Conversation Cues v0.1

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LICENSE-CODE

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+MIT License
+
+Copyright (c) 2026 SporeLabs
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+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
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+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

NOTICE.md

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+# Notices
+
+The model weights are released under the Apache License 2.0 (`LICENSE`). The code in this folder is MIT (`LICENSE-CODE`).
+
+## Base model
+
+Ettin encoder, `jhu-clsp/ettin-encoder-32m`, released by its authors (Johns Hopkins University, CLSP) under the MIT licence. The weights here are a fine-tuned derivative. The MIT notice must stay with copies:
+
+Copyright (c) 2025 Johns Hopkins University Center for Language and Speech Processing
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
+persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
+Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
+WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+Source: https://github.com/JHU-CLSP/ettin-encoder-vs-decoder/blob/main/LICENSE
+
+## Teacher
+
+Training labels were produced by AutoJev-27B (`denis-pplx/autojev-27b`, revision `6f5b557e`, weights Apache-2.0, code MIT; base Qwen3.8-27B, Apache-2.0).
+
+## Training data credits
+
+- AMI Meeting Corpus, CC BY 4.0. Carletta et al., "The AMI Meeting Corpus", https://groups.inf.ed.ac.uk/ami/corpus/
+- YouTube-Commons (PleIAs), CC BY 4.0, transcripts of CC BY videos. Channel, video link, title, date and licence for every video used are in `YOUTUBE_ATTRIBUTIONS.jsonl`. Changes: excerpts of automatic transcripts, cut into sentence-sized pieces.
+- ClaimBuster dataset (Arslan, Hassan, Li, Tremayne), CC BY 4.0, https://doi.org/10.5281/zenodo.3609356
+- MASSIVE (FitzGerald et al., Amazon), CC BY 4.0
+- ABCD: Action-Based Conversations Dataset (Chen et al., ASAPP), MIT
+- SporeLabs templates: written by us for this project.

PARITY.md

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+# Parity
+
+Date 2026-10-06 14:54. CPU, fp32. The standalone `cue.py` was run on 500 dev rows (seed 7) against the training checkpoint; scores must match exactly.
+
+| head | max abs diff | gate (= 0) |
+|---|---|---|
+| commitment | 0.00e+00 | pass |
+| open_question | 0.00e+00 | pass |
+| delegate | 0.00e+00 | pass |
+
+## ONNX: cue-text
+
+Date 2026-10-06 14:54. `onnx_export.py` output (opset 17, dynamic batch and length) run with onnxruntime 1.30.0 on CPU, against the torch model, on AMI dev rows. Per head: up to 250 positives (AutoJev label >= 0.5) plus negatives, 500 rows, seed 7. AUROC is against the AutoJev labels; the gate is that int8 stays within 0.005 AUROC of torch.
+
+| head | n | pos | AUROC torch | AUROC fp32 onnx | AUROC int8 | int8 - torch | max abs diff fp32 | max abs diff int8 | gate |
+|---|---|---|---|---|---|---|---|---|---|
+| commitment | 500 | 152 | 0.9901 | 0.9901 | 0.9894 | -0.0008 | 1.0e-06 | 0.101 | pass |
+| open_question | 500 | 250 | 0.9835 | 0.9835 | 0.9828 | -0.0007 | 1.1e-06 | 0.200 | pass |
+| delegate | 500 | 29 | 0.9823 | 0.9823 | 0.9823 | +0.0000 | 8.0e-07 | 0.183 | pass |
+
+int8 moves single scores by up to about 0.15 (last-but-one column) and the ranking by under 0.005 AUROC. Thresholds were chosen on the torch model and apply to the int8 graph as well.
+
+Sizes: fp32 128.4 MB, int8 39.6 MB.

