---
license: mit
language:
  - en
library_name: pytorch
tags:
  - text-classification
  - negotiation
  - deal-loss-prediction
  - explainable-ai
  - film
  - roberta
datasets:
  - stanfordnlp/craigslist_bargains
metrics:
  - roc_auc
pipeline_tag: text-classification
---

# Veridian.ai — Lost Quote Intelligence System (LQIS)

Real-time, **explainable deal-loss prediction** for buyer–seller negotiations, with rep-level
coaching. Given a running dialogue, the model outputs a **loss probability** (updated turn-by-turn),
the **turn at which the deal tipped**, the **named signals** that drove the score, and rule-based
**coaching**.

> ⚠️ **Methodology demonstration, not a production B2B model.** Trained on consumer (C2C) Craigslist
> price-haggling; treat numbers as a demonstration of the method, not certified B2B performance.

## Architecture

A hierarchical, conditionally-modulated pipeline:

```
each turn ──► RoBERTa (frozen, fine-tuned) ──► CLS [768]
                                                  │  sequence
            Temporal Transformer (2L, 8 heads) ◄──┘ ──► mean-pool ──► e_conv [768]
                                                                          │
LIWC features (90-d) ──► Conditioning MLP ──► γ, β [768] ──► e_fused = γ ⊙ e_conv + β
                                                                          │
                                              σ(Linear(768→1)) ──► LOSS PROBABILITY
                                              + SHAP seams (token · turn · feature) ──► coaching
```

- **Fusion = FiLM** (Feature-wise Linear Modulation): low-dimensional linguistic/market signals
  *modulate* the conversation representation rather than competing with it as tokens.
- **External market line is identity / demo-only** (`external_mode="identity"`) — fetched & shown
  live but **not used in the trained score**, to avoid label leakage. (No learnable external params.)
- **Calibration:** post-hoc Platt scaling (monotonic → AUC preserved) so the loss gauge is usable.

## Results (held-out test, n = 703)

| Model | Test AUC-ROC |
|---|---|
| Flat RoBERTa (turn encoder) | 0.804 |
| Temporal transformer (val) | 0.939 |
| **Full FiLM pipeline** | **0.899** |

F1 0.635 · Precision 0.753 · Recall 0.549 · Accuracy 0.88. Ablation flat→full = **+0.095 AUC**
(genuine temporal + LIWC lift; external demo-only). Live, real-dialogue discrimination AUC ≈ 0.85.

## Files

```
models/roberta_turn_encoder/   # HF-native frozen fine-tuned encoder (AutoModel)
models/temporal_transformer.pt # 2-layer temporal transformer state_dict
models/film_head.pt            # FiLM conditioning + classifier state_dict
models/roberta_aux_head.pt     # aux head for token-SHAP (approximate)
models/liwc_scaler.joblib      # MinMaxScaler (train-fit) for LIWC features
models/calibration.json        # Platt {a,b}
models/metrics.json            # measured metrics
src/                           # modeling code to reconstruct the pipeline
inference.py                   # runnable example
```

## Usage

```bash
pip install -r requirements.txt
python inference.py
```

```python
from src.inference.live_scorer import LiveScorer
scorer = LiveScorer.build()
out = scorer.score([
    {"speaker": "buyer",  "text": "Is the charger still available?"},
    {"speaker": "seller", "text": "Yes, asking $10."},
    {"speaker": "buyer",  "text": "That is too expensive, $4 is my max."},
    {"speaker": "seller", "text": "I could maybe do $8."},
    {"speaker": "buyer",  "text": "No. $4 or I am done."},
], fetch_external=False)
print(out["loss_probability"], out["tipping_turn"], out["coaching"])
```

## Intended use & limitations

- **Use:** demonstrate real-time, explainable loss scoring + coaching on short price-negotiation
  dialogues; research / educational.
- **Out of scope:** generic / non-negotiation chat is out-of-distribution and will read near 0%.
  Not validated for production B2B decisioning. Probabilities are calibrated on C2C data; expect
  recalibration on real B2B funnels.
- **Explainability ≠ accuracy:** the SHAP seams explain a prediction; they don't change it.

## Training data & attribution

Fine-tuned on the **Craigslist Bargaining** corpus (He et al., 2018). This repo redistributes only
**derived model weights**, not the dataset. Please cite:

```bibtex
@inproceedings{he2018decoupling,
  title={Decoupling Strategy and Generation in Negotiation Dialogues},
  author={He, He and Chen, Derek and Balakrishnan, Anusha and Liang, Percy},
  booktitle={EMNLP}, year={2018}
}
```

`roberta-base` (MIT) · `empath` (MIT). License of this repo: **MIT** (see `LICENSE`).