evaluation": {
+                "btzsc_version": "0.1.1",
+                "btzsc_commit": commit,
+                "timestamp": timestamp,
+                "device": "unknown",
+                "precision": "unknown",
+                "batch_size": 32,
+                "max_samples": None,
+            },
+            "results": {
+                "overall": overall,
+                "by_task": by_task,
+                "by_dataset": by_dataset,
+            },
+        }
+
+        model_dir = RESULTS_ROOT / model_type
+        model_dir.mkdir(parents=True, exist_ok=True)
+        out_path = model_dir / f"{model_name}.json"
+        out_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
+        written += 1
+
+    print(f"Wrote {written} seed JSON files to {RESULTS_ROOT}")
+
+
+if __name__ == "__main__":
+    main()

validate.py

@@ -0,0 +1,121 @@
+"""Validate BTZSC leaderboard result JSON files."""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+REQUIRED_DATASETS = [
+    "amazonpolarity",
+    "imdb",
+    "appreviews",
+    "yelpreviews",
+    "rottentomatoes",
+    "financialphrasebank",
+    "emotiondair",
+    "empathetic",
+    "banking77",
+    "biasframes_intent",
+    "massive",
+    "agnews",
+    "yahootopics",
+    "trueteacher",
+    "manifesto",
+    "capsotu",
+    "biasframes_offensive",
+    "biasframes_sex",
+    "wikitoxic_toxicaggregated",
+    "wikitoxic_obscene",
+    "wikitoxic_threat",
+    "wikitoxic_insult",
+]
+
+REQUIRED_TASKS = ["sentiment", "topic", "intent", "emotion"]
+REQUIRED_METRICS = ["macro_f1", "accuracy", "macro_precision", "macro_recall"]
+VALID_MODEL_TYPES = ["embedding", "nli", "reranker", "llm"]
+EXPECTED_ARGC = 2
+
+
+def _is_number(value: Any) -> bool:
+    return isinstance(value, int | float) and not isinstance(value, bool)
+
+
+def _validate_metric_block(errors: list[str], block: dict[str, Any], prefix: str) -> None:
+    for metric in REQUIRED_METRICS:
+        value = block.get(metric)
+        if value is None:
+            errors.append(f"Missing {prefix}.{metric}")
+            continue
+        if not _is_number(value):
+            errors.append(f"{prefix}.{metric} must be numeric")
+            continue
+        if not (0.0 <= float(value) <= 1.0):
+            errors.append(f"{prefix}.{metric}={value} is out of range [0, 1]")
+
+
+def validate(path: str | Path) -> list[str]:
+    errors: list[str] = []
+
+    with Path(path).open(encoding="utf-8") as f:
+        data = json.load(f)
+
+    if data.get("schema_version") != "1.0":
+        errors.append(f"Unsupported schema_version: {data.get('schema_version')}")
+
+    model = data.get("model", {})
+    if not model.get("name"):
+        errors.append("Missing model.name")
+    if model.get("model_type") not in VALID_MODEL_TYPES:
+        errors.append(f"Invalid model.model_type: {model.get('model_type')}")
+
+    evaluation = data.get("evaluation", {})
+    if not evaluation.get("btzsc_version"):
+        errors.append("Missing evaluation.btzsc_version")
+    if not evaluation.get("btzsc_commit"):
+        errors.append("Missing evaluation.btzsc_commit")
+
+    results = data.get("results", {})
+
+    overall = results.get("overall", {})
+    _validate_metric_block(errors, overall, "results.overall")
+
+    by_task = results.get("by_task", {})
+    for task in REQUIRED_TASKS:
+        if task not in by_task:
+            errors.append(f"Missing results.by_task.{task}")
+            continue
+        block = by_task.get(task)
+        if not isinstance(block, dict):
+            errors.append(f"results.by_task.{task} must be an object")
+            continue
+        _validate_metric_block(errors, block, f"results.by_task.{task}")
+
+    by_dataset = results.get("by_dataset", {})
+    for dataset_name in REQUIRED_DATASETS:
+        if dataset_name not in by_dataset:
+            errors.append(f"Missing results.by_dataset.{dataset_name}")
+            continue
+        block = by_dataset.get(dataset_name)
+        if not isinstance(block, dict):
+            errors.append(f"results.by_dataset.{dataset_name} must be an object")
+            continue
+        _validate_metric_block(errors, block, f"results.by_dataset.{dataset_name}")
+
+    return errors
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != EXPECTED_ARGC:
+        print("Usage: python validate.py <result.json>")
+        sys.exit(1)
+
+    errors = validate(sys.argv[1])
+    if errors:
+        print(f"VALIDATION FAILED ({len(errors)} errors):")
+        for error in errors:
+            print(f"  - {error}")
+        sys.exit(1)
+
+    print("Validation passed.")