README.md

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+---
+license: apache-2.0
+base_model: jhu-clsp/ettin-encoder-32m
+language:
+  - en
+tags:
+  - text-classification
+  - on-device
+  - meetings
+---
+
+# cue-text
+
+The open model behind SporeLabs Conversation Cues (Cue). A 32M-parameter text model that reads one line of a meeting or call and scores three things: whether the speaker just **promised** to do something, asked the others a **real question**, or **handed work** to someone (a person, the team, or an assistant). It reads the line plus up to three earlier turns and runs on a laptop CPU or in a browser tab.
+
+| | |
+|---|---|
+| Base model | `jhu-clsp/ettin-encoder-32m` (MIT) |
+| Size | 32M parameters; 128 MB fp32, 40 MB int8 ONNX |
+| Outputs | `commitment`, `open_question`, `delegate` |
+| Language | English |
+| Licence | model weights Apache-2.0 (`LICENSE`); code MIT (`LICENSE-CODE`) |
+| Benchmark | [Cue benchmark v0.1](https://huggingface.co/datasets/SporeLabs/cue-benchmark) |
+
+## What each score means
+
+- **commitment**: the speaker is promising to do something themselves later, or promising for their team. "I'll email you once it's done", "leave that with me", "we'll get back to you next week". Not a commitment: suggestions, "maybe" and "might", asking others to do it, repeating what someone promised earlier, predictions, questions.
+- **open_question**: the speaker asks the others a real question and waits for an answer. "What did they quote?", "Anyone know if Thursday works?". Not a question in this sense: rhetorical questions, "right?" checks, "really?", "sorry, what?", talking to oneself, "all in favour?", and requests to do a task.
+- **delegate** (a hand-off): the speaker hands a piece of work to someone else to do later: "Priya, could you check the price?", "someone needs to write the reply", "make me a slide deck on churn", "remind me to call the bank". Not a hand-off: doing it themselves, quick in-the-moment asks ("pass the marker", "share your screen"), vague wishes, past assignments, asking for information.
+
+A score is a number from 0 to 1. `thresholds.json` holds, per head, a default cutoff fitted on human-labelled development data (200 lines labelled blind against these definitions, plus AMI development meetings); scores at or above it count as a hit. Move a cutoff to trade missed cues for false ones. Treat the scores as a ranking, not as probabilities.
+
+## Input
+
+One line to score, plus up to three earlier turns, most recent last. For each earlier turn, the model needs to know whether the same person spoke it (`same`) or someone else (`other`); if you do not know the speakers, leave the speaker out and it uses `?`. The model reads one string:
+
+```
+I'll send the deck by Friday. [SEP] other: Can someone send the deck to Halvorsen? [SEP] other: The quote is still open.
+```
+
+`cue.format_input` builds it. Text can be raw transcript text with punctuation or lowercase ASR output. Inputs are cut at 160 tokens.
+
+## Run it
+
+```python
+from huggingface_hub import snapshot_download
+model_dir = snapshot_download("SporeLabs/cue-text")
+```
+
+```
+pip install torch transformers safetensors
+python predict.py --context "A: can someone own the budget deck?" --speaker B "sure, I'll do it."
+```
+
+```python
+from cue import CueModel
+m = CueModel.from_pretrained(model_dir)
+scores = m.score("sure, I'll do it.", context=[{"speaker": "A", "text": "can someone own the budget deck?"}], speaker="B")
+hits = m.decide(scores)                    # uses thresholds.json
+```
+
+`cue.py` needs only torch, transformers and safetensors. For the browser or other runtimes, use `onnx/model_fp32.onnx` or `onnx/model_int8.onnx`: inputs `input_ids` and `attention_mask`, output `probs` with one column per head in the order above. The int8 file stays within 0.005 AUROC of the torch model (see [`PARITY.md`](PARITY.md)).
+
+## Intended use
+
+Flagging lines in transcripts of work meetings and calls for a person to look at: possible action items, questions that got no answer, requests to an assistant. It is meant as a first pass that a person or a larger model reviews, and it is small enough to run on the device that holds the audio.
+
+Out of scope: deciding anything about a person; use as evidence of what someone agreed to; languages other than English; text that is not conversation (documents, email bodies); any setting where a missed or false hit has serious consequences without review.
+
+## Evaluation
+
+Scored on the Cue benchmark's test splits with this package as released (fp32), scores at or above `thresholds.json` counting as hits. AUROC does not depend on the threshold; F1 is at the shipped threshold, with its 95% interval resampled by meeting. AMI's action-item labels cover both promises and work handed to someone, so `commitment_ami` counts a row as found when either `commitment` or `delegate` fires (score: the larger of the two).
+
+| head | benchmark task | AUROC | F1 (95% CI) | rows / positives |
+|---|---|---|---|---|
+| commitment or delegate | `commitment_ami` | 0.916 | 0.100 (0.063 to 0.138) | 33,612 / 118 |
+| open_question | `question_ami` | 0.904 | 0.525 (0.497 to 0.552) | 33,612 / 1,283 |
+| open_question | `question_icsi` | 0.962 | 0.602 (0.556 to 0.651) | 15,153 / 622 |
+
+On AMI questions, precision is 0.40 and recall 0.77. Raise a threshold if you need fewer false hits.
+
+AMI's gold counts only action items and questions linked to its meeting summaries, a narrower definition than the one this model was taught. The benchmark has no delegation task.
+
+## Training data
+
+The model trained on 32,850 sentences, each labelled by AutoJev-27B (Apache-2.0, revision `6f5b557e`):
+
+| source | rows | licence |
+|---|---|---|
+| AMI Meeting Corpus | 19,186 | CC BY 4.0 |
+| SporeLabs templates (our own Python templates for calls and requests) | 4,257 | ours, Apache-2.0 |
+| YouTube-Commons transcript sentences, 3,356 videos | 4,697 | CC BY 4.0, credit list below |
+| ABCD customer-service dialogues (ASAPP) | 2,702 | MIT |
+| ClaimBuster US presidential debate sentences | 1,096 | CC BY 4.0 |
+| MASSIVE (Amazon), 912 English utterances | 912 | CC BY 4.0 |
+
+Development rows, which pick the checkpoint and thresholds, are AMI dev meetings, held-out templates and 200 hand-labelled rows; they are never trained on.
+
+YouTube-Commons credit: transcripts of videos shared under CC BY, from the PleIAs/YouTube-Commons dataset. `YOUTUBE_ATTRIBUTIONS.jsonl` lists channel, channel id, video link, title, date and licence for each of the 3,356 videos. We used excerpts of automatic transcripts, cut into sentence-sized pieces. The weights contain no transcript text.
+
+Full attributions are in `NOTICE.md`.
+
+## Limits
+
+- **The labels are a model's, not people's.** Training labels come from AutoJev-27B, so the model learns its definition of a promise or a hand-off and repeats its mistakes. Rhetorical questions ("who would say no to that?") score as questions.
+- **English meetings and calls.** Most training text is AMI (product-design meetings) plus templates and short customer-service turns. Other domains, languages and long monologues are untested.

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+      "lstrip": false,
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+      "rstrip": false,
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+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 8192,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "[UNK]"
+}

config.json

@@ -0,0 +1,20 @@
+{
+ "architecture": "cue-multihead",
+ "kind": "cues",
+ "base_model": "jhu-clsp/ettin-encoder-32m",
+ "heads": [
+  "commitment",
+  "open_question",
+  "delegate"
+ ],
+ "product_heads": [
+  "commitment",
+  "open_question",
+  "delegate"
+ ],
+ "max_tokens": 160,
+ "n_ctx": 3,
+ "input_format": "target [SEP] rel: previous turn [SEP] ... (see cue.format_input)",
+ "backbone_dir": "backbone",
+ "heads_file": "heads.safetensors"
+}

cue.py

@@ -0,0 +1,129 @@
+"""Standalone loader for the SporeLabs Conversation Cues (Cue) text model.
+
+Depends only on torch, transformers and safetensors.
+
+    from cue import CueModel
+    m = CueModel.from_pretrained(".")            # the folder that holds config.json
+    m.score("I'll send the deck by Friday.",
+            context=[{"speaker": "A", "text": "Can someone own the budget deck?"}], speaker="B")
+    # {'commitment': 0.97, 'open_question': 0.01, 'delegate': 0.02, ...}
+
+Folder layout: config.json, thresholds.json, backbone/ (ModernBERT encoder with its pooling head, tokenizer files),
+heads.safetensors (one Linear(hidden, 1) per head). The forward pass is: encoder -> mean pool -> dense/GELU/LayerNorm
+head -> one logit per question -> sigmoid.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+from pathlib import Path
+
+import torch
+
+SEP = " [SEP] "
+
+
+def format_input(text: str, context: list[dict] | None = None, speaker: str | None = None, n_ctx: int = 3) -> str:
+    """One string: `<target> [SEP] <rel>: <previous turn> [SEP] <rel>: <turn before> ...`, most recent turn first.
+
+    <rel> is `same` (same speaker as the target), `other`, or `?` when the target speaker is unknown.
+    """
+    ctx = list(context or [])[-n_ctx:] if n_ctx > 0 else []
+    parts = [text or "."]
+    for c in reversed(ctx):
+        if speaker is None or c.get("speaker") is None:
+            rel = "?"
+        else:
+            rel = "same" if c.get("speaker") == speaker else "other"
+        parts.append(f"{rel}: {c.get('text', '')}")
+    return SEP.join(parts)
+
+
+def parse_turn(line: str) -> dict:
+    """'A: hello there' -> {'speaker': 'A', 'text': 'hello there'}; a line without 'X:' has no speaker."""
+    m = re.match(r"^\s*([^:\s][^:]{0,30}?)\s*:\s*(.*)$", line)
+    if m:
+        return {"speaker": m.group(1).strip(), "text": m.group(2).strip()}
+    return {"speaker": None, "text": line.strip()}
+
+
+class CueModel:
+    def __init__(self, backbone, tokenizer, heads: dict, config: dict, thresholds: dict, device: str):
+        self.backbone, self.tok, self.heads = backbone, tokenizer, heads
+        self.config, self.thresholds, self.device = config, thresholds, device
+        self.head_names = list(heads)
+
+    # ------------------------------------------------------------------ loading
+    @classmethod
+    def from_pretrained(cls, path_or_repo: str | os.PathLike, device: str = "cpu") -> "CueModel":
+        from safetensors.torch import load_file
+        from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+        root = Path(path_or_repo)
+        config = json.loads((root / "config.json").read_text())
+        tp = root / "thresholds.json"
+        thresholds = json.loads(tp.read_text()) if tp.exists() else {}
+        bb_dir = root / config.get("backbone_dir", "backbone")
+        backbone = AutoModelForSequenceClassification.from_pretrained(bb_dir)  # ModernBertForSequenceClassification
+        backbone.to(device).eval()
+        tok = AutoTokenizer.from_pretrained(bb_dir)
+        sd = load_file(str(root / config.get("heads_file", "heads.safetensors")))
+        d = backbone.config.hidden_size
+        heads = {}
+        for q in config["heads"]:
+            lin = torch.nn.Linear(d, 1)
+            lin.load_state_dict({"weight": sd[f"{q}.weight"], "bias": sd[f"{q}.bias"]})
+            heads[q] = lin.to(device).eval()
+        return cls(backbone, tok, heads, config, thresholds, device)
+
+    # ------------------------------------------------------------------ forward
+    @torch.no_grad()
+    def _logits(self, input_ids, attention_mask) -> dict[str, torch.Tensor]:
+        b = self.backbone
+        h = b.model(input_ids=input_ids, attention_mask=attention_mask)[0]
+        if b.config.classifier_pooling == "cls":
+            h = h[:, 0]
+        else:
+            m = attention_mask.unsqueeze(-1).to(h.dtype)
+            h = (h * m).sum(1) / m.sum(1)
+        v = b.drop(b.head(h))
+        return {q: head(v)[:, 0] for q, head in self.heads.items()}
+
+    def probs_from_strings(self, texts: list[str], batch: int = 64) -> dict[str, list[float]]:
+        """Sigmoid scores per head for already formatted input strings."""
+        max_len = int(self.config.get("max_tokens", 160))
+        out = {q: [0.0] * len(texts) for q in self.head_names}
+        order = sorted(range(len(texts)), key=lambda i: len(texts[i]))
+        for i in range(0, len(order), batch):
+            ix = order[i:i + batch]
+            enc = self.tok([texts[j] or "." for j in ix], padding=True, truncation=True, max_length=max_len,
+                           return_tensors="pt").to(self.device)
+            for q, z in self._logits(enc["input_ids"], enc["attention_mask"]).items():
+                for j, v in zip(ix, torch.sigmoid(z.float()).cpu().tolist()):
+                    out[q][j] = 0.0 if not texts[j] else float(v)
+        return out
+
+    def score_many(self, rows: list[dict], batch: int = 64) -> list[dict[str, float]]:
+        """rows: [{text, context?: [{speaker, text}], speaker?}] -> one {head: probability} dict per row."""
+        n_ctx = int(self.config.get("n_ctx", 3))
+        xs = [format_input(r["text"], r.get("context"), r.get("speaker"), n_ctx) for r in rows]
+        p = self.probs_from_strings(xs, batch)
+        return [{q: p[q][i] for q in self.head_names} for i in range(len(rows))]
+
+    def score(self, text: str, context: list[dict] | None = None, speaker: str | None = None) -> dict[str, float]:
+        return self.score_many([{"text": text, "context": context, "speaker": speaker}])[0]
+
+    # ------------------------------------------------------------------ decisions
+    def threshold(self, head: str) -> float | None:
+        t = (self.thresholds.get("heads") or {}).get(head) or {}
+        return t.get("threshold")
+
+    def decide(self, scores: dict[str, float]) -> dict[str, bool | None]:
+        """Fired / not fired at the dev thresholds (None when a head has no threshold yet)."""
+        return {q: (None if self.threshold(q) is None else s >= self.threshold(q)) for q, s in scores.items()}
+
+    @property
+    def product_heads(self) -> list[str]:
+        return list(self.config.get("product_heads") or self.head_names)

heads.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54b7e3e3b4d617c894e007bb883fc495a6e37e77b0b36a5157ca4c52e847a2e7
+size 5084

onnx/model_fp32.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0efd374e65e62e0a0e424440a9445985699109b46a61f1b4c1fb67438734f343
+size 128351810

onnx/model_int8.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a2571a23b1774085c0b5def2a2c93816e776c96867c948295b31278aff536da
+size 39621115

onnx_export.py

@@ -0,0 +1,77 @@
+"""Export a package folder (config.json, backbone/, heads.safetensors) to ONNX for the in-browser demo.
+
+  python onnx_export.py DIR            # writes DIR/onnx/model_fp32.onnx and DIR/onnx/model_int8.onnx
+
+The graph takes input_ids and attention_mask (int64, dynamic batch and sequence length) and returns `probs`
+[batch, n_heads], the sigmoid of each head's logit, in the order of config.json "heads". The int8 file is dynamic
+quantisation of the weights with per-channel scales: the embedding table (19.3 of the 32 million parameters) and the
+attention and MLP up-projection matrices. The MLP down-projection stays fp32: quantising it cost up to 0.02 AUROC
+on the delegate head, the other layers cost under 0.001 (see PARITY.md).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import torch
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from cue import CueModel  # noqa: E402
+
+
+class ProbWrapper(torch.nn.Module):
+    def __init__(self, m: CueModel):
+        super().__init__()
+        self.backbone = m.backbone
+        self.w = torch.nn.Parameter(torch.cat([m.heads[q].weight for q in m.head_names], 0).detach().clone(), requires_grad=False)
+        self.b = torch.nn.Parameter(torch.cat([m.heads[q].bias for q in m.head_names], 0).detach().clone(), requires_grad=False)
+
+    def forward(self, input_ids, attention_mask):
+        b = self.backbone
+        h = b.model(input_ids=input_ids, attention_mask=attention_mask)[0]
+        mk = attention_mask.unsqueeze(-1).to(h.dtype)
+        h = (h * mk).sum(1) / mk.sum(1)          # mean pooling (config classifier_pooling = "mean")
+        v = b.head(h)
+        return torch.sigmoid(v @ self.w.T + self.b)
+
+
+def export(folder: Path, opset: int = 17):
+    from transformers import AutoModelForSequenceClassification
+    from onnxruntime.quantization import QuantType, quantize_dynamic
+
+    folder = Path(folder)
+    cfg = json.loads((folder / "config.json").read_text())
+    m = CueModel.from_pretrained(folder)
+    if m.backbone.config.classifier_pooling != "mean":
+        sys.exit("only mean pooling is exported")
+    # eager attention exports cleanly; the weights are identical
+    m.backbone = AutoModelForSequenceClassification.from_pretrained(folder / cfg["backbone_dir"], attn_implementation="eager").eval()
+    w = ProbWrapper(m).eval()
+    out = folder / "onnx"
+    out.mkdir(exist_ok=True)
+    ids = torch.randint(5, 1000, (2, 24))
+    mask = torch.ones_like(ids)
+    mask[1, 18:] = 0
+    fp32 = out / "model_fp32.onnx"
+    torch.onnx.export(w, (ids, mask), str(fp32), input_names=["input_ids", "attention_mask"], output_names=["probs"],
+                      dynamic_axes={"input_ids": {0: "batch", 1: "seq"}, "attention_mask": {0: "batch", 1: "seq"},
+                                    "probs": {0: "batch"}}, opset_version=opset, dynamo=False)
+    int8 = out / "model_int8.onnx"
+    import onnx
+    names = [n.name for n in onnx.load(str(fp32)).graph.node if n.op_type == "MatMul"]
+    keep = ("/attn/Wqkv/", "/attn/Wo/", "/mlp/Wi/")   # int8; the MLP down-projection, head and rotary stay fp32
+    quantize_dynamic(str(fp32), str(int8), weight_type=QuantType.QInt8, per_channel=True,
+                     nodes_to_exclude=[n for n in names if not any(k in n for k in keep)])
+    for p in (fp32, int8):
+        print(f"{p.name}: {p.stat().st_size / 1e6:.1f} MB")
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("dir")
+    ap.add_argument("--opset", type=int, default=17)
+    a = ap.parse_args()
+    export(Path(a.dir), a.opset)

predict.py

@@ -0,0 +1,47 @@
+"""Score a line of conversation. Needs torch, transformers, safetensors.
+
+  python predict.py "I'll send the deck by Friday."
+  python predict.py --context "A: can someone own the budget deck?" --speaker B "sure, I'll do it."
+  echo '{"text": "...", "context": [{"speaker": "A", "text": "..."}], "speaker": "B"}' | python predict.py --jsonl
+
+--context is repeatable, oldest turn first; only the last 3 are used. --model is the model folder
+(default: the folder this file is in).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+from cue import CueModel, parse_turn
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--model", default=str(Path(__file__).resolve().parent))
+    ap.add_argument("--context", action="append", default=[], help='"SPEAKER: text", oldest first; repeatable')
+    ap.add_argument("--speaker", default=None, help="speaker of the target line (needed to tell same/other speaker)")
+    ap.add_argument("--jsonl", action="store_true", help="read {text, context?, speaker?} rows from stdin")
+    ap.add_argument("--device", default="cpu")
+    ap.add_argument("text", nargs="*")
+    a = ap.parse_args()
+
+    m = CueModel.from_pretrained(a.model, device=a.device)
+    if a.jsonl:
+        rows = [json.loads(line) for line in sys.stdin if line.strip()]
+    else:
+        ctx = [parse_turn(c) for c in a.context]
+        rows = [{"text": t, "context": ctx, "speaker": a.speaker} for t in a.text]
+    if not rows:
+        ap.error("give a text argument or --jsonl")
+    heads = m.product_heads
+    for r, s in zip(rows, m.score_many(rows)):
+        fired = m.decide(s)
+        print(json.dumps({"text": r["text"], **{h: round(s[h], 4) for h in heads},
+                          "fired": [h for h in heads if fired.get(h)]}))
+
+
+if __name__ == "__main__":
+    main()

thresholds.json

@@ -0,0 +1,58 @@
+{
+ "rule": "mean F1 over human200 and ami_dev on a 0.01 grid; moved only when the bootstrap 95% interval of the gain over the previous threshold is above zero; fixed before re-scoring any test set",
+ "heads": {
+  "commitment": {
+   "threshold": 0.54307,
+   "previous": 0.54307,
+   "dev_set": "human200 (Coleman, blind, product definitions) + ami_dev (AMI human annotations)",
+   "gain_mean_f1": 0.017,
+   "gain_ci95": [
+    -0.0108,
+    0.0507
+   ],
+   "f1_human200": 0.8254,
+   "f1_ami_dev": 0.5816,
+   "n_human200": 200,
+   "pos_human200": 33,
+   "n_ami_dev": 5921,
+   "pos_ami_dev": 274,
+   "moved": false
+  },
+  "open_question": {
+   "threshold": 0.69,
+   "previous": 0.53315,
+   "dev_set": "human200 (Coleman, blind, product definitions) + ami_dev (AMI human annotations)",
+   "gain_mean_f1": 0.0186,
+   "gain_ci95": [
+    0.0067,
+    0.0274
+   ],
+   "f1_human200": 0.9189,
+   "f1_ami_dev": 0.5628,
+   "n_human200": 200,
+   "pos_human200": 58,
+   "n_ami_dev": 18987,
+   "pos_ami_dev": 777,
+   "moved": true
+  },
+  "delegate": {
+   "threshold": 0.51,
+   "previous": 0.31651,
+   "dev_set": "human200 (Coleman, blind, product definitions) + ami_dev (AMI human annotations)",
+   "gain_mean_f1": 0.0624,
+   "gain_ci95": [
+    0.0096,
+    0.1131
+   ],
+   "f1_human200": 0.9,
+   "f1_ami_dev": 0.3333,
+   "n_human200": 200,
+   "pos_human200": 31,
+   "n_ami_dev": 18952,
+   "pos_ami_dev": 22,
+   "moved": true
+  }
+ },
+ "fitted": "2026-10-07",
+ "previous_rule": "max-F1 on dev; thresholds are fixed before any test run"
+